THE
STING (1973)
IVV
Annoyed by Loud Chewing? The Problem Is You
La separación de la madre en la infancia provoca alteraciones en la
microbiota (microorganismos) intestinal del bebé que pueden causar el
desarrollo de trastornos del comportamiento que persisten hasta la edad adulta,
según un estudio realizado en roedores que publica la revista Nature
Communications.
Los episodios traumáticos durante la niñez están asociados con un mayor
riesgo de desarrollar enfermedades psiquiátricas, metabólicas e intestinales en
la edad adulta, aunque los mecanismos por los que se produce este fenómeno en
patologías tan diversas se desconocen, según el español Consejo Superior de
Investigaciones Científicas (CSIC).
Yolanda Sanz, del Instituto de Agroquímica y Tecnología de Alimentos
del CSIC, detalló que el estrés prolongado provocado por la separación de la
madre en roedores recién nacidos provoca una disfunción en el eje
hipotalámico-hipofisario-adrenal, uno de los principales sistemas de control
neuroendocrino del organismo.
"Esto, a su vez, ocasiona alteraciones en diversas funciones
fisiológicas afectando, entre otros, al sistema nervioso central y a las
emociones", dijo.
Según la científica, en este trabajo se ha demostrado que la separación
de la madre en la infancia provoca alteraciones en la composición y funciones
de la microbiota intestinal relacionadas con la síntesis de neurotransmisores.
Estas alteraciones, a su vez, son responsables del desarrollo de
trastornos del comportamiento como la ansiedad, lo que podría aumentar el
riesgo de desarrollar enfermedades psiquiátricas como la depresión en la edad
adulta.
En este estudio se emplearon ratones libres de gérmenes y ratones
convencionales para poder establecer una relación causal entre el estrés, los
trastornos del comportamiento y la microbiota intestinal.
Así, se constató que mientras algunas de las alteraciones
neuroendocrinas producidas por el estrés crónico son independientes de la
presencia de microbiota, esta es esencial para el desarrollo de alteraciones
del comportamiento, actuando como factor causal de la ansiedad.
Los resultados del trabajo, liderado por la Universidad McMaster de
Canadá, podrían aplicarse en un futuro para mejorar el estado de salud mental y
reducir el riesgo de desarrollar patologías psiquiátricas mediante la
modulación de la microbiota intestinal a través de la dieta, por ejemplo a
través de la administración de bacterias beneficiosas conocidas como
probióticos, según el CSIC.
7. Testing: Exposure to a test can affect
scores on subsequent exposures to that test, which can be confused with a treatment
effect.
9. Additive and Interactive Effects
of Threats to Internal Validity: The impact of a threat can be added
to that of another threat or may depend on the level of another threat.
2. Violated Assumptions of Statistical Tests: can lead to
overestimating or underestimating the size and significance of an effect.
7. Testing: Exposure to a test can affect
scores on subsequent exposures to that test, which can be confused with a treatment
effect.
9. Additive and Interactive Effects
of Threats to Internal Validity: The impact of a threat can be added
to that of another threat or may depend on the level of another threat.
Statistical
Conclusion Validity
refers to the appropriate use of statistics to
infer whether the presumed independent and dependent variables co-vary.
2. Violated Assumptions of Statistical Tests: can lead to
overestimating or underestimating the size and significance of an effect.
5. Context Dependent Mediation: An exp
Statistical Concepts:
Variability : (Variable )Something
that the researcher/experimenter can
measure.
Statistics (what are they and what are the different types)
Statistical Concepts: A measure of central tendency is a single value that
attempts to describe a set of data by identifying the central position within
that set of data. As such, measures of central tendency are sometimes called
measures of central location. They are also classed as summary statistics. The
mean (often called the average) is most likely the measure of central tendency
that you are most familiar with, but there are others, such as the median and
the mode.
Rectangular Distribution : The scores in a rectangular distribution
are all about equally frequent or probable. An example of a rectangular
distribution is the theoretical distribution representing the six possible
scores that can be obtained by rolling a single six-sided die.
Bimodal Distribution: In the case of a bimodal distribution, two
distinct ranges of scores are more common than any other. A likely example of a
bimodal distribution would be the heights of the athletes attending the annual
sports banquet for a very large high school that has only two sports teams:
women’s gymnastics and men’s basketball. If this example seems a little
contrived, it should. Bimodal distributions are relatively rare, and they
usually reflect the fact that a sample is composed of two meaningful
subsamples.
Normal Distribution: The nice thing about the normal distribution
is that if you know that a set of observations is normally distributed, this
further improves your ability to describe the entire set of scores in the
sample.
Hypothesis vs. Theory: So what is the Relationship between theory
and data you might ask? Well the first relationship is
Deduction
Hypothesis: Prediction about specific events that is
derived from the theory.
Different types of hypotheses
Probability Theory: Probability Theory: From the classical
perspective, the probability of an event is a very simple thing: It is (a) the
number of all specific outcomes that qualify as the event in question divided
by (b) the total number of all possible outc
Reliability
(different types) Reliability:
refers to the consistency of a measure. Validity: is the extent to which
a test measures what it claims to measure. There is Three main features that
need to be included to measure are:
Control: refers
to how well the experimenter controlled the experiment. The control is
important because without control, researchers cannot establish cause and
effect.
Validity (different types)
Internal And External Objectivity can be
achieved form a thorough review of the literature and the development of a
theoretical framework. The literature
review should be presented so that the reader can judge the objectivity of the
research questions. Purpose of Research Design Provides the plan or blueprint
for testing research questions and hypotheses. Involves structure and strategy
to maintain control and intervention fidelity.
Independent
Variable: The variable the experimenter has control over, can change in
some way to see if it has an effect on other variables in the study.
Dependent Variable: The
variable that is measured to see if a place: change takes.
Theory: Definition of a theory: A set of
logically consistent statements about some psychological phenomenon that best
summarizes existing empirical knowledge of the phenomenon organizes this
knowledge in the form of precise statements of the relationship among variables
provides a tentative explanation for the phenomenon serves as a basis for
making predictions about behavior. So we have our method, we have our
assumptions, so how do we actually do research? Relationship between theory and
data
Hypothesis: Prediction about specific
events that is derived from the theory. Induction: Logical process of reasoning
from specific events to the theory (either confirming or disproving the
theory).
Generalisability: The aim of
psychological research is to produce results which can then be generalised
beyond the setting of the experiment. If an experiment is lacking in realism we
will be unable to generalise. However, even if an experiment is high in
realism, we still may not be able to generalise.
Control Variable: The
variable that is not manipulated that serves as a comparison group from the
other variables in the study. This third variable is used to ensure that the
independent variable, when manipulated is actually having an effect on the
dependent variable. For example, if a similar change occurs in the control
variable as the dependent variable, this indicates that the change may not be
the result of the independent variable manipulation and may be a natural change
in the variable. In a experiment the researcher manipulates the independent
variable to see if it has an effect on the dependent variable.
Sample Variance: The sample
variance equals the mean squared deviation from
. A small means that the
observed values cluster around the average, while a large variance means that
they are more spread out. Thus, the variance is a measure of the “spread” in
the sampled values.
Selection Threat --
Differences in groups that exist before treatment or intervention begins.
Especially problematic when participants selected for belonging to a specific
group. Differences may be due to initial differences in groups – not treatment
or intervention.
Method: A technique used to analyze data.
Commonly, a method is aligned with a particular strategy for gathering data, as
particular methods commonly require particular types of data. “Method” is
therefore commonly used to refer to strategies for both analyzing and gathering
data.
Methodology: A body of practices,
procedures, and rules used by researchers to offer insight into the workings of
the world.
Insight: Evidence contributing to an
understanding of a case or set of cases. Comparative-historical researchers are
generally most concerned with causal insight, or insight into causal processes.
Treatment
Diffusion
- This occurs when a comparison group learns about the program either directly
or indirectly from program group participants. In a school context, children
from different groups within the same school might share experiences during
lunch hour. Or, comparison group students, seeing what the program group is
getting, might set up their own experience to try to imitate that of the
program group. In either case, if the diffusion of imitation affects the
posttest performance of the comparison group, it can have an jeopardize your
ability to assess whether your program is causing the outcome.
Threats to
internal validity
Ambiguous Temporal Precedence: Ambiguous
Directionality --
When the independent variable is not manipulated, the direction of the
influence is not always clear (e.g., impact of a therapist empathy on client
outcome – does therapist get warmer b/c client improves or vice versa?)
Selection: -- Differences
in groups that exist before treatment or intervention begins. Especially
problematic when participants selected for belonging to a specific group.
Differences may be due to initial differences in groups – not treatment or
intervention.
History: History Threat -- Events
outside experimental manipulation influence collective or individual
participation “Many change-producing events may have occurred between
Observation 1 and Observation 2 “ (Source: Internet Presentation) “History is more …[likely] the longer the
lapse between the pretest and posttest.” (Source: Alliant
Maturation: Maturation
Threat - Normal developmental changes in participants between pre and post
tests. Gains/Losses over time may be due to normal maturation.
Regression: Regression
Threat --
The tendency to drift towards the mean as one takes a test multiple times.
Especially problematic when choosing “extreme” scores which can “regress”
toward the mean due to influences other than treatment. “Occurs when individuals are
selected for an intervention or treatment on the basis of extreme scores on
a pretest. Extreme scores are more likely to reflect larger (positive or
negative) errors in measurement (chance factors). Such extreme measurement
errors are NOT likely to occur on a second testing.” (Source: Internet
Presentation)
Attrition: Attrition
Threat – In a longitudinal study where you need to measure responses from a
participant at multiple time points, attrition refers to when participants drop
out or leave a study, causes problems as it reduces available data, in
particular when it is different across groups.
In a longitudinal
study where you need to measure responses from a participant at multiple time
points, attrition refers to when participants drop out or leave a study, causes
problems as it reduces available data, in particular when it is different
across groups. When participants drop
out or leave a study, causes problems as it reduces available data, in
particular when it is different across groups.
Instrumentation:
Instrumentation threat: How do we fix it?
Additive and
Interactive Effects: This is when two internal validity threats are
present, and at least one of the threats’ effects is dependent on the other
threat or is greater because of the other threat.
External Validity:This is when the results found are not
generalizable to the population of interest.
Selection
(Interaction of Causal Units): This is a threat when the participants
used in a study are of a specific type such that the results found with them might
not apply (be generalizable) to other participants, but researchers imply this.
Interaction of the causal relationship
over treatment variations: :
This is a problem when the research study has a TREATMENT, but a weird
variation of the treatment is used, and results found may not be generalizable
to other variations of that treatment.
a. Example: Researchers test a manualized CBT
treatment. This CBT treatment is meant to be 12 weeks, but researchers did a 7
week version of it, so the results may be different. Effects found with this
treatment are not necessarily the same at results that would have been found
with the treatment they were trying to test (the 12 week treatment). OR
Researchers combined CBT with another treatment, which may find different
results. OR They only used part of the treatment, such as the Cognitive portion
of the treatment.
Interaction of the causal relationship
with outcomes: This
is similar to the idea of mono operation bias, but just for the DV. This is a threat when the outcome (usually
just one Dependent Variable) was measured a certain way (usually with only one
measure). It is a problem when measuring the outcome in a different way could
have given different results.
Interaction of causal relationship
w/settings: This
is a threat when the research was done in a particular setting (environment),
and results may not be generalizable to a different setting. By setting we are
talking about specific research setting such as laboratory, school, home,
internet, ect. NOT geographic location like Texas or New York. Geographic
locations are more of a selection threat because we are talking about different
types of people with results that may not generalize to other types of people.
Here we are talking about a setting where results may not generalize to other
settings.
Context-dependent Mediation: This is a
threat when a mediating variable may not mediate the same way in a different
situation (different setting, or participants, or treatment, or task, ect).
This is only when the study has a mediating variable, and this relationship
(the variable mediating the relationship between two other variables) may not
be the same in a different situation. Example: Suppose a study used only
males to study depression. They found that owning more video games makes them
super excited and causes sleeplessness, which later causes depression in these
people. So, sleeplessness mediates the relationship between video games and
depression. However, this may not be the case if females were used. Owning video
games may bore them, which may make them depressed, but it doesn’t cause
sleeplessness because they aren’t excited about the games. So the video games
cause depression for them, but this isn’t mediated by sleeplessness. Or if different video games were used, then
sleeplessness may not mediate the relationship the same way.
Interaction of History and Treatment: This is
different than the Internal Validity threat History. That is when an event
affects participants and causes the results found. THIS one is when results
found from a study are not generalizable to other time periods. This usually
has to do with whole eras. It mostly applies to old studies, when times were
different, and results found may not be the same today.
Construct Validity Threats This is when researchers did not actually
measure the construct they intended to measure.
Inadequate
explication of constructs: This is when researchers do not get at a
construct all the way. This could be when researchers only measure part of a
construct. Or when they measure the wrong construct. Or when they use a measure
with low reliability or validity. Or when they use a measure that is too broad
to get at the measure they want. Or when they only use part of a measure but
don’t check the reliability and validity of this portion of the measure.
Construct confounding: There is a
construct that the researchers have not accounted for that is responsible for
causing or affecting the results and relationship found. There is a construct
here that is unaccounted for (missing). The must some evidence for this, good
reason to believe this is a problem.
Mono-operation bias: This is a
problem when only one instrument (measure) is used to measure ONE construct.
For any particular construct in the study, if there was only one thing
measuring it, this is a problem. It may occur multiple times in a study, but
only talk about one of those times when asked to describe a threat.
Mono-method bias: This is the
same thing except replace “instrument” or “measure” with “method”. Researchers
could use more than one measure for any ONE construct, but if that ONE
construct only has ONE method used to measure it, you may not be getting at the
construct as well as you could.
Confounding constructs with levels: Mono-method
bias: This is the same thing except replace “instrument” or “measure” with
“method”. Researchers could use more than one measure for any ONE construct,
but if that ONE construct only has ONE method used to measure it, you may not
be getting at the construct as well as you could.
*** The following Construct Validity threats are somewhat specific and
Medium to Low risk. Look in Methods to find these***
Reactive Self-report changes: This is when
people change the way they are answering in a self-report because they want to
be accepted to the study of receive treatment, or because they want to appear
socially acceptable.
Reactivity to the experimental situation:
This
is when something in the experimental situation is affecting the way the
participants respond. This could include the Hawthorne effect- a reaction to
something in the environment. It could also include hypothesis guessing done by
the participants, or the placebo effect.
Experimental
Expectancies: This is also called the Rosenthal effect. It is when the experimenter conveys the
message of what is wanted from the participant and that leads PT to act in a
certain way. Instead of actually measuring the construct naturally in the
participant, you are just picking up on how the participant is trying to meet
expectations
Novelty & Disruption Effects: This is when
participants may respond better to things that are new. So instead of just
measuring the construct as it is meant to be, the fact that the treatment or
measure is new to them may skew things. OR they may respond particularly poorly
or poorly to something that disrupts their routine.
Compensatory Equalization: This is when
treatment provides something very desirable (researchers are testing a Free
Ice-Cream treatment), and researchers attempt to compensate by giving things to
the non-treatment group (vouchers they can use for ice cream after the study).
Doing this must then be included as part of the treatment construct
description.
Compensatory Rivalry: This is when
(competitive) participants who are not receiving treatment may be motivated to
show they can do as well as those receiving treatment. This rivalry motivation
could change what you are measuring, and must then be included as part of the
treatment construct description.
2. Interaction of Setting and Treatment - Results in one
setting may not work in another. Recognition programs at hospital for doctors
wont work for mcdonalds slave workers
THE
STING (1973)
THE
EMPIRE STRIKES BACK (1980)
DEAD
POETS SOCIETY [1989
IVV
IVW
IVE
IWR
IWP
IWS
IWM
IWO
IWN
VNQ
VIG
EFA
ILF
WEE
SHY
DIA
QQQ
SPY
VTI
LQD
SHY
TLT
IEF
EWW
IWB
IWM
IWV
IVV
ILF
RWR
http://www.scientificcomputing.com/news/2016/01/human-machine-superintelligence-can-solve-worlds-most-dire-problems
http://qz.com/538482/america-is-an-incredibly-freudian-culture-that-doesnt-believe-in-freud-anymore/
Annoyed by Loud Chewing? The Problem Is You
La separación de la madre en la infancia provoca alteraciones en la
microbiota (microorganismos) intestinal del bebé que pueden causar el
desarrollo de trastornos del comportamiento que persisten hasta la edad adulta,
según un estudio realizado en roedores que publica la revista Nature
Communications.
Los episodios traumáticos durante la niñez están asociados con un mayor
riesgo de desarrollar enfermedades psiquiátricas, metabólicas e intestinales en
la edad adulta, aunque los mecanismos por los que se produce este fenómeno en
patologías tan diversas se desconocen, según el español Consejo Superior de
Investigaciones Científicas (CSIC).
Yolanda Sanz, del Instituto de Agroquímica y Tecnología de Alimentos
del CSIC, detalló que el estrés prolongado provocado por la separación de la
madre en roedores recién nacidos provoca una disfunción en el eje
hipotalámico-hipofisario-adrenal, uno de los principales sistemas de control
neuroendocrino del organismo.
"Esto, a su vez, ocasiona alteraciones en diversas funciones
fisiológicas afectando, entre otros, al sistema nervioso central y a las
emociones", dijo.
Según la científica, en este trabajo se ha demostrado que la separación
de la madre en la infancia provoca alteraciones en la composición y funciones
de la microbiota intestinal relacionadas con la síntesis de neurotransmisores.
Estas alteraciones, a su vez, son responsables del desarrollo de
trastornos del comportamiento como la ansiedad, lo que podría aumentar el
riesgo de desarrollar enfermedades psiquiátricas como la depresión en la edad
adulta.
En este estudio se emplearon ratones libres de gérmenes y ratones
convencionales para poder establecer una relación causal entre el estrés, los
trastornos del comportamiento y la microbiota intestinal.
Así, se constató que mientras algunas de las alteraciones
neuroendocrinas producidas por el estrés crónico son independientes de la
presencia de microbiota, esta es esencial para el desarrollo de alteraciones
del comportamiento, actuando como factor causal de la ansiedad.
Los resultados del trabajo, liderado por la Universidad McMaster de
Canadá, podrían aplicarse en un futuro para mejorar el estado de salud mental y
reducir el riesgo de desarrollar patologías psiquiátricas mediante la
modulación de la microbiota intestinal a través de la dieta, por ejemplo a
través de la administración de bacterias beneficiosas conocidas como
probióticos, según el CSIC.
Threats to Validity
1. Ambiguous Temporal Precedence: Lack of
clarity about which variable occurred first.
2. Selection: sometimes at the start of an experiment, the
average person receiving one experimental condition already differs from the
average person receiving another condition. This difference might account for
any result
3. History: Events occurring concurrently with
treatment could cause the observed effect.
4. Maturation: Naturally occurring changes over time could be
confused with a treatment effect.
5. Regression: when units are selected for their
extreme scores, they will often have less extreme scores on other variables,
which can be confused with a treatment effect.
6. Attrition: Loss of respondents to treatment or to measurement
can produce artificial effects if that loss is systematically correlated with
conditions.
7. Testing: Exposure to a test can affect
scores on subsequent exposures to that test, which can be confused with a treatment
effect.
8. Instrumentation: The nature of a measure
may change over time or conditions in a way that can be confused with a
treatment effect. For example, a spring might become weaker over time and easier
to push, artificially increasing reaction times.
9. Additive and Interactive Effects
of Threats to Internal Validity: The impact of a threat can be added
to that of another threat or may depend on the level of another threat.
Statistical
Conclusion Validity: refers to the appropriate use of statistics to
infer whether the presumed independent and dependent variables co-vary.
1. Low Statistical Power: An insufficiently
powered experiment may incorrectly conclude that the relationship between
treatment and outcome is not significant
2. Violated Assumptions of Statistical Tests: can lead to
overestimating or underestimating the size and significance of an effect.
3. Fishing and the Error Rate Problem: Repeated
tests for significant relationships, if uncorrected for the number of tests,
can artificially inflate statistical significance.
4. Unreliability
of Measures: weakens the
relationship between two variables and strengthens or weakens the relationships
among three or more variables.
5. Restriction of Range: Reduced range on a variable
usually weakens the relationship between it and another variable.
6. Unreliability of Treatment
Implementation: If a treatment that is intended to be implemented
in a standardized manner is implemented only partially for some respondents,
effects may be underestimated compared with full implementation.
7. Extraneous Variance in the Experimental Setting: Some
features of an experimental setting may inflate error making detection of an
effect more difficult.
8. Heterogeneity of Units: Increased variability on the
outcome variable within conditions increases error variance, making detection
of a relationship more difficult. The more the units in a study are
heterogeneous within conditions on an outcome variable, the greater will be the
standard deviations on that variable and on any others correlated with it.
Possible solution is blocking - measure relevant respondent characteristics and
use them for blocking or as covariates.
9. Inaccurate Effect Size Estimation: Some
statistics systematically overestimate or underestimate the size of an effect.
Threats to Validity
Construct Validity: The validity of inferences about
whether the cause-effect relationship holds over variation in persons,
settings, treatment variables, and measurement variables.
1. Inadequate Explication of Constructs
2. Construct Confounding - operations usually involve
more than one construct; failure to describe all the constructs may result in
incomplete construct inferences.
3. Mono-Operation Bias - using only one
operationalization of each construct. Single operations both under-represent
constructs and may contain irrelevancies.
4. Mono-Method Bias - when all operationalizations use
the same method, that method becomes part of the construct actually studied.
5. Confounding Constructs with levels of constructs - Inferences
about the constructs that best represent study operations may fail to describe
the limited levels of the construct that were actually studied
6. Treatment sensitive factorial structure - the
structure of a measure may change as a result of treatment, change that may be
hidden if the same scoring is always used. The structure itself becomes a
construct rather than just the treatment, person, etc.
7. Reactive Self-Report Changes - the
factorial structure and the level of responses can be affected by whether a
person is accepted into the treatment or control group separate from the
treatment effect.
8. Reactivity to the Experimental Situation - subjects
may try to guess what the experimenter is studying and give results that they
think the researcher wants to see. Sometimes referred to as the "placebo
effect."
9. Experimenter Expectancies - these
expectations become part of the molar treatment package. For example, teacher
expectations about student achievement become self fulfilling prophecies.
10. Novelty and Disruption Effects - when an
innovation is introduced, it can breed excitement, energy, and enthusiasm that
contribute to success, especially if little innovation previously occurred.
11. Compensatory Equalization - when two
conditions are being contrasted and the control group is compensated to equal
out the inequity.
12. Compensatory Rivalry - public assignment of units
to control and experimental conditions can sometimes cause rivalry in which the
control group tries to show that it can perform as well as the experimental
group.
13. Resentful Demoralization - members of
a group receiving a less desirable treatment may be resentful and demoralized,
changing their responses to the outcome measures.
14. Treatment Diffusion - the participants in one
condition receive some or all of the treatment in the other condition.
(Participants try to surreptitiously receive the treatment for its benefit)
External Validity: defined as the question, "to
what populations, settings, and variables can this effect be generalized?"
1. Interaction of the Causal Relationship with Units: An effect
found with certain kinds of units might not hold if other kinds of units had
been studied.
2. Interaction of the Causal Relationship over treatment
variations: An effect found with one treatment variation might
not hold with other variations of that treatment, or when that treatment is combined
with other treatments, or when only part of that treatment is used.
3. Interaction of the Causal Relationship with outcomes: An effect
found on one kind of outcome observation may not hold if other outcome
observations were used.
4. Interaction of the Causal Relationship with Settings: An effect
found in one kind of setting may not hold if other kinds of settings were to be
used.
5. Context Dependent Mediation: An exp
Threats to Validity
Internal Validity: Reasons why
inferences that the relationship between two variables is causal may be incorrect:
1. Ambiguous Temporal Precedence: Lack of
clarity about which variable occurred first.
2. Selection: sometimes at the start of an experiment, the
average person receiving one experimental condition already differs from the
average person receiving another condition. This difference might account for
any result
3. History: Events occurring concurrently with
treatment could cause the observed effect.
4. Maturation: Naturally occurring changes over time could be
confused with a treatment effect.
5. Regression: when units are selected for their
extreme scores, they will often have less extreme scores on other variables,
which can be confused with a treatment effect.
6. Attrition: Loss of respondents to treatment or to measurement
can produce artificial effects if that loss is systematically correlated with
conditions.
7. Testing: Exposure to a test can affect
scores on subsequent exposures to that test, which can be confused with a treatment
effect.
8. Instrumentation: The nature of a measure
may change over time or conditions in a way that can be confused with a
treatment effect. For example, a spring might become weaker over time and easier
to push, artificially increasing reaction times.
9. Additive and Interactive Effects
of Threats to Internal Validity: The impact of a threat can be added
to that of another threat or may depend on the level of another threat.
Statistical
Conclusion Validity
refers to the appropriate use of statistics to
infer whether the presumed independent and dependent variables co-vary.
1. Low Statistical Power: An insufficiently
powered experiment may incorrectly conclude that the relationship between
treatment and outcome is not significant
2. Violated Assumptions of Statistical Tests: can lead to
overestimating or underestimating the size and significance of an effect.
3. Fishing and the Error Rate Problem: Repeated
tests for significant relationships, if uncorrected for the number of tests,
can artificially inflate statistical significance.
4. Unreliability
of Measures: weakens the
relationship between two variables and strengthens or weakens the relationships
among three or more variables.
5. Restriction of Range: Reduced range on a variable
usually weakens the relationship between it and another variable.
6. Unreliability of Treatment
Implementation: If a treatment that is intended to be implemented
in a standardized manner is implemented only partially for some respondents,
effects may be underestimated compared with full implementation.
7. Extraneous Variance in the Experimental Setting: Some
features of an experimental setting may inflate error making detection of an
effect more difficult.
8. Heterogeneity of Units: Increased variability on the
outcome variable within conditions increases error variance, making detection
of a relationship more difficult. The more the units in a study are
heterogeneous within conditions on an outcome variable, the greater will be the
standard deviations on that variable and on any others correlated with it.
Possible solution is blocking - measure relevant respondent characteristics and
use them for blocking or as covariates.
9. Inaccurate Effect Size Estimation: Some
statistics systematically overestimate or underestimate the size of an effect.
Threats to Validity
Construct Validity: The validity of inferences about
whether the cause-effect relationship holds over variation in persons,
settings, treatment variables, and measurement variables.
1. Inadequate Explication of Constructs
2. Construct Confounding - operations usually involve
more than one construct; failure to describe all the constructs may result in
incomplete construct inferences.
3. Mono-Operation Bias - using only one
operationalization of each construct. Single operations both under-represent
constructs and may contain irrelevancies.
4. Mono-Method Bias - when all operationalizations use
the same method, that method becomes part of the construct actually studied.
5. Confounding Constructs with levels of constructs - Inferences
about the constructs that best represent study operations may fail to describe
the limited levels of the construct that were actually studied
6. Treatment sensitive factorial structure - the
structure of a measure may change as a result of treatment, change that may be
hidden if the same scoring is always used. The structure itself becomes a
construct rather than just the treatment, person, etc.
7. Reactive Self-Report Changes - the
factorial structure and the level of responses can be affected by whether a
person is accepted into the treatment or control group separate from the
treatment effect.
8. Reactivity to the Experimental Situation - subjects
may try to guess what the experimenter is studying and give results that they
think the researcher wants to see. Sometimes referred to as the "placebo
effect."
9. Experimenter Expectancies - these
expectations become part of the molar treatment package. For example, teacher
expectations about student achievement become self fulfilling prophecies.
10. Novelty and Disruption Effects - when an
innovation is introduced, it can breed excitement, energy, and enthusiasm that
contribute to success, especially if little innovation previously occurred.
11. Compensatory Equalization - when two
conditions are being contrasted and the control group is compensated to equal
out the inequity.
12. Compensatory Rivalry - public assignment of units
to control and experimental conditions can sometimes cause rivalry in which the
control group tries to show that it can perform as well as the experimental
group.
13. Resentful Demoralization - members of
a group receiving a less desirable treatment may be resentful and demoralized,
changing their responses to the outcome measures.
14. Treatment Diffusion - the participants in one
condition receive some or all of the treatment in the other condition.
(Participants try to surreptitiously receive the treatment for its benefit)
External Validity:
Defined as the question, "to
what populations, settings, and variables can this effect be generalized?"
1. Interaction of the Causal Relationship with Units: An effect
found with certain kinds of units might not hold if other kinds of units had
been studied.
2. Interaction of the Causal Relationship over treatment
variations: An effect found with one treatment variation might
not hold with other variations of that treatment, or when that treatment is combined
with other treatments, or when only part of that treatment is used.
3. Interaction of the Causal Relationship with outcomes: An effect
found on one kind of outcome observation may not hold if other outcome
observations were used.
4. Interaction of the Causal Relationship with Settings: An effect
found in one kind of setting may not hold if other kinds of settings were to be
used.
5. Context Dependent Mediation: An exp
Statistical Concepts:
Central
Tendency (mean, median, mode)
Variability : (Variable )Something
that the researcher/experimenter can
measure.
Statistics (what are they and what are the different types)
Statistical Concepts: A measure of central tendency is a single value that
attempts to describe a set of data by identifying the central position within
that set of data. As such, measures of central tendency are sometimes called
measures of central location. They are also classed as summary statistics. The
mean (often called the average) is most likely the measure of central tendency
that you are most familiar with, but there are others, such as the median and
the mode.
Rectangular Distribution : The scores in a rectangular distribution
are all about equally frequent or probable. An example of a rectangular
distribution is the theoretical distribution representing the six possible
scores that can be obtained by rolling a single six-sided die.
Bimodal Distribution: In the case of a bimodal distribution, two
distinct ranges of scores are more common than any other. A likely example of a
bimodal distribution would be the heights of the athletes attending the annual
sports banquet for a very large high school that has only two sports teams:
women’s gymnastics and men’s basketball. If this example seems a little
contrived, it should. Bimodal distributions are relatively rare, and they
usually reflect the fact that a sample is composed of two meaningful
subsamples.
Normal Distribution: The nice thing about the normal distribution
is that if you know that a set of observations is normally distributed, this
further improves your ability to describe the entire set of scores in the
sample.
Hypothesis vs. Theory: So what is the Relationship between theory
and data you might ask? Well the first relationship is
Deduction
Theory: A set of logically consistent statements
about some psychological phenomenon that best summarizes existing empirical
knowledge of the phenomenon organizes this knowledge in the form of precise statements of the relationship
among variables provides a tentative
explanation for the phenomenon serves as a basis for making predictions about
behavior, from theory to actual research.
Hypothesis: Prediction about specific events that is
derived from the theory.
Different types of hypotheses
Probability Theory: Probability Theory: From the classical
perspective, the probability of an event is a very simple thing: It is (a) the
number of all specific outcomes that qualify as the event in question divided
by (b) the total number of all possible outc
Reliability
(different types) Reliability:
refers to the consistency of a measure. Validity: is the extent to which
a test measures what it claims to measure. There is Three main features that
need to be included to measure are:
Control: refers
to how well the experimenter controlled the experiment. The control is
important because without control, researchers cannot establish cause and
effect.
Realism: is where
psychological research is provided information about how people in the real
world behave. If an experiment is too controlled, too artificial or situation,
participants can act differently than they would in real life.
Generalizability: is the
primary objective of psychological research is producing results that can be
generalized beyond the experiment setup.
Validity (different types)
Internal And External Objectivity can be
achieved form a thorough review of the literature and the development of a
theoretical framework. The literature
review should be presented so that the reader can judge the objectivity of the
research questions. Purpose of Research Design Provides the plan or blueprint
for testing research questions and hypotheses. Involves structure and strategy
to maintain control and intervention fidelity.
Variable:
Something that the researcher/experimenter can measure.
Independent
Variable: The variable the experimenter has control over, can change in
some way to see if it has an effect on other variables in the study.
Dependent Variable: The
variable that is measured to see if a place: change takes.
Theory: Definition of a theory: A set of
logically consistent statements about some psychological phenomenon that best
summarizes existing empirical knowledge of the phenomenon organizes this
knowledge in the form of precise statements of the relationship among variables
provides a tentative explanation for the phenomenon serves as a basis for
making predictions about behavior. So we have our method, we have our
assumptions, so how do we actually do research? Relationship between theory and
data
Hypothesis: Prediction about specific
events that is derived from the theory. Induction: Logical process of reasoning
from specific events to the theory (either confirming or disproving the
theory).
Definition: A way of knowing
characterized by the attempt to apply systematic, objective, empirical methods
when searching for causes of natural events. Probabilistic Statistical
determinism: Based on what we have observed, is the likelihood of two events
occurring together (whether causal, predictive, or simple relational) greater
than chance? Objectivity: without bias of the experimenter or participants.
Data-driven: conclusions are based on the data-- objective information.
Data-driven: conclusions are based on the data-- objective information.
Generalisability: The aim of
psychological research is to produce results which can then be generalised
beyond the setting of the experiment. If an experiment is lacking in realism we
will be unable to generalise. However, even if an experiment is high in
realism, we still may not be able to generalise.
Control Variable: The
variable that is not manipulated that serves as a comparison group from the
other variables in the study. This third variable is used to ensure that the
independent variable, when manipulated is actually having an effect on the
dependent variable. For example, if a similar change occurs in the control
variable as the dependent variable, this indicates that the change may not be
the result of the independent variable manipulation and may be a natural change
in the variable. In a experiment the researcher manipulates the independent
variable to see if it has an effect on the dependent variable.
Sample Variance: The sample
variance equals the mean squared deviation from
. A small means that the
observed values cluster around the average, while a large variance means that
they are more spread out. Thus, the variance is a measure of the “spread” in
the sampled values.
Selection Threat --
Differences in groups that exist before treatment or intervention begins.
Especially problematic when participants selected for belonging to a specific
group. Differences may be due to initial differences in groups – not treatment
or intervention.
Method: A technique used to analyze data.
Commonly, a method is aligned with a particular strategy for gathering data, as
particular methods commonly require particular types of data. “Method” is
therefore commonly used to refer to strategies for both analyzing and gathering
data.
Methodology: A body of practices,
procedures, and rules used by researchers to offer insight into the workings of
the world.
Insight: Evidence contributing to an
understanding of a case or set of cases. Comparative-historical researchers are
generally most concerned with causal insight, or insight into causal processes.
Treatment
Diffusion
- This occurs when a comparison group learns about the program either directly
or indirectly from program group participants. In a school context, children
from different groups within the same school might share experiences during
lunch hour. Or, comparison group students, seeing what the program group is
getting, might set up their own experience to try to imitate that of the
program group. In either case, if the diffusion of imitation affects the
posttest performance of the comparison group, it can have an jeopardize your
ability to assess whether your program is causing the outcome.
Threats to
internal validity
Ambiguous Temporal Precedence: Ambiguous
Directionality --
When the independent variable is not manipulated, the direction of the
influence is not always clear (e.g., impact of a therapist empathy on client
outcome – does therapist get warmer b/c client improves or vice versa?)
Ambiguous Directionality: How to fix
it?
Unless
the temporal order is clear, the directionality is difficult to determine.
Be
clear in statements of causality and describe when causality is unclear.
Selection: -- Differences
in groups that exist before treatment or intervention begins. Especially
problematic when participants selected for belonging to a specific group.
Differences may be due to initial differences in groups – not treatment or
intervention.
History: History Threat -- Events
outside experimental manipulation influence collective or individual
participation “Many change-producing events may have occurred between
Observation 1 and Observation 2 “ (Source: Internet Presentation) “History is more …[likely] the longer the
lapse between the pretest and posttest.” (Source: Alliant
Maturation: Maturation
Threat - Normal developmental changes in participants between pre and post
tests. Gains/Losses over time may be due to normal maturation.
Regression: Regression
Threat --
The tendency to drift towards the mean as one takes a test multiple times.
Especially problematic when choosing “extreme” scores which can “regress”
toward the mean due to influences other than treatment. “Occurs when individuals are
selected for an intervention or treatment on the basis of extreme scores on
a pretest. Extreme scores are more likely to reflect larger (positive or
negative) errors in measurement (chance factors). Such extreme measurement
errors are NOT likely to occur on a second testing.” (Source: Internet
Presentation)
Attrition: Attrition
Threat – In a longitudinal study where you need to measure responses from a
participant at multiple time points, attrition refers to when participants drop
out or leave a study, causes problems as it reduces available data, in
particular when it is different across groups.
In a longitudinal
study where you need to measure responses from a participant at multiple time
points, attrition refers to when participants drop out or leave a study, causes
problems as it reduces available data, in particular when it is different
across groups. When participants drop
out or leave a study, causes problems as it reduces available data, in
particular when it is different across groups.
Testing:
Testing Threat -- Changes in a test score due to taking it more than once,
through familiarization, recall.
Instrumentation:
Instrumentation threat: How do we fix it?
Randomly assign participants to two groups, one treatment
group & one control group.
Researchers can expect similar effects in treatment group
versus control group over time. Or, use
tests rather than observation, ratings, or interviews.
Additive and
Interactive Effects: This is when two internal validity threats are
present, and at least one of the threats’ effects is dependent on the other
threat or is greater because of the other threat.
External Validity:This is when the results found are not
generalizable to the population of interest.
Selection
(Interaction of Causal Units): This is a threat when the participants
used in a study are of a specific type such that the results found with them might
not apply (be generalizable) to other participants, but researchers imply this.
Interaction of the causal relationship
over treatment variations: :
This is a problem when the research study has a TREATMENT, but a weird
variation of the treatment is used, and results found may not be generalizable
to other variations of that treatment.
a. Example: Researchers test a manualized CBT
treatment. This CBT treatment is meant to be 12 weeks, but researchers did a 7
week version of it, so the results may be different. Effects found with this
treatment are not necessarily the same at results that would have been found
with the treatment they were trying to test (the 12 week treatment). OR
Researchers combined CBT with another treatment, which may find different
results. OR They only used part of the treatment, such as the Cognitive portion
of the treatment.
b. Remedy:
Use the whole treatment as it was meant to be given.
c. Risk:
Low. Researches would likely admit that they administered a treatment differently
than it was supposed to be administered.
Look in: Methods, measures, and Limitations.
Interaction of the causal relationship
with outcomes: This
is similar to the idea of mono operation bias, but just for the DV. This is a threat when the outcome (usually
just one Dependent Variable) was measured a certain way (usually with only one
measure). It is a problem when measuring the outcome in a different way could
have given different results.
Interaction of causal relationship
w/settings: This
is a threat when the research was done in a particular setting (environment),
and results may not be generalizable to a different setting. By setting we are
talking about specific research setting such as laboratory, school, home,
internet, ect. NOT geographic location like Texas or New York. Geographic
locations are more of a selection threat because we are talking about different
types of people with results that may not generalize to other types of people.
Here we are talking about a setting where results may not generalize to other
settings.
Context-dependent Mediation: This is a
threat when a mediating variable may not mediate the same way in a different
situation (different setting, or participants, or treatment, or task, ect).
This is only when the study has a mediating variable, and this relationship
(the variable mediating the relationship between two other variables) may not
be the same in a different situation. Example: Suppose a study used only
males to study depression. They found that owning more video games makes them
super excited and causes sleeplessness, which later causes depression in these
people. So, sleeplessness mediates the relationship between video games and
depression. However, this may not be the case if females were used. Owning video
games may bore them, which may make them depressed, but it doesn’t cause
sleeplessness because they aren’t excited about the games. So the video games
cause depression for them, but this isn’t mediated by sleeplessness. Or if different video games were used, then
sleeplessness may not mediate the relationship the same way.
1.Remedy: Try
to use generalizable situations.
2.Risk: RARE.
The study must have a mediating variable in it.
Look
in: Methods, Results, and Conclusions
Interaction of History and Treatment: This is
different than the Internal Validity threat History. That is when an event
affects participants and causes the results found. THIS one is when results
found from a study are not generalizable to other time periods. This usually
has to do with whole eras. It mostly applies to old studies, when times were
different, and results found may not be the same today.
Construct Validity Threats This is when researchers did not actually
measure the construct they intended to measure.
Inadequate
explication of constructs: This is when researchers do not get at a
construct all the way. This could be when researchers only measure part of a
construct. Or when they measure the wrong construct. Or when they use a measure
with low reliability or validity. Or when they use a measure that is too broad
to get at the measure they want. Or when they only use part of a measure but
don’t check the reliability and validity of this portion of the measure.
Construct confounding: There is a
construct that the researchers have not accounted for that is responsible for
causing or affecting the results and relationship found. There is a construct
here that is unaccounted for (missing). The must some evidence for this, good
reason to believe this is a problem.
Mono-operation bias: This is a
problem when only one instrument (measure) is used to measure ONE construct.
For any particular construct in the study, if there was only one thing
measuring it, this is a problem. It may occur multiple times in a study, but
only talk about one of those times when asked to describe a threat.
Mono-method bias: This is the
same thing except replace “instrument” or “measure” with “method”. Researchers
could use more than one measure for any ONE construct, but if that ONE
construct only has ONE method used to measure it, you may not be getting at the
construct as well as you could.
Confounding constructs with levels: Mono-method
bias: This is the same thing except replace “instrument” or “measure” with
“method”. Researchers could use more than one measure for any ONE construct,
but if that ONE construct only has ONE method used to measure it, you may not
be getting at the construct as well as you could.
Treatment-sensitive factorial structure:
This
is when participants who are exposed to treatment see a measure in a different
way than those who were not exposed to the treatment. For people who have
received the treatment, the answers may be broken down into different “factor
structures”. People who did not receive the treatment have answers that result
in a single factor. The structure of the measure is different between the
people who received treatment and the people who didn’t.
*** The following Construct Validity threats are somewhat specific and
Medium to Low risk. Look in Methods to find these***
Reactive Self-report changes: This is when
people change the way they are answering in a self-report because they want to
be accepted to the study of receive treatment, or because they want to appear
socially acceptable.
Reactivity to the experimental situation:
This
is when something in the experimental situation is affecting the way the
participants respond. This could include the Hawthorne effect- a reaction to
something in the environment. It could also include hypothesis guessing done by
the participants, or the placebo effect.
Experimental
Expectancies: This is also called the Rosenthal effect. It is when the experimenter conveys the
message of what is wanted from the participant and that leads PT to act in a
certain way. Instead of actually measuring the construct naturally in the
participant, you are just picking up on how the participant is trying to meet
expectations
Novelty & Disruption Effects: This is when
participants may respond better to things that are new. So instead of just
measuring the construct as it is meant to be, the fact that the treatment or
measure is new to them may skew things. OR they may respond particularly poorly
or poorly to something that disrupts their routine.
Compensatory Equalization: This is when
treatment provides something very desirable (researchers are testing a Free
Ice-Cream treatment), and researchers attempt to compensate by giving things to
the non-treatment group (vouchers they can use for ice cream after the study).
Doing this must then be included as part of the treatment construct
description.
Compensatory Rivalry: This is when
(competitive) participants who are not receiving treatment may be motivated to
show they can do as well as those receiving treatment. This rivalry motivation
could change what you are measuring, and must then be included as part of the
treatment construct description.
Resentful Demoralization: This is when
there is a very desirable treatment (Free Ice Cream Therapy). Participants not
receiving this desirable treatment may be so resentful or demoralized that they
may respond more negatively than otherwise, and this resentful demoralization
must then be included as part of the treatment construct. The people not
getting the free ice cream may be upset and report extra depression because
they don’t care anymore. Then the results would indicate tha
Inadequate Pre-operational Explication of
constructions.
We didn’t clearly define things before we
started. Mono-operational Bias. We use a single measure of a construct that is
not complete. Mono-method Bias. Using only one approach to measuring a
construct. We only use surveys to assess employee engagement they are
self-report and subject to bias as self-report. Hypothesis Guessing.
Participants try to guess what we are looking for an act differently.
Participants learn they are in a pilot program aimed at improving success so
they work harder or report better results.
Must due: External validity randomly select
people to study (not randomly assigned). Replication even on small scale over
time over sample of a study. Clear about how you select people, how do we get
people to this discreetness time settings.
Internal Validity: Confidence in cause and
effect Requirements. Difference in Dependent Variable. Independent variable
before Dependent Variable. Extraneous factors (alterative rival hypotheses. In
theory: Two identical groups Pretest-posttest design.
Makes sure I use the interactions: selection, settings and history. Interactions
selection and treatment. Selected have a different reaction to our program than
other programs. Make sure you select high potential for our program.
Interactions setting and treatment. Results in one setting may not work in
other settings. Interactions History and treatment the results we see today may
not be true for other times.
External Validity
So basically External Validity threats arise from
1. Interaction of Selection and Treatment -Those we select
have a different reaction our program than others (if we select high potential
people, self-fulfilling prophecy will affect the outcome/DV)
2. Interaction of Setting and Treatment - Results in one
setting may not work in another. Recognition programs at hospital for doctors
wont work for mcdonalds slave workers
3. Interaction of History and Treatment -results
we see may not hold true for other points in time. Stock pickers at tech bubble
won't get same result of Apple stock as they would now. Difference of timing
aka History and Treatment
What
is Random Error?
Random
error is caused by any factors that randomly affect measurement of the variable
across the sample. For instance, each person's mood can inflate or deflate
their performance on any occasion. In a particular testing, some children may
be feeling in a good mood and others may be depressed. If mood affects their
performance on the measure, it may artificially inflate the observed scores for
some children and artificially deflate them for others. The important thing
about random error is that it does not have any consistent effects across the
entire sample. Instead, it pushes observed scores up or down randomly. This
means that if we could see all of the random errors in a distribution they
would have to sum to 0 -- there would be as many negative errors as positive
ones. The important property of random error is that it adds variability to the
data but does not affect average performance for the group. Because of this,
random error is sometimes considered noise.
What
is Systematic Error?
Systematic
error is caused by any factors that systematically affect measurement of the
variable across the sample. For instance, if there is loud traffic going by
just outside of a classroom where students are taking a test, this noise is
liable to affect all of the children's scores -- in this case, systematically
lowering them. Unlike random error, systematic errors tend to be consistently
either positive or negative -- because of this, systematic error is sometimes
considered to be bias in measurement.
Reducing
Measurement Error
So,
how can we reduce measurement errors, random or systematic? One thing you can
do is to pilot test your instruments, getting feedback from your respondents
regarding how easy or hard the measure was and information about how the
testing environment affected their performance. Second, if you are gathering
measures using people to collect the data (as interviewers or observers) you
should make sure you train them thoroughly so that they aren't inadvertently
introducing error. Third, when you collect the data for your study you should
double-check the data thoroughly. All data entry for computer analysis should
be "double-punched" and verified. This means that you enter the data
twice, the second time having your data entry machine check that you are typing
the exact same data you did the first time. Fourth, you can use statistical
procedures to adjust for measurement error. These range from rather simple
formulas you can apply directly to your data to very complex modeling
procedures for modeling the error and its effects. Finally, one of the best things
you can do to deal with measurement errors, especially systematic errors, is to
use multiple measures of the same construct. Especially if the different
measures don't share the same systematic errors, you will be able to
triangulate across the multiple measures and get a more accurate sense of
what's going on
TYPES OF RELIABILITY AND
VALIDITY
Instructions: Match the definitions/examples with the
correct type of reliability or validity. Technically there may be more than one
correct answer for each definition/example, but each term is intended to be
used only once.
____7. Error
___2. Reliability
____8. Face validity
___3. Validity
____9. Predictive validity
___4. Construct validity
____10. Inter-rater reliability
___5. Criterion validity
____11. Discriminant validity
___6. Internal reliability/Inter-item consistency
____12. Content validity
Social Science Research
Methods
Distinction
between empirical vs. non-empirical ways of knowing
- (UN) SCIENTIFIC THINKING Because someone told us that something is true. 2. Reasoning = A
priori method (proposed by Charles Peirce): a person develops a belief by
reasoning, listening to others’ reasoning, and drawing on previous
intellectual knowledge – not based on experience or direct observation. So now that we have a firm grounding in
some of the basic theories and theorists within psychology, and a basic
understanding of the multiple conceptions of personality and how it
develops, the logical next step is to explore how we come to these
conclusions with regard to models of personality and other psychological
concepts. In other words, how do we scientifically ascertain whether these
theories hold water, and how we can most accurately quantify and
categorize human behavior, while still attempting to allow for the grey
area of individual differences?
- (Somewhat) scientific thinking . Experience
- Experience based errors in thinking The first one is an Illusory Correlation, or thinking that one has
observed an association between events that either:
- Confirmation Bias – In psychology and cognitive science, confirmation bias (or confirmatory bias) is a tendency to search for or interpret information in a way that confirms one's preconceptions, leading to statistical errors.
- Confirmation Bias – In psychology and cognitive science,
confirmation bias (or confirmatory bias) is a tendency to search for or
interpret information in a way that confirms one's preconceptions, leading
to statistical errors. Finally, the last common error in psychological
research is the Availability Heuristic is a mental shortcut that relies on
immediate examples that come to mind. When you are trying to make a
decision, a number of related events or situations might immediately
spring to the forefront of your thoughts. As a result, you might judge
that those events are more frequent and possible than others. You give
greater credence to this information and tend to overestimate the
probability and likelihood of similar things happening in the future.
- The scientific method: 4. Scientific Method:
- Steps of The Scientific Method:
- Scientific Thinking in Research: So what are the CRITERIA FOR SCIENTIFIC METHOD?
- ASSUMPTIONS ABOUT BEHAVIORS OR OBSERVATIONS:
- Social Science Research Methods Method: a technique used to analyze data. commonly, a method is aligned with a particular strategy for gathering data, as particular methods commonly require particular types of data. “method” is therefore commonly used to refer to strategies for both analyzing and gathering data.
- Methodology: A body of practices, procedures, and rules used by researchers to offer insight into the workings of the world.
- Insight: Evidence contributing to an understanding of
a case or set of cases. Comparative-historical researchers are generally
most concerned with causal insight, or insight into causal processes.
- Comparative-historical
analysis: A prominent research
tradition in the social sciences, especially in political science and
sociology. Works within this research tradition use comparative-historical
methods, pursue causal explanations, and analyze units of analysis at the
meso- or macro-level.
- Epistemology: A branch of philosophy that considers the possibility of knowledge and understanding. Within the social sciences, epistemological debates commonly focus on the possibility of gaining insight into the causes of social phenomena.
- Positivism: An epistemological approach that was popular among most of the founding figures of the social sciences. It claims that the scientific method is the best way to gain insight into our world. Within the social sciences, positivism suggests that scientific methods can be used to analyze social relations in order to gain knowledge..
- Ethnographic
methods: A type of social scientific method that gains
insight into social relations through participant observation, interviews,
and the analysis of art, texts, and oral histories. It is commonly used to
analyze culture and is the most common method of anthropology.
- Case
Study (Within-case methods): A category of methods used in the social sciences that offer
insight into the determinants of a particular phenomenon for a particular
case. For this, they analyze the processes and characteristics of the
case.
- Ideographic explanation: Causal explanations that explore the causes of
a particular case. Such explanations are not meant to apply to a larger
set of cases and commonly focus on the particularities of the case under
analysis.
- BROAD CATEGORIES OF SOCIAL SCIENCE RESEARCH
- Statistical methods: The most common subtype of comparative methods. It operationalizes variables for several cases, compares the cases to explore relationships between the variables, and uses probability theory to estimate causal effects or risks. Within the social sciences, statistics uses natural variation to approximate experimental methods. There are diverse subtypes of statistical methods.
- Comparative methods: Diverse methods used in the social sciences that offer insight through cross-case comparison. For this, they com- pare the characteristics of different cases and highlight similarities and differences between them. Comparative methods are usually used to explore causes that are common among a set of cases. They are commonly used in all social scientific disciplines.
- Experimental
methods: The most powerful method used in the social
sciences, albeit the most difficult to use. It manipulates individuals in
a particular way (the treatment) and explores the impact of this treat-
ment. It offers powerful insight by controlling the environment, thereby
allowing researchers to isolate the impact of the treatment
- How we
conduct empirical research: Key Terms and Concepts
- Field vs. Laboratory Research
- Natural sciences: In natural sciences,
most experiments have been undertaken in carefully controlled laboratory
conditions Seeking laws of physics, etc. Inanimate objects are unlikely to
react differently in controlled and uncontrolled conditions The main goal
is to avoid contaminating the experiment via the introduction of third
variables. Far more exacting and finer measurement is possible under
controlled conditions.
- Laboratory experiments in social science: Closely controlled social science experiments do have the advantage of limiting the impact of third variables, but the unnatural situation presents problems for applying the findings outside the laboratory “Artificiality” of laboratory setting
- Environmental Impact: Human behavior is
sensitive to the environment within which it occurs People act differently
in a laboratory than in the natural world Several characteristics of
laboratories are thought to influence behavior The very third variables
controlled in the lab may be the ones that determine behavior in the real
world So, findings from laboratory experiments may only be valid for laboratory
environments.
- An example: New commercials are
tested in controlled conditions Eye tracking Liking for commercials
Influence on purchase interest May try to provide less artificial
conditions for study Simulated living room Commercials that test high in
lab experiments often do not work very well when used in real marketing
campaigns.
- So, experiments move out of the lab: Researchers want to retain some of the advantages of the experiment: Ability to manipulate/introduce the independent variable and to control how much of it is presented Time order—which comes first While sacrificing some of their ability to control third variables The goal is to improve our ability to generalize our findings to the real world.
- Field Research: One way to do so is to
carry out a ‘field experiment’ The researcher still manipulates the
independent variable, but she does so within the natural world
- What problems do we encounter? Naturalistic
Observation: “Naturalistic observation involves methods designed to
examine behavior without the experimenter interfering with it in any
way.” Prominent Example: Research
of Margaret Mead Participant Observation:
- Expense Tradeoff between extensive and
intensive study Budget constraints on number of sites, etc. Access/permission Some research
may present concerns to authorities, citizens, etc. Gain
authorization/support prior to entering the field Maintain good relationships
with community leaders, etc. throughout the intervention/research.
- Capturing ‘natural
experiments’ Sometimes unusual or
unique events occur
- “Natural experiments” Because most such events are unplanned, the
ability to prepare for them is limited May keep a research group,
materials and resources ready for certain types of events Must engage in
‘firehouse research’ gathering as much data as possible in a short time Inefficient,
and may miss important data However, real-world events, etc. may provide
very valuable data—may have both internal and external validity
- So what can we
conclude? When understanding the
distinction between laboratory and field research and decide which one to
choose. To do this we much clarify the tradeoffs:
- Important Types of Validity Internal
Validity - One of the keys to understanding internal validity (IV) is
the recognition that when it is associated with experimental research it
refers both to how well the study was run (research design, operational
definitions used, how variables were measured, what was/wasn't measured,
etc.), and how confidently one can conclude that the change in the
dependent variable was produced solely by the independent variable and not
extraneous ones. In group experimental research, IV answers the question,
"Was it really the treatment that caused the difference between the
means/variances of the subjects in the control and experimental
groups?" Similarly, in single-subject research (e.g., ABAB or
multiple baseline), IV attempts to answer the question, "Do I really
believe that it was my treatment that caused a change in the subject's
behavior, or could it have been a result of some other factor?" In
descriptive studies (correlational, etc.) internal validity refers only to
the accuracy/quality of the study (e.g., how well the study was run-see
beginning of this paragraph).
- Reliability and Validity and Field Vs. Laboratory Research: Field Research: High internal validity, low external validity, Low reliability. Laboratory Research: High external validity, low internal validity, High reliability.
- Comparative- Historical methods: Since the rise of the social sciences, researchers have used comparative- historical methods to expand insight into diverse social phenomena and, in so doing, have made great contributions to our understanding of the social world.
- Comparative-Historical
Analysis: Mahoney and
Rueschemeyer (2003) refer to it as comparative-historical analysis in
recognition of the tradition’s growing multidisciplinary character. In
addition to sociology, comparative-historical analysis is quite prominent
in political science and is present—albeit much more marginally—in
history, economics, and anthropology.
- 4 types of comparative-historical research:
- Comparative and Historical Research by number of cases and length of time studied:
- How do we understand Comparative Historical
Research? As the Venn diagram in
this Figure depicts, comparative-historical analysis has four main
defining elements. Two are methodological, as works within the research
tradition employ both within-case methods and comparative methods.
Comparative-historical analysis is also defined by epistemology.
Specifically, comparative-historical works pursue social scientific
insight and therefore accept the possibility of gaining insight through
comparative-historical and other methods. Finally, the unit of analysis is
a defining element, with comparative-historical analysis focusing on more
aggregate social units.
- Historical
methods, also known as historiography, are the most common analytic techniques used
in the discipline of history. They are generally used to explore either
what happened at a particular time and place or what the characteristics
of a phenomenon were like at a particular time and place.
- Comparative-Historical
Methods in Comparative Perspective: Similar to statistical and experimental
methods, comparative-historical methods employ comparison as a means of
gaining insight into causal determinants. Similar to ethnographic and
historical methods, comparative-historical methods explore the
characteristics and causes of particular phenomena. Comparative-historical analysis,
however, does not simply combine the methods from other major
methodological traditions—none of the major comparative methods is very
common in comparative-historical analysis.
As a consequence, comparative-historical researchers commonly avoid
statistics and simply focus on causal processes. Additional reasons for
the limited use of statistical comparison within the
comparative-historical research tradition include the limited availability
of historical data needed for appropriate statistical analyses and the
small number of cases analyzed by comparative-historical researchers.
- So what does this tool-kit look like? • Historically specific. It is likely to be limited to the specific time(s) and place(s) studied, like traditional historical research.
- Comparative
Historical “toolkit” Besides comparative
methods, comparative-historical scholars employ several different types of
within-case methods: Ethnography
- Historical Events Research &
Event-Structure Analysis: It often utilizes a process known as
Historical Events Research.
- Steps of Event-Structure Analysis: An event structure
analysis requires several steps:
- More on Oral
Histories: Another way to get a
very rich understanding of how individuals experienced historical events
is through oral history.
- Historical
Process Research: Most likely to be qualitative and case-oriented (traditional
history of country X)
- Cross-Sectional Comparative Research: Comparisons between
countries during one time period can help social scientists identify the
limitations of explanations based on single-nation research. These
comparative studies may focus on a period in either the past or the
present. Could be quantitative (!!!!) or qualitative.
- Cross-Sectional vs.
Longitudinal: Cross-Sectional
Research – Takes place by looking at an event at one time point.
- Problems With
Historical Research: Reliance on Secondary vs. Primary sources of information (data).
- Case Study Method: “An empirical inquiry about a contemporary
phenomenon (e.g., a “case”), set within its real-world context—especially
when the boundaries between phenomenon and context are not clearly evident
(Yin, 2009a, p. 18; SagePub, 2014)’
- Assumptions: “Among other features,
case study research assumes that examining the context and other complex
conditions related to the case(s) being studied are integral to
understanding the case(s) (SagePub, 2014).”
- Concerns and
Problems: Often considered the
“method of last resort”
- Three Steps in designing a “Case”:
- Data Collection:
- Evidence from multiple sources:
- Case Study Protocol: The typical protocol
consists of a set of questions to be addressed while collecting the case
study data (whether actually taking place at a field setting or at your
own desk when extracting information from an archival source).
- Collect Data for Rival Explanations: A final data
collection topic stresses the role of seeking data to examine rival
explanations. The desired rival thinking should draw from a continual
sense of skepticism as a case study proceeds.
- Presenting your Case: You need to present the evidence in your case
study with sufficient clarity (e.g., in separate texts, tables, and
exhibits) to allow readers to judge independently your later
interpretation of the data.
- Case Study Data
Analysis: Whether using computer
software to help you or not, the researcher will be the one who must
define the codes to be used and the procedures for logically piecing
together the coded evidence into broader themes—in essence creating your
own unique algorithm befitting your particular case study. The strength of
the analytic course will depend on a marshaling of claims that use your
data in a logical fashion.
- Techniques:
- What about when
quantitative data is available? Correlations between single case studies can
be used only if there is sufficient data to run a statistical analysis.
- Meta Analysis: A study
of multiple Case Studies: The logic for such a cross-case synthesis
emulates that used in addressing whether the findings from a set of
multiple experiments—too small in number to be made part of any
quantitative meta-analysis (a study of the results of other
studies)—support any broader pattern of conclusions.
- Can we generalize at
all from a Case Study? To the extent that any study concerns itself
with generalizing, case studies tend to generalize to other situations (on
the basis of analytic claims), whereas surveys and other quantitative
methods tend to generalize to populations (on the basis of statistical
claims).
- Quantitative and Qualitative Research Methods:
- Example of Survey Used in Quantitative Research: So as we can see here,
this is an example of the, what are known as likert-type scales which are
used to numerically quantify a research participant’s response. Each
response is assigned a number which can then be entered into a statistical
analysis to utilize mathematical principles to generate understandable
results based on a smaller sample of people which is taken from the larger
population.
- Relationship between
the Sample and the Population: So when we think about this sample vs.
population issue, what are we talking about, well essentially we are
saying that since we cant run an analysis on an entire population, a feat
that would be nearly impossible we must collect data from the population
that is large enough to use the inferential statistical methods, based on
rules of probability to make a generalization to what is occurring in the
population. So in the case of heavy metal musical preference and dysphoric
mood, we are essentially stating that because we can not effectively
sample all heavy metal listeners in the world and give them our survey,
instead we want to collect a representative sample from the larger
population.
- When to use Quantitative Research:
- Advantages and Disadvantages:
- QuantitativeQuantitative Advantages:
- Qualitative Research: Focus on “language
rather than numbers”
- Advantages and Disadvantages: Qualitative:
- Qualitative Research in Psychology: “Today,
a growing number of psychologists are re-examining and re-exploring
qualitative methods for psychological research, challenging the more
traditional ‘scientific’ experimental approach”
- When to use Qualitative Research:
- Content and Thematic Analysis - Content Analysis, or Thematic Analysis (the
terms are frequently used interchangeably and generally mean much the
same), is particularly useful for conceptual, or thematic, analysis or
relational analysis. It can quantify the occurrences of concepts selected
for examination (Wilkinson & Birmingham, 2003).
- Grounded Theory - is frequently considered to offer researchers a suitable
qualitative method for in-depth exploratory investigations. . It is a
rigorous approach which provides the researcher with a set of systematic
strategies and assumes that when investigating social processes
qualitatively from the bottom up there will be an emergence of a working
theory about the population from the qualitative data (Willig, 2008, pp.
44).
- Discursive psychology
and Discourse Analysis: Discourse Analysis: The discursive
approach looks to verbal behavior as a more direct means of uncovering
underlying cognitions (Harré,1995) rather than assigning a numerical value
‘score’ or scale to a behavior. This approach takes the view that
interpretation and empathy are involved in attempting to understand human
behavior. Self-report, from people being studied, can then become a
valuable resource in its own right.
- What do we do if we want the best of both worlds?
- Statistics: are a set of mathematical procedures for
summarizing and interpreting observations. These observations are
typically numerical or categorical facts about specific people or things,
and they are usually referred to as data.
- Central Tendency and
DispersionCentral: Tendency Dispersion Variability Range
Sigma.
- Shape of Distribution:
- Inferential Statistics: Decisions about what
to conclude from a set of research findings (sample date) need to be made
in a logical, unbiased fashion, using mathematical principles to make
presumptions about the population.
- Probability Theory: From the classical perspective, the
probability of an event is a very simple thing: It is (a) the number of
all specific outcomes that qualify as the event in question divided by (b)
the total number of all possible outcomes.
- Type I Error: incorrectly rejecting
the null hypothesis when it is, in fact, correct.
- Type II Error: occurs when we fail
to reject an incorrect null hypothesis.
- Effect Size and Significance TestingEffect Size: the magnitude of the
effect in which they happen to be interested.
- Important Statistical Notation
- Internal Validity –
How valid a study is: the more we avoid confounding variables (variables that could
interfere in attaining the true results of our study) the higher the level
of internal validity.
- Attrition Threat – In a longitudinal study where you need to
measure responses from a participant at multiple time points, attrition
refers to when participants drop out or leave a study, causes problems as
it reduces available data, in particular when it is different across
groups.
- **Confounding
Variables specially whether it avoids confounding (more than one possible
independent variable [cause] acting at the same time). The less chance for
confounding in a study, the higher its internal validity is.
- Attrition Threat – In a longitudinal study where you need to measure responses from a
participant at multiple time points, attrition refers to when participants
drop out or leave a study, causes problems as it reduces available data,
in particular when it is different across groups. When participants drop
out or leave a study, causes problems as it reduces available data, in
particular when it is different across groups.
- Attrition: How do we fix it?
- Treatment Diffusion - This occurs when a comparison group learns about the program either
directly or indirectly from program group participants. In a school
context, children from different groups within the same school might share
experiences during lunch hour. Or, comparison group students, seeing what
the program group is getting, might set up their own experience to try to
imitate that of the program group. In either case, if the diffusion of
imitation affects the posttest performance of the comparison group, it can
have an jeopardize your ability to assess whether your program is causing
the outcome. Notice that this threat to validity tend to equalize the
outcomes between groups, minimizing the chance of seeing a program effect
even if there is one. References
http://www.socialresearchmethods.net/kb/intsoc.php
- Treatment Diffusion: How do we fix it? By isolating one group of participants from the other, this mitigates the likelihood that demand characteristics of the contrast between treatment conditions could interfere with the results and threaten the internal validity of the study in this way.
- Testing Threat -- Changes in a test score due to taking it more than once, through familiarization, recall. Also called “pretest sensitization,” this refers to the effects of taking a test upon performance on a second testing. Merely having been exposed to the pretest may influence performance on a posttest. Testing becomes a more viable threat to internal validity as the time between pretest and posttest is shortened.” (Source: Internet Presentation)
- Testing Threat: How do we fix it?
- History Threat -- Events outside experimental manipulation influence collective or individual participation
- History Threat: How do we Fix it?
- Instrumentation Threat -- Changes in the measurement or procedure
over the course of a study (e.g., with observations, coding systems over
time, drift systematically). Changes
in the measurement or procedure over the course of a study (e.g., with
observations, coding systems over time, drift systematically).
- Instrumentation threat: How do we fix it?
- Selection Threat -- Differences in groups that exist before treatment or intervention begins. Especially problematic when participants selected for belonging to a specific group. Differences may be due to initial differences in groups – not treatment or intervention.
- Selection Threat: How do we fix it?
- Maturation Threat - Normal developmental changes in participants between pre and post tests. Gains/Losses over time may be due to normal maturation.
- Maturation Threat: How do we fix it?
- Inequitable Treatments Threat -- When participants in one group outperform or underperform relative to another group as a result of study expectation of a certain performance.
- Inequitable Treatments Threat: How do we fix it?
- Threats to Internal Validity
- Training of treatment administrators and personnel associated with the study
- Statistical Regression Threat -- The tendency to drift towards the mean as one takes a test multiple times. Especially problematic when choosing “extreme” scores which can “regress” toward the mean due to influences other than treatment.
- Statistical Regression Threat: How to fix it?
- Interaction with Selection (with other factors)
- Interaction with selection with other factors threat: How to fix it?
- Ambiguous Directionality -- When the independent variable is not manipulated, the direction
of the influence is not always clear (e.g., impact of a therapist empathy
on client outcome – does therapist get warmer b/c client improves or vice
versa?)
- Ambiguous Directionality: How to fix it?
- What is a threat to External Validity?
- Selection (Interaction of Causal Units): This is distinguished from selection threat to internal validity. This is the selection threat to external validity. You can think of this one as the interaction of causal units. This is a threat when the participants used in a study are of a specific type such that the results found with them might not apply (be generalizable) to other participants, but researchers imply this.
- Remedy: Either try to have participants that are very representative (don’t miss out on certain people by only doing college students or white people or high SES etc), or don’t try to generalize to other people.
- Interaction of the causal relationship over treatment variations: This is a problem when the research study has a TREATMENT, but a weird variation of the treatment is used, and results found may not be generalizable to other variations of that treatment.
- Remedy: Use the whole treatment as it was meant to be given.
- Interaction of the causal relationship with outcomes: This is similar to the idea of mono operation bias, but just for the DV. This is a threat when the outcome (usually just one Dependent Variable) was measured a certain way (usually with only one measure). It is a problem when measuring the outcome in a different way could have given different results.
- Remedy: Use more than one way to measure the DV.
- Interaction of causal relationship w/settings: This is a threat when the research was done in a particular setting
(environment), and results may not be generalizable to a different setting.
By setting we are talking about specific research setting such as
laboratory, school, home, internet, ect. NOT geographic location like
Texas or New York. Geographic locations are more of a selection threat
because we are talking about different types of people with results that
may not generalize to other types of people. Here we are talking about a
setting where results may not generalize to other settings.
- Remedy: Try to do studies in the appropriate types of settings you are
trying to generalize to or in more than one setting.
- Context-dependent
Mediation: This is a threat when a mediating variable may not mediate the same
way in a different situation (different setting, or participants, or
treatment, or task, ect). This is only when the study has a mediating
variable, and this relationship (the variable mediating the relationship
between two other variables) may not be the same in a different situation.
- Remedy: Try to use generalizable situations.
- Interaction of History and Treatment: This is different than the Internal Validity threat History. That is when an event affects participants and causes the results found. THIS one is when results found from a study are not generalizable to other time periods. This usually has to do with whole eras. It mostly applies to old studies, when times were different, and results found may not be the same today.
Development, Reliability, and Validity of a Dissociation Scale.
BERNSTEIN, EVE M. Ph.D.; PUTNAM, FRANK W. M.D.
Abstract
(Somewhat) scientific thinking
knowing by direct observation or experience.
Subject to errors in thinking!
Experience based errors in thinking
(b) exists but is not as strong as is believed,
or (c) is in the opposite direction from what is believed.
Experience based errors in thinking
In psychology and cognitive science, confirmation bias (or confirmatory bias) is a tendency to search for or interpret information in a way that confirms one's preconceptions, leading to statistical errors.
The scientific method
Definition: A way of knowing characterized by the attempt to apply systematic, objective, empirical methods when searching for causes of natural events.
Probabilistic Statistical determinism: Based on what we have observed, is the likelihood of two events occurring together (whether causal, predictive, or simple relational) greater than chance?
Objectivity: without bias of the experimenter or participants.
Data-driven: conclusions are based on the data-- objective information.
Steps of The Scientific Method
Scientific Thinking in Research
ASSUMPTIONS ABOUT BEHAVIORS OR OBSERVATIONS:
Determinism: Events and behaviors have causes.
Discoverability: Through systematic observation, we can discover causes – and work towards more certain and comprehensive explanations through repeated discoveries.
Research Question
A set of logically consistent statements about some psychological phenomenon that
best summarizes existing empirical knowledge of the phenomenon
Deduction: Reasoning from a set of general statements towards the prediction of some specific event. Based on a theory, one can deduct an event or behavior given particular conditions.
Hypothesis: Prediction about specific events that is derived from the theory.
Induction: Logical process of reasoning from specific events to the theory (either confirming or disproving the theory).
Adapted from Shafron (2012), Lange (2013) Chapter 1, & Habarth ( 2012)
Methodology
Comparative-Historical Analysis
Epistemology
Ethnographic Methods
Ethnographic Methods
Case Study
Ideographic Explanation
Comparative Methods
Statistical Methods
Experimental Methods
BROAD CATEGORIES OF SOCIAL SCIENCE RESEARCH
2. Laboratory Research versus Field Research
3. Quantitative versus Qualitative Research
How we conduct empirical research: Key Terms and Concepts
Adapted from Miller (2014) & University of Kentucky, (2014), Psychology Press (2004), Huitt & Kaeck (1999)
**Confounding Variables
An example
.
Reliability and Validity and Field Vs. Laboratory Research
.
Reliability and Validity and Field Vs. Laboratory Research
Comparative-Historical Analysis
4 types of comparative-historical research
Comparative and Historical Research by number of cases and length of time studied
Comparative-Historical Methods in Comparative Perspective
As a consequence, comparative-historical researchers commonly avoid statistics and simply focus on causal processes. Additional reasons for the limited use of statistical comparison within the comparative-historical research tradition include the limited availability of historical data needed for appropriate statistical analyses and the small number of cases analyzed by comparative-historical researchers.
Historical Events Research & Event-Structure Analysis
Historical Process Research
Most likely to be qualitative and case-oriented (traditional history of country X)
Cross-Sectional Comparative Research
Adapted from Miller (2014) & University of Kentucky, (2014), Psychology Press (2004), Huitt & Kaeck (1999)
When to use the Case Study Method
Concerns and Problems
Three Steps in designing a “Case”
1. Direct observations (e.g., human actions or a physical environment)
* You are always better off using multiple rather than single sources of evidence.
Case Study Data Analysis
What about when quantitative data is available?
Can we generalize at all from a Case Study?
- Interactions selection and treatment= those we select have a different reaction our program than others. We select high potentials for our pilot; it works better for them than others.
- Interactions Setting and treatment= results in one setting may not work in another. Recognition programs that work in a hospital may not work in manufacturing, many not work due settings etc.
- Interactions History and treatment= the results we see may not hold true for other points in time. Stock pickers were very successful during the “tech bubble” have not been able to replicate the success since.
http://www.socialresearchmethods.net/kb/intsoc.php
Well, Psychological
Research is defined as the scientific exploration, designed to describe,
predict, and control human behavior, and to understand the hard facts of the
human mind across diverse psychological and cross-cultural populations.
So the first
logical question before engaging in any kind of research is, how do we know
something to be true in day to day life?
Well the first way
is Authority, because someone told us that something is true. This could be any
authority figure, like a professor, or someone in the media. Because someone
told us it’s the truth, we often believe it to be so. Obviously, this has
issues from a scientific perspective. When engaging in research we can’t rely
on what others tell us alone, we need to have hard, replicable data to support
it.
The second method
is Reasoning. The main method of reasoning is the A priori method (proposed by
Charles Peirce): where a person develops a belief by reasoning, listening to
others’ reasoning, and drawing on previous intellectual knowledge – not based
on experience or direct observation. While this might work when developing
opinions in day-to-day life, we can’t say that this gives us hard facts that
are generalizable to any population. Opinions derived from this method, however
can be said to create a good starting point for research.
Empiricism:
knowing by direct
observation or experience.
Subject to errors
in thinking!
Threats to Internal
Validity. So as we start to move towards scientific thinking we get to our
third way of knowing, Experience, or knowing by direct observation. While this
is all well and good, it is subject to errors in thinking. Indeed, our
perceptions may differ between individuals when encountering the same
experience, so we cant say that what we come up with will always be accurate.
And, in fact there are several well known types of errors in experience-based
conclusions and in psychological research in general.
(a) doesn’t exist,
(b) exists but is
not as strong as is believed,
or (c) is in the
opposite direction from what is believed
The term was first coined in 1973 by
psychologists Amos Tversky and Daniel Kahneman. They suggested that the
availability heuristic occurs unconsciously and operates under the principle
that "if you can think of it, it must be important." Things that come
to mind more easily are believed to be far more common and more accurate
reflections of the real world.
For example, after seeing several news reports
about car thefts, you might make a judgment that vehicle theft is much more
common than it really is in your area. This type of availability heuristic can
be helpful and important in decision-making. When faced with a choice, we often
lack the time or resources to investigate in greater depth. Faced with the need
to an immediate decision, the availability heuristic allows people to quickly
arrive at a conclusion.
While it can be
useful at times, the availability heuristic can lead to problems and errors.
Reports of child abductions, airplane accidents, and train derailments often
lead people to believe
that such events are much more typical than they truly are.
Definition: A way
of knowing characterized by the attempt to apply systematic, objective,
empirical methods when searching for causes of natural events.
Probabilistic Statistical
determinism: Based on what we have observed, is the likelihood of two events
occurring together (whether causal, predictive, or simple relational) greater
than chance?
Objectivity:
without bias of the experimenter or participants.
Ask a Question
Do Background Research
Construct a Hypothesis
Test Your Hypothesis by Doing an Experiment
Analyze Your Data and Draw a Conclusion
Communicate Your Results
Well the first is
that its:
Empirical: All
information is based on observation.
Once again, that it
is Objective: Observations are verified by others.
For it to be organized
it has to be Systematic: Observations are made in a step-by-step fashion.
It has to be
Controlled: Potentially confusing factors are eliminated.
And finally,
results must be made Public: Built on previous research, open to critique and
replication, building towards theories.
Lawful: Every event
can be understood as a sequence of natural causes and effects.
Determinism: Events
and behaviors have causes.
Discoverability:
Through systematic observation, we can discover causes – and work towards more
certain and comprehensive explanations through repeated discoveries.
10.
Research Question: Definition of a theory: A set of
logically consistent statements about some psychological phenomenon that
a)
best summarizes existing
empirical knowledge of the phenomenon
b)
organizes this knowledge in
the form of precise statements of the relationship among variables
c)
provides a tentative
explanation for the phenomenon
d)
serves as a basis for
making predictions about behavior.
11. From theory to actual
research: Definition of a theory:
A set of logically consistent statements about some psychological
phenomenon that
best summarizes existing empirical knowledge of the phenomenon
organizes this knowledge in the form of precise statements of the
relationship among variables
provides a tentative explanation for the phenomenon
serves as a basis for making predictions about behavior.
1.
Basic versus Applied Research
Goal
of describing, predicting, & explaining fundamental principles
of behavior vs. solving real-life problems
2.
Laboratory Research versus Field Research
Research
in controlled laboratories vs. uncontrolled or real-life contexts
3.
Quantitative versus Qualitative Research
Descriptive
& inferential statistics vs. narrative analysis (e.g., case studies,
observational research, interviews):
Variable: Something that the researcher/experimenter can
measure.
Independent
Variable: The variable the experimenter has control over, can
change in some way to see if it has an effect on other variables in the study.
Dependent
Variable: The variable that is measured to see if a change takes place:
Control
Variable: The variable that is not manipulated that serves
as a comparison group from the other variables in the study. This third
variable is used to ensure that the independent variable, when manipulated is
actually having an effect on the dependent variable. For example, if a similar
change occurs in the control variable as the dependent variable, this indicates
that the change may not be the result of the independent variable manipulation
and may be a natural change in the variable.
In a
experiment the researcher manipulates the independent variable to see if it has
an effect on the dependent variable.
Experimental Method: takes a representative
sample from the larger population and attempts to generalize results of
experiment on sample to the population from which it was derived.
Dependent Variable
Independent Variable
For example, police try randomly stopping
motorists and testing for alcohol to see if alcohol-related crashes decline.
Service disruptions
Political campaigns
School shooting
Institution of Wi-Fi in an area
Reliability -
Reliability is the degree to which the findings from a study produce
stable and consistent results. For example, if we looked at the same group of
people in the exact same way at a different time, would be draw the same
conclusions?
Validity - Validity refers to how well does the type of
research we engage in measures what it is purported to measure. (e.g., when we
try to understand the behaviors of North American people in general, are
college students a good group to look at and generalize?)
External Validity - The extent to which a study's results (regardless of
whether the study is descriptive or experimental) can be generalized/applied to
other people or settings reflects its external validity. Typically, group
research employing randomization will initially possess higher external
validity than will studies (e.g., case studies and single-subject experimental
research) that do not use random selection/assignment.
• Historical Events
Research –focuses on one short historical period (1 case, 1 time period)
• Historical Process Research –traces a
sequence of events over a number of years (1 case, many time periods)
• Cross-sectional Comparative Research
comparing data from one time period between two or more nations (many cases, 1
time period)
• Comparative Historical Research –
longitudinal comparative research (many cases)
• Narrative. It researches a story involving specific actors and
other events occurring at the same time (Abbott, 1994:102), or one that takes
account of the position of actors and events in time and in a unique historical
context (Griffin, 1992).
• Inductive. The research develops an explanation for what happened
from the details discovered about the past.
• Case-oriented. It focuses on the nation or
other unit as a whole.
• Holistic. It is concerned with the context
in which events occurred and the interrelations among different events and
processes: “how different conditions or parts fit together” (Ragin,
1987:25–26).
• Conjunctural. This is because, it is
argued, “no cause ever acts except in complex conjunctions with others” (Abbot, 1994:101).
• Temporal. It becomes temporal by taking
into account the related series of events that unfold over time.
Historical Methods
Idiographic Methods
Nomothetic Explanations
Historical events research is research on
past events that does not follow processes for some long period of time—that is
basically crosssectional—is historical events research rather than
historical process research.
Event Structure Analysis is a qualitative
approach that relies on a systematic coding of key events or national
characteristics to identify the underlying structure of action in a chronology
of events.
• Oral History can be useful for
understanding historical events that occurred within the lifetimes of living
individuals.
(Example: an auto manufacturing plant that
produces SUVs closes in your home town).
• Classifying historical information into
discrete events: plant closes, worker strike, corporate buy-out, gas prices
increase.
• Ordering events into a temporal sequence:
1. gas prices increase 2. Corporate buy-out 3. Worker strike 4. Plant closes.
• Identifying prior steps that are prerequisites for subsequent events:
Oil embargo, political
This requires intensive interviewing of
individuals, which is an obtrusive measure. However, there are many oral
histories
archived at university libraries and the
Library of Congress. You might get lucky and find out that oral histories of
community members were conducted during the time of the plant closing. You
could qualitatively analyze the oral histories for information about how people
perceived the plant closing and its importance to the community.
• Case could be anything from
– A small community (village)
- Even looking at event’s across the whole
world!
Example: comparing the perceptions of events taking place in the recent
conflicts between Ukraine and Russia from media reports from both sides.
Longitudinal Research –
Takes places by looking at several cross-sections to gain an understanding of
the evolution of events and how they change over time.
These types of research are
often utilized in comparative historical analysis but are also utilized in
experimental and quasi-experimental research among most of the types of
research covered by this course!
Primary Sources – Collecting data from the
individual source such as being present when an event (such as an important
election) takes place. This is more verifiable because we are seeing the data
for what it is.
Secondary Sources – Collecting data from
others who have already collected the data such as news papers, magazines, and
interviews.
These sources of data are prone to the bias
of the source, therefore the data may be somewhat inaccurate.
Example: Hurst Publications and the Spanish
American War
The Spanish-American War is often referred to
as the first "media war." During the 1890s, journalism that
sensationalized—and sometimes even manufactured—dramatic events was a powerful
force that helped propel the United States into war with Spain. Led by
newspaper owners William Randolph Hearst and Joseph Pulitzer, journalism of the
1890s used melodrama, romance, and hyperbole to sell millions of newspapers--a
style that became known as yellow journalism.
The term yellow journalism came from a
popular New York World comic called "Hogan's Alley," which featured a
yellow-dressed character named the "the yellow kid." Determined to
compete with Pulitzer's World in every way, rival New York Journal owner
William Randolph Hearst copied Pulitzer's sensationalist style and even hired
"Hogan's Alley" artist R.F. Outcault away from the World. In
response, Pulitzer commissioned another cartoonist to create a second yellow
kid. Soon, the sensationalist press of the 1890s became a competition between
the "yellow kids," and the journalistic style was coined "yellow
journalism."
Yellow journals like the New York Journal and
the New York World relied on sensationalist headlines to sell newspapers. William
Randolph Hearst understood that a war with Cuba would not only sell his papers,
but also move him into a position of national prominence. From Cuba, Hearst's
star reporters wrote stories designed to tug at the heartstrings of Americans.
Horrific tales described the situation in Cuba--female prisoners, executions,
valiant rebels fighting, and starving women and children figured in many of the
stories that filled the newspapers. But it was the sinking of the battleship
Maine in Havana Harbor that gave Hearst his big story--war. After the sinking
of the Maine, the Hearst newspapers, with no evidence, unequivocally blamed the
Spanish, and soon U.S. public opinion demanded intervention.
Today, historians point to the
Spanish-American War as the first press-driven war. Although it may be an
exaggeration to claim that Hearst and the other yellow journalists started the
war, it is fair to say that the press fueled the public's passion for war.
Without sensational headlines and stories about Cuban affairs, the mood for
Cuban intervention may have been very different. At the dawn of the twentieth
century, the United States emerged as a world power, and the U.S. press proved
its influence. Adapted from: http://www.pbs.org/crucible/frames/_journalism.html
View Documentary on the Spanish American War:
https://www.youtube.com/watch?v=8g8NpQsmxj4
Yellow Journalism: https://www.youtube.com/watch?v=0wFrAny77UY.
• Documents and other evidence may have been
lost or damaged.
• Available evidence may represent a sample
biased toward more newsworthy figures.
• Written records will be biased toward those
who were more prone to writing.
• Feelings of individuals involved in past
events may be hard, if not impossible, to reconstruct.
When to use the Case Study Method: “First and most important, the
choices among different research methods, including the case study method, can
be determined by the kind of research question that a study is trying to
address (e.g., Shavelson & Towne, 2002, pp. 99–106, SagePub, 2014).”
“Second, by emphasizing the study of a phenomenon within its real-world
context, the case study method favors the collection of data in natural
settings, compared with relying on “derived” data (Bromley, 1986, p. 23,
SagePub, 2014)”
Third, the case study method is now commonly used in conducting
evaluations (SagePub, 2014).
Lack of trust in procedures and processes
Inability to generalize results
When done poorly problems increase.
Define the Case
Select one of 4 types of case study designs
Use theory in design work
1.
Direct observations (e.g., human actions or a physical environment)
2. Interviews (e.g., open-ended conversations
with key participants)
3. Archival records (e.g., student records)
4. Documents (e.g., newspaper articles,
letters and e-mails, reports)
5. Participant-observation (e.g., being
identified as a researcher but also filling a real-life role in the scene being
studied)
6. Physical artifacts (e.g., computer
downloads of employees’ work)
Triangulation
Literature Review
Direct Observation
* You are always better off using multiple
rather than single sources of evidence.
Importantly, the questions in the protocol are directed at the
researcher, not at any field participant. In this sense, the protocol differs
entirely from any instrument used in a conventional interview or survey. The
protocol’s questions in effect serve as a mental framework,
During data collection, the skepticism should involve worrying about
whether events and actions are as they appear to be and whether participants are
giving candid responses.
Having a truly skeptical attitude will result in collecting more data
than if rivals were not a concern.
Ideally, such evidence will come from a formal case study database that
you compile for your files after completing your data collection.
Your analysis can begin by systematically organizing your data
(narratives and words) into hierarchical relationships, matrices, or other
arrays (e.g., Miles & Huberman, 1994).
Pattern-Matching
Open-Ended Questions
Time-Series-Like Analysis
If selecting your case(s) to be studied is
the most critical step in doing case
study research, analyzing your case study
data is probably the most troublesome.
Much of the problem relates to false
expectations: that the data will somehow
“speak for themselves,” or that some counting
or tallying procedure will be sufficient to produce the main findings for a
case study. Wrong. Instead, consider
the following alternatives.
You actually made some key assumptions for
your analysis when you defined
your research questions and your case. Was
your motive in doing the case study
mainly to address your research questions? If
so, then the techniques for analyzing
the data might be directed at those questions
first. Was your motive to derive
more general lessons for which your case(s)
are but examples? If so, your analysis
might be directed at these lessons. Finally,
if your case study was driven by a
discovery motive, you might start your
analysis with what you think you have
discovered.
Now comes a “reverse” lesson. Realizing that
key underlying assumptions for
later analysis may in fact have been implicit
at the initial stages of your case
study, you could have anticipated and planned
the analytic strategies or implications when conducting those initial stages.
Collecting the actual data may lead to changes in this plan, but having an
initial plan that needs to be revised (even drastically) may be better than
having no plan at all.
For instance, one possibility is to stipulate
some pattern of expected findings
at the outset of your case study. A
pattern-matching logic would later enable you
to compare your empirically based pattern
(based on the data you had collected)
with the predicted one. The prediction in a
community study might have stipulated that the patterns of outcomes in many
different economic and social sectors (e.g., retail sales, housing sales,
unemployment, and population turnover) would be “catastrophically” affected by
a key event— the closing of a military base in a small, single-employer town
(Bradshaw, 1999).
The analysis would then examine the data in
each sector, comparing pre-post
trends with those in other communities and
statewide trends. The pattern-matching
results should be accompanied by a detailed
explanation of how and why the base closure had (or had not) affected these
trends. By also collecting data on and then examining possible rival
explanations (e.g., events co-occurring with the key event or other contextual
conditions), support for the claimed results would be
strengthened even further.
Second, a case study may not have started
with any predicted patterns but in fact
may have started with an open-ended research
question that would lead to the use of an explanation-building technique. The
purpose of the case study here would be to build an explanation for the issue
and then again to deliberately entertain rival explanations.
A third technique mimics the time-series
analyses in quantitative research. In
case study research, the simplest time series
can consist of assembling key events
into a chronology. The resulting array (e.g.,
a word table consisting of time and
types of events as the rows and columns) may
not only produce an insightful
descriptive pattern but also may hint at
possible causal relationships, because any
presumed causal condition must precede any
presumed outcome condition. Assuming again the availability of data about rival
hypotheses, such information
would be used in examining the chronological
pattern. When the rivals do not fit
the pattern, their rejection considerably
strengthens the basis for supporting your
original claims.
If the case study included some major
intervening event in the midst of the
chronological sequence, the array could serve
as a counterpart to an interrupted
time series in experimental research. For
instance, imagine a case study in which
a new executive assumed leadership over an
organization. The case study might
have tracked the production, sales, and
profit trends before and after the executive’s ascendance. If all the trends
were in the appropriate upward direction, the case study could begin to build a
claim, crediting the new leader with these
accomplishments. Again, attending to rival
conditions (such as that earlier policies
might have been put into place by the new
executive’s predecessor) and
making them part of the analysis would
further strengthen the claim.
The replication or corroboratory frameworks can vary. In a direct
replication,the single cases would be predicted to arrive at similar results.
In summary, to the extent that any study concerns itself with generalizing,
case studies tend to generalize to other situations (on the basis of analytic
claims), whereas surveys and other quantitative methods tend to generalize to
populations (on the basis of statistical claims).
Quantitative Research: This type of research is charged with
quantifying (measuring and counting) and subscribes to to a particular
empirical approach to knowledge, believing that by measuring accurately enough we
can make claims about the object at study.
Qualitative Research: This type of research is charged with the
quality or qualities of an experience or phenomenon. Qualitative research
rejects the notion of their being a simple relationship between our perception
of the world and the world itself, instead arguing that each individual places
different meaning on different events or
experiences and that these are constantly changing. Qualitative research
generally gathers text based data through exercises with a small number of
participants, usually semi structured or unstructured interviews.
1) When we are looking for a
numerical answer
2) When we want to study numerical change
3) When we want to find out about the state of something or to explain a
phenomena
4) When we want to test a hypothesis
ü Concise
ü Accurate
ü Strictly Controlled
ü Replicable
ü Can indicate causation
ü Ideally is objective
ü Quantitative Disadvantages:
ü Limited understanding of individuality
ü Groups people into categories
ü Can be accused of oversimplifying human
nature
1. The first situation where quantitative
research will fail is when we want
to explore a problem in depth. Quantitative
research is good at providing
information in breadth, from a large number
of units, but when we
want to explore a problem or concept in
depth, quantitative methods
can be too shallow. To really get under the
skin of a phenomenon, we
will need to go for ethnographic methods,
interviews, in-depth case
studies and other qualitative techniques.
2. We saw above that quantitative research is
well suited for the testing of
theories and hypotheses. What quantitative
methods cannot do very
well is develop hypotheses and theories. The
hypotheses to be tested may
come from a review of the literature or
theory, but can also be developed
by using exploratory qualitative research.
3. If the issues to be studied are
particularly complex, an in-depth qualitative
study (a case study, for example) is more
likely to pick up on this
than a quantitative study. This is partly
because there is a limit to how
many variables can be looked at in any one
quantitative study, and partly
because in quantitative research the
researcher defines the variables to be
studied herself, while in qualitative
research unexpected variables may
emerge.
4. Finally, while quantitative methods are
best for looking at cause and
effect (causality, as it is known),
qualitative methods are more suited to
looking at the meaning of particular events or
circumstances.
“Embraces “intersubjectivity” or how people
may construct meaning…”
Focus on the individual and their real lived
experience.
Qualitative Advantages:
Appreciates research
participant’s individuality
Provides insider view of
research question
Less structured than
quantitative approach
Qualitative Disadvantages:
Not always appropriate to
generalize results to larger population
Time consuming
Difficult to test a
hypothesis
Content and Thematic Analysis
Grounded Theory (Generating Theory from Data)
Discourse and Narrative Analysis Individual Case Studies
Mixed-Methods Designs - questionnaire) and qualitative (for example, a number of
case studies) methods. Mixed-methods research is a flexible approach, where the
research design is determined by what we want to find out rather than by any
predetermined epistemological position. In mixed-methods research, qualitative
or quantitative components can predominate, or both can have equal status.
http://www.sagepub.com/upm-data/3869_muij.pdf
Rectangular Distribution
Bimodal Distribution
Normal Distribution
Null Hypothesis
Alternative Hypothesis
Alpha level
Therefore, internal
validity refers to how well a piece of research allows you to choose among
alternate explanations of something. A research study with high internal
validity lets you choose one explanation over another with a lot of confidence, because
it avoids (many possible) confounds.
“The differential loss of individuals from
treatment and/or comparison groups. This is often a problem when research
participants are volunteers. Volunteers may drop out of the study if they find
it is consuming too much of their time. Others may drop out if they find the
task to be too arduous.” (Source: Internet presentation)
It
also can be thought of as“…refer[ing] to the drop out of subjects in an
experiment…if people drop out of an experiment they do so for a reason…[perhaps
indicates] a problem with either a noxious or an inadequate independent
variable” (Kroth, 1988, p. 60).
The best way to account for this is to
randomly assign participants to two groups, one treatment group & one
control group.
Researchers can expect similar effects in treatment group versus control
group over time.
The impact of “taking a test, and whether or
not any special circumstances existed which may have influenced sores on a
subsequent test…taking a test may cause anxiety, stress or self-consciousness
the first time one takes it, but such emotions may be absent at posttest time
[causing an improvement that is not simply due to the treatment, or IV]”
(Kroth, 1988, p. 60).
Randomly assign participants to two groups,
one treatment group & one control group.
Researchers can expect similar effects in
treatment group versus control group over time.
“Many change-producing events may have
occurred between Observation 1 and Observation 2 “ (Source: Internet
Presentation)
“History is more …[likely] the longer the
lapse between the pretest and posttest.” (Source: Alliant International University
- Internet Presentation)
This was described best by Kroth, 1988 who
stated:
“Things happen between a pretest and posttest,
especially when there may be 6 months to a year between the two…These …events,
or outside influences [confounding variables] may cause changes we see between
pretest time and posttest time may be due to these outside factors, not the
independent variable” (Kroth, 1988, p. 60)
Consistent methods / measurements in
longitudinal studies from one time point to the next.
“Changes
in the way a test or other measuring instrument is calibrated that could
account for results of a research study (different forms of a test can have
different levels of difficulty). This threat typically arises from
unreliability in the measuring instrument. Can also be present when using
observers.” (Source: Internet Presentation)
Another possibility is whether the instrument
tends to…[vary] all by itself, registering…a spurious increase or decrease
which is unrelated to what the good old independent variable is trying to do”
(Kroth, 1988, p. 60).
Randomly assign participants to two groups,
one treatment group & one control group.
Researchers can expect similar effects in
treatment group versus control group over time.
Or, use tests rather than observation,
ratings, or interviews.
“This
can occur when intact groups are compared. The groups may have been different to
begin with. If three different classrooms are each exposed to a different
intervention, the classroom performances may differ only because the groups
were different to begin with.” (Source: Internet Presentation)
“…the biases which result from comparing one
group of complex and unique people to another group of complex and unique
subjects” (Kroth, 1988, p. 60).
Randomly assign participants to two groups,
one treatment group & one control group.
Researchers can expect similar effects in
treatment group versus control group over time.
“Changes
in physical, intellectual, or emotional characteristics, that occur naturally
over time, that influence the results of a research study.” (Source: Internet
Presentation)
“Internal
changes occurring between the pretest … and posttest… which can cause changes
in the dependent variable. These are internal changes, like becoming tired or
heavier or weaker, less anxious, or more inattentive with the passage of time.”
(Kroth, 1988, p. 60)
Randomly assign participants to two groups,
one treatment group & one control group.
Researchers can expect similar effects in
treatment group versus control group over time.
Isolating groups.
Blind studies.
Special Treatment Threat -- When groups are
treated differently as a result of being in the study – or one group is treated
differently from another group b/c of their treatment status.
Special
Treatment Threat: How to fix it?
“Occurs
when individuals are selected for an intervention or treatment on the basis of
extreme scores on a pretest. Extreme scores are more likely to reflect larger
(positive or negative) errors in measurement (chance factors). Such extreme
measurement errors are NOT likely to occur on a second testing.” (Source:
Internet Presentation)
This
“refers to the fact that a group at the extreme ends of a normal curve has a
greater probability of being “closer” to the mean of that curve the next time
the group is measured” (Kroth, 1988, p. 60).
The problem is with single assessments. Two
randomly assigned groups should equally regress toward the mean.
Randomly assign participants to two groups,
one treatment group & one control group.
Researchers can expect similar effects in
treatment group versus control group over time.
Also known as: Additive and interactive
effects of threats to internal validity.
--
When certain participants (selection)
experience drop out, historical effects, testing issues as a group.
One research scholar, Vanbeek states: “No
threat is completely isolated, often one threat can lead to another or enhance
another, often in vicious cycles.”
Randomly assign participants to two groups,
one treatment group & one control group.
Researchers can expect similar effects in
treatment group versus control group over time
Unless the temporal order is clear, the
directionality is difficult to determine.
Be clear in statements of causality and
describe when causality is unclear.
Threats to External Validity
This is when the results
found are not generalizable to the population of interest.
You may see an effect, but
its not the effect you wanted to measure or it doesn’t
generalize outside of the small group you are studying!
Example:
An article entitled “Depression among Asian Americans” studies depression, but
the participants were only from Japan and China, and were upper-middle class.
This is a problem because researchers are trying to find out information about
Asian Americans in general, but only used Japanese and Chinese upper SES
people. The results are not generalizable to all Asian Americans, and
depression may work differently with other types of Asians and Asians of other
SES’s. This WOULD NOT be a problem,
however, if the article was called “Depression among wealthy Japanese and
Chinese Americans”, since researchers then don’t intend for the results to be
generalized to all Asian Americans.
Risk: EXTREMELY HIGH. This
happens ALL THE TIME in studies.
Look in: PARTICIPANTS to see who
was used for the study. Then look at the title, abstract, and/or discussion to
make sure you know who they are trying to generalize the results to and talk
about.
Example: Researchers test a manualized CBT treatment. This CBT treatment
is meant to be 12 weeks, but researchers did a 7 week version of it, so the
results may be different. Effects found with this treatment are not necessarily
the same at results that would have been found with the treatment they were
trying to test (the 12 week treatment). OR Researchers combined CBT with
another treatment, which may find different results. OR They only used part of
the treatment, such as the Cognitive portion of the treatment.
Risk: Low. Researches would
likely admit that they administered a treatment differently than it was
supposed to be administered.
Look in: Methods, measures,
and Limitations
Example: Unintelligent
researchers wanted to measure if taking Prozac effects basketball players’
performance. They give them the drug, then measure the percentage of shots they
make during a game, and compare this to their old shot percentage. The effects
found may have been different if they had measured a different outcome than
shot percentage. If they had included assists, blocks, passes, coaches ratings
of them, ect, the different results may have been found about the players
performance.
Risk: Medium. Look for just one DV with just one measure
that could have been measured differently. (If the one outcome was height and
they used a ruler, do NOT apply this threat, because there is no other way to
measure height and get different results).
Look in: Methods
Example:
Researchers wanted to test children’s reactions to their parents instructions.
They tested this in a room at a local elementary school, asking parents to tell
their kids to complete certain tasks. The kids were super compliant. HOWEVER,
the setting here was a school, where kids are accustomed to being compliant. If
the study were done at children’s homes, different results may have been found.
You cannot generalize this to how children’s reaction to their parents
instructions in general, only how they react to them at a school.
Risk:
HIGH. Only 1 setting is used in most studies.
Look
in: Methods in Procedure
Example: Suppose a study used only males to
study depression. They found that owning more video games makes them super
excited and causes sleeplessness, which later causes depression in these
people. So, sleeplessness mediates the relationship between video games and
depression. However, this may not be the case if females were used. Owning
video games may bore them, which may make them depressed, but it doesn’t cause
sleeplessness because they aren’t excited about the games. So the video games
cause depression for them, but this isn’t mediated by sleeplessness. Or if different video games were used, then
sleeplessness may not mediate the relationship the same way.
Risk: RARE. The study must have a mediating variable in it.
Look in: Methods, Results, and Conclusions
Example: Researchers in the 40’s found that Japanese-Americans felt very
low allegiance to the USA, and reported that their ethnic identity caused them
great distress. These results were gathered during World War II, when
Japanese-Americans were hated and oppressed by many people in the US in an
extreme way. These results do not generalize to modern day as much. These
results do not hold in the same way they once did, and Japanese-Americans may
feel very differently about their allegiance to the USA, or about their
distress over ethnic identity.
RELIABILITY
Reliability
refers to the consistency of a measure. A measure is considered reliable if we
get the same result repeatedly. A research method is considered reliable if we
can repeat it and get the same results.
Coolican
(1994) pointed out
“Any
measure we use in life should be reliable, otherwise it’s useless. You wouldn’t
want your car speedometer or a thermometer to give you different readings for
the same values of different occasions. This applies to psychological measures
as much as any other.”
A ruler for
example would be reliable, as the results could be replicated time after time and the same results would be gained (consistency). If you measure the length
of a book on Monday, and your ruler tells you its 25 cm long, it will still
tell you its 25cm long on Friday.
An
IQ test however may be unreliable, if a person sits the test on Monday
and scores 140, and then sits the same test on Friday and scores 90. Even
though it can be replicated, it
shows low consistency and therefore
is an unreliable test.
Some
research methods (such as laboratory studies) have high reliability as they can
be replicated and the results checked for consistency. Other research methods
however (such as case studies and interviews) have lower reliability as they
are difficult or impossible to replicate. As they cannot be replicated, we
cannot check how consistent the results are.
We will look
in more detail of the specific reliability of various research methods throughout
the course.
A
study may be high in reliability, but the results may still be meaningless if
we don’t have validity. Validity is the extent to which a test measures what it
claims to measure.
There are
three main aspects of validity that we investigate in psychological research Control, Realism and
Generalisability.(p138)
Control
Realism
The
whole point of psychological research is to provide information about how
people behave in real life. If an experiment is too controlled, or the
situation too artificial, participants may act differently than they would in
real life. Therefore, the results may lack validity.
Journal of Nervous & Mental Disease:
Original Article: PDF Only
http://allpsych.com/researchmethods/researcherror/
Dissociation is a lack of the normal integration of thoughts, feelings, and experiences into the stream of consciousness and memory. Dissociation occurs to some degree in normal individuals and is thought to be more prevalent in persons with major mental illnesses. The Dissociative Experiences Scale (DES) has been developed to offer a means of reliably measuring dissociation in normal and clinical populations. Scale items were developed using clinical data and interviews, scales involving memory loss, and consultations with experts in dissociation. Pilot testing was performed to refine the wording and format of the scale. The scale is a 28-item self-report questionnaire. Subjects were asked to make slashes on 100-mm lines to indicate where they fall on a continuum for each question. In addition, demographic information (age, sex, occupation, and level of education) was collected so that the connection between these variables and scale scores could be examined. The mean of all item scores ranges from 0 to 100 and is called the DES score. The scale was administered to between 10 and 39 subjects in each of the following populations: normal adults, late adolescent college students, and persons suffering from alcoholism, agoraphobia, phobicanxious disorders, posttraumatic stress disorder, schizophrenia, and multiple personality disorder. Reliability testing of the scale showed that the scale had good test-retest and good split-half reliability. Item-scale score correlations were all significant, indicating good internal consistency and construct validity. A Kruskal-Wallis test and post hoc comparisons of the scores of the eight populations provided evidence of the scale's criterion-referenced validity. The scale was able to distinguish between subjects with a dissociative disorder (multiple personality) and all other subjects.
Dissociation is a lack of the normal integration of thoughts, feelings, and experiences into the stream of consciousness and memory. Dissociation occurs to some degree in normal individuals and is thought to be more prevalent in persons with major mental illnesses. The Dissociative Experiences Scale (DES) has been developed to offer a means of reliably measuring dissociation in normal and clinical populations. Scale items were developed using clinical data and interviews, scales involving memory loss, and consultations with experts in dissociation. Pilot testing was performed to refine the wording and format of the scale. The scale is a 28-item self-report questionnaire. Subjects were asked to make slashes on 100-mm lines to indicate where they fall on a continuum for each question. In addition, demographic information (age, sex, occupation, and level of education) was collected so that the connection between these variables and scale scores could be examined. The mean of all item scores ranges from 0 to 100 and is called the DES score. The scale was administered to between 10 and 39 subjects in each of the following populations: normal adults, late adolescent college students, and persons suffering from alcoholism, agoraphobia, phobicanxious disorders, posttraumatic stress disorder, schizophrenia, and multiple personality disorder. Reliability testing of the scale showed that the scale had good test-retest and good split-half reliability. Item-scale score correlations were all significant, indicating good internal consistency and construct validity. A Kruskal-Wallis test and post hoc comparisons of the scores of the eight populations provided evidence of the scale's criterion-referenced validity. The scale was able to distinguish between subjects with a dissociative disorder (multiple personality) and all other subjects.
(C) Williams & Wilkins 1986. All Rights Reserved.
http://www.duggans-serra.com/mobile/obit.php?id=1463998&name=Joseph-William-Waters&loca=Daly-City-CA
According to the lectures, and the book. I learned that, in the social sciences there is no best kind of research. I think researchers probably use several methods in order to conduct research. Empirical, all information is based on observation. Objectivity, Observations is verified by others. Systematic, observations are made in a step-by-step fashion. Controlled, potentially confusing factors are eliminated. Public, built on previous research, open to critique and replication, building towards theories.
It dependents what king of research or for what purpose you are researching. For example in social science is the science of people or collections of people, such as groups, firms, societies, or economies, and their individual or collective behaviors. It can be classified into disciplines such s psychology, sociology, and economics. The society very much is more for the “collective”. I think, using the scientific method is imperative for any kind of research. .
When we consider the advantages and disadvantages of laboratory vs. field research, are there any others that come to mind that were not outlined in lecture?
A) Field Research/Ethnography: Participant observation is based on living among the people under study for a period of time, could be months or maybe years, and gathering data through continuous involvement in their lives and activities. The ethnographer begins systematic observation and keeps notes, in which the significant events of each day are recorded along with informants and interpretations. These demands are met through two major research techniques participant observation and key informant interviewing. An example would be the one on the video that Maria has been spending several months with Steve a drug user, and the ethical problem come now, the participant do not realize that their behavior is being observed. Obviously (there is no consent) cannot give voluntary informed consent to be involved in the study. Steve confesses that he is HIV positive and his partner does not know, there is a confidentiality issue.
2. Are there some things we can do in the field that we just cannot do in the lab and vise-versa?
A) I learned that clear advantage of laboratory experiments over field experiments is that it is much easier to obtain large amounts of very detailed information from participants in the laboratory. An important reason why laboratory experiments are more artificial than field experiments is because the participants in laboratory experiments are aware that their behavior. One of the advantages of field experiments over laboratory experiments is that the behavior of the participants is often more of their normal behavior. The greatest advantage of field experiments over laboratory experiments is that they are less artificial
3. What are your ideas as researchers-in-training for accounting for the disadvantages of each and what problems might you foresee arising with your idea?
A) I learned that, the method of investigation used most often by psychologists is the experimental method. Some of the advantages of the experimental method are common to both laboratory and field experiments. I would have to know reliability and validity and field vs. laboratory research. To avoid any confounding variables. These are variables that are manipulated/allowed to vary systematically along with the independent variable. The presence of any confounding variables can destroy the experiment, because it prevents from being able to interpret our findings.Question: “History is created by the victor.” Do we believe that comparative historical research gives us a good grasp of what actually happened in the past?
I do not think so. Because, according to the lecture, the comparative-historical analysis has four main defining elements. Comparative-historical analysis is also defined by epistemology. Specifically, comparative-historical works pursue social scientific insight and therefore accept the possibility of gaining insight through comparative-historical plus other methods. Finally, the unit of analysis is a defining element, with comparative-historical analysis focusing on more aggregate social units.
Four main elements:
Historical Events Research focuses on one short historical period (1 case, 1 time period)
Historical Process Research –traces a sequence of events over a number of years (1 case, many time periods)
Cross-sectional Comparative Research -- comparing data from one time period between two or more nations (many cases, 1 time period)
Comparative Historical Research – longitudinal comparative research (many cases) over a prolonged period of time. Comparative and Historical Research by number of cases and length of time studied.
What are some of the benefits and negatives of the Case Study method. When compared to other types of research reviewed in the course thus far? Can you think of some specific examples where the case study method might be preferable?
The Case Study Method, Case study is a” Strategy for doing research which involves an empirical investigation of a particular contemporary phenomenon within its real life context using multiple sources of evidence” (Robson, 1993, p. 146). Case studies are in-depth investigations of a single person, group, event or community. Depending on the case study it says whether the research is field or laboratory research, we always loose valuable information about individual variation when we try to collect the information of the experiences, emotions, and behaviors into common experiences that we can measure numerically and generalize across a population. If I understand correctly the lecture, we cannot generalize from a Case Study, must be used three things: Descriptive, Exploratory, and Explanatory.
Consider generalizing the results of research done on a small sample to the general population.
I think I need to consider the Type of design chosen: Questions the conditions under which the findings be generalized deals with the ability to generalize the findings outside the study to other populations and environments. Purpose of Research Design: Provides the plan or blueprint for testing research questions and hypotheses. Involves structure and strategy to maintain control and intervention fidelity. Accuracy: Accomplished through the theoretical framework and literature review. All aspects of the study systematically and logically follow form the research questions. Time: Is there enough time for completion of the study. Control: Achieved with steps taken by the searcher to hold the conditions of the study uniform and avoid or decrease the effect of intervening, extraneous, or mediating variables of the dependent variable or outcome. Ensures that every subject receiving the intervention of treatment receive the identical intervention or treatment.
what are some of the benefits and negatives of qualitative and quantitative research?
Variables that occur during the study that interfere with or influence the relationship between the independent and dependent variables.
Intervening and mediating variables are processes that occur during the study.
Objectivity can be achieved form a thorough review of the literature and the development of a theoretical framework.
Instrumentation: changes in equipment used to make measurements or changes in observational techniques may cause measurements to vary between participants related to treatment fidelity.
Controlling Extraneous Variables Using a homogeneous sample
Using consistent data-collection procedures- constancy. A homogeneous sample is one in which the researcher chooses participants who are alike – for example, participants who belong to the same subculture or have similar characteristics. Homogeneous sampling can be of particular use for conducting focus groups because individuals are generally more comfortable sharing their thoughts and ideas with other individuals who they perceive to be similar to them. Patton, M. (2001). Qualitative Research & Evaluation Methods.
when thinking of this, Could one said to be superior to the other, or are they context specific?
Consider generalizing the results, according to the lecture, the independent variable is: the variable that the researcher hypothesizes will have an effect on the dependent variable Usually manipulated (experimental study) The independent variable is: manipulated by means of a program, treatment, or intervention done to only one group in the study (experimental group ) The control group gets the standard treatment or no treatment.
The dependent variable is a factor, trait, or condition that can exist in differing amounts or types. Not manipulated and pressured to vary with changes in the independent variable The variable the researcher is interested in explaining. Randomization Each subject in the study has an equal chance of being assigned to the control group or the experimental group.
Assumes that any important intervening, extraneous, or mediating variable will be equally distributed between the groups, minimizing variance and decreasing selection bias.
Testing: Taking the same test more than once can influence the participant’s responses the next time test is taken.
I understand that: There are two reasons for error that can result in a sample that is different from the population from which it is derived.
These are: The sampling error - chance, random sample bias mistake - constant error due to inadequate design.
Inferential statistics take into account the sampling error.
These statistics skew the sample is not correct. That's a problem of research design.
Inferential statistics refer only to random error. Value P
The reason for calculating an inferential statistics is to get a value of p (p = probability). The p value is the probability that the samples are from the same population with respect to the dependent variable (outcome).
Typically, we are testing the hypothesis samples (groups) differ in the result.
The value of p is directly related to the null hypothesis. The p value determines if the null hypothesis is rejected. It is used to estimate if we think that the null hypothesis is true. The p value provides an estimate of how often we would get the result by chance, if in fact the null hypothesis were true. If the p value is small, reject the null hypothesis and accept that the samples are really different in the result. If the p value is large, I accept the null hypothesis and conclude that the treatment or prediction variable had no effect on the outcome.
Do we believe the peer reviewed publication process outlined in the posted videos is sufficient for the general public to ascertain and the results of such research?
Let's see if I get this:
It is very important to follow these process outlined in the videos. However it does not guarantee they will accept your paper (the journal ). According to the videos, do have peer review before sending the paper to a journal, however is important to match journal and paper, make sure you count the word that is accepted by this publication/journal etc, following all the citations the videos are advising which is making me tired and disappointed just by thinking the process.
If is minor revision you should be positive because that means change all of what is advise and may be published. If is major changes, that makes me think do not even bother because you have to rewrite your hard work or star over your paper. Yes, I believe the peer reviewed publication process outlined in the posted videos helps. I am not sure if it is sufficient.....
What are some of the reasons why the constructs of reliability and validity are important to measure?
Reliability: refers to the consistency of a measure. Validity: is the extent to which a test measures what it claims to measure. There is Three main features that need to be included to measure are: Control: refers to how well the experimenter controlled the experiment. The control is important because without control, researchers cannot establish cause and effect. Realism: is where psychological research is provided information about how people in the real world behave. If an experiment is too controlled, too artificial or situation, participants can act differently than they would in real life. Generalizability: is the primary objective of psychological research is producing results that can be generalized beyond the experiment setup.
How do we use the words reliability and validity in everyday life? What do these words mean? Is there a difference between them or do they mean the same thing?
Reliability and Validity
How do we use the words reliability and validity in everyday life? What do these words mean? Is there a difference between them or do they mean the same thing?
RELIABILITY
Reliability refers to the consistency of a measure. A measure is considered reliable if we get the same result repeatedly. A research method is considered reliable if we can repeat it and get the same results.
Coolican (1994) pointed out
“Any measure we use in life should be reliable, otherwise it’s useless. You wouldn’t want your car speedometer or a thermometer to give you different readings for the same values of different occasions. This applies to psychological measures as much as any other.”
A ruler for example would be reliable, as the results could be replicated time after time and the same results would be gained (consistency). If you measure the length of a book on Monday, and your ruler tells you its 25 cm long, it will still tell you its 25cm long on Friday.
An IQ test however may be unreliable, if a person sits the test on Monday and scores 140, and then sits the same test on Friday and scores 90. Even though it can be replicated, it shows low consistency and therefore is an unreliable test.
Some research methods (such as laboratory studies) have high reliability as they can be replicated and the results checked for consistency. Other research methods however (such as case studies and interviews) have lower reliability as they are difficult or impossible to replicate. As they cannot be replicated, we cannot check how consistent the results are.
A study may be high in reliability, but the results may still be meaningless if we don’t have validity. Validity is the extent to which a test measures what it claims to measure.
There are three main aspects of validity that we investigate in psychological research Control, Realism and Generalisability.(p138)
Control
This refers to how well the experimenter has controlled the experimental situation. Control is important as without it, researchers can not establish cause and effect relationships. In other words, without control, we cannot state that it was the independent variable (IV) which caused the change in the dependant variable (DV). The result could have been caused by another variable, called an extraneous variable (EV). These are variables which have not been controlled by the experimenter, and which may affect the DV (see below).
Realism
The whole point of psychological research is to provide information about how people behave in real life. If an experiment is too controlled, or the situation too artificial, participants may act differently than they would in real life. Therefore, the results may lack validity.
T
Generalisability
The aim of psychological research is to produce results which can then be generalised beyond the setting of the experiment. If an experiment is lacking in realism we will be unable to generalise. However, even if an experiment is high in realism, we still may not be able to generalise.
For example, the participants may be all from a small group of similar people, meaning low population validity. Many experiments use white, middle class American college students as participants. What issues with generalisability can you think of?
Lecture 1: Distinction between empirical vs. non-empirical ways of knowing
(UN) SCIENTIFIC THINKING
Authority
Because someone told us that something is true.
2. Reasoning
A priori method (proposed by Charles Peirce): a person develops a belief by reasoning, listening to others’ reasoning, and drawing on previous intellectual knowledge – not based on experience or direct observation.
3. Experience
Empiricism:
Illusory Correlation
Definition: thinking that one has observed an association between events that
(a) doesn’t exist,
Confirmation Bias –
Experience based errors in thinking
Availability Heuristic - An availability heuristic is a mental shortcut that relies on immediate examples that come to mind.
As a result, you might judge that those events are more frequent and possible than others and tend to overestimate the probability and likelihood of similar things happening in the future.
So how do we do it right?
So given all of these errors in thinking and understanding, how could we ever hope to accurately observe?
4. Scientific Method:
Ask a Question
Do Background Research
Construct a Hypothesis
Test Your Hypothesis by Doing an Experiment
Analyze Your Data and Draw a Conclusion
Communicate Your Results
CRITERIA FOR SCIENTIFIC METHOD:
Empirical: All information is based on observation.
Objectivity: Observations are verified by others.
Systematic: Observations are made in a step-by-step fashion.
Controlled: Potentially confusing factors are eliminated.
Public: Built on previous research, open to critique and replication, building towards theories
Lawful: Every event can be understood as a sequence of natural causes and effects.
Definition of a theory:
organizes this knowledge in the form of precise statements of the relationship among variables
provides a tentative explanation for the phenomenon
serves as a basis for making predictions about behavior.
From theory to actual research
Relationship between theory and data
Social Science Research Methods
What is a Research Method Anyway?
Method: A technique used to analyze data. Commonly, a method is aligned with a particular strategy for gathering data, as particular methods commonly require particular types of data. “Method” is therefore commonly used to refer to strategies for both analyzing and gathering data.
Methodology: A body of practices, procedures, and rules used by researchers to offer insight into the workings of the world.
Insight: Evidence contributing to an understanding of a case or set of cases. Comparative-historical researchers are generally most concerned with causal insight, or insight into causal processes.
Comparative-historical analysis: A prominent research tradition in the social sciences, especially in political science and sociology. Works within this research tradition use comparative-historical methods, pursue causal explanations, and analyze units of analysis at the meso- or macro-level.
Epistemology: A branch of philosophy that considers the possibility of knowledge and understanding. Within the social sciences, epistemological debates commonly focus on the possibility of gaining insight into the causes of social phenomena.
Positivism: An epistemological approach that was popular among most of the founding figures of the social sciences. It claims that the scientific method is the best way to gain insight into our world. Within the social sciences, positivism suggests that scientific methods can be used to analyze social relations in order to gain knowledge...
Ethnographic methods: A type of social scientific method that gains insight into social relations through participant observation, interviews, and the analysis of art, texts, and oral histories. It is commonly used to analyze culture and is the most common method of anthropology.
Case Study (Within-case methods): A category of methods used in the social sciences that offer insight into the determinants of a particular phenomenon for a particular case. For this, they analyze the processes and characteristics of the case.
Ideographic explanation: Causal explanations that explore the causes of a particular case. Such explanations are not meant to apply to a larger set of cases and commonly focus on the particularities of the case under analysis.
Comparative methods: Diverse methods used in the social sciences that offer insight through cross-case comparison. For this, they com- pare the characteristics of different cases and highlight similarities and differences between them. Comparative methods are usually used to explore causes that are common among a set of cases. They are commonly used in all social scientific disciplines.
Statistical methods: The most common subtype of comparative methods. It operationalizes variables for several cases, compares the cases to explore relationships between the variables, and uses probability theory to estimate causal effects or risks. Within the social sciences, statistics uses natural variation to approximate experimental methods. There are diverse subtypes of statistical methods.
Experimental methods: The most powerful method used in the social sciences, albeit the most difficult to use. It manipulates individuals in a particular way (the treatment) and explores the impact of this treat- ment. It offers powerful insight by controlling the environment, thereby allowing researchers to isolate the impact of the treatment.
1. Basic versus Applied Research
Goal of describing, predicting, & explaining fundamental principles
of behavior vs. solving real-life problems
Research in controlled laboratories vs. uncontrolled or real-life contexts
Descriptive & inferential statistics vs. narrative analysis (e.g., case studies, observational research, interviews)
Variable: Something that the researcher/experimenter can measure.
Independent Variable: The variable the experimenter has control over, can change in some way to see if it has an effect on other variables in the study.
Dependent Variable: The variable that is measured to see if a change takes place:
Control Variable: The variable that is not manipulated that serves as a comparison group from the other variables in the study. This third variable is used to ensure that the independent variable, when manipulated is actually having an effect on the dependent variable. For example, if a similar change occurs in the control variable as the dependent variable, this indicates that the change may not be the result of the independent variable manipulation and may be a natural change in the variable.
(UN) SCIENTIFIC THINKING
Authority
Because someone told us that something is true.
2. Reasoning
A priori method (proposed by Charles Peirce): a person develops a belief by reasoning, listening to others’ reasoning, and drawing on previous intellectual knowledge – not based on experience or direct observation.
3. Experience
Empiricism:
Illusory Correlation
Definition: thinking that one has observed an association between events that
(a) doesn’t exist,
Confirmation Bias –
Experience based errors in thinking
Availability Heuristic - An availability heuristic is a mental shortcut that relies on immediate examples that come to mind.
As a result, you might judge that those events are more frequent and possible than others and tend to overestimate the probability and likelihood of similar things happening in the future.
So how do we do it right?
So given all of these errors in thinking and understanding, how could we ever hope to accurately observe?
4. Scientific Method:
Ask a Question
Do Background Research
Construct a Hypothesis
Test Your Hypothesis by Doing an Experiment
Analyze Your Data and Draw a Conclusion
Communicate Your Results
CRITERIA FOR SCIENTIFIC METHOD:
Empirical: All information is based on observation.
Objectivity: Observations are verified by others.
Systematic: Observations are made in a step-by-step fashion.
Controlled: Potentially confusing factors are eliminated.
Public: Built on previous research, open to critique and replication, building towards theories
Lawful: Every event can be understood as a sequence of natural causes and effects.
Definition of a theory:
organizes this knowledge in the form of precise statements of the relationship among variables
provides a tentative explanation for the phenomenon
serves as a basis for making predictions about behavior.
From theory to actual research
Relationship between theory and data
Social Science Research Methods
What is a Research Method Anyway?
Method: A technique used to analyze data. Commonly, a method is aligned with a particular strategy for gathering data, as particular methods commonly require particular types of data. “Method” is therefore commonly used to refer to strategies for both analyzing and gathering data.
Methodology: A body of practices, procedures, and rules used by researchers to offer insight into the workings of the world.
Insight: Evidence contributing to an understanding of a case or set of cases. Comparative-historical researchers are generally most concerned with causal insight, or insight into causal processes.
Comparative-historical analysis: A prominent research tradition in the social sciences, especially in political science and sociology. Works within this research tradition use comparative-historical methods, pursue causal explanations, and analyze units of analysis at the meso- or macro-level.
Epistemology: A branch of philosophy that considers the possibility of knowledge and understanding. Within the social sciences, epistemological debates commonly focus on the possibility of gaining insight into the causes of social phenomena.
Positivism: An epistemological approach that was popular among most of the founding figures of the social sciences. It claims that the scientific method is the best way to gain insight into our world. Within the social sciences, positivism suggests that scientific methods can be used to analyze social relations in order to gain knowledge...
Ethnographic methods: A type of social scientific method that gains insight into social relations through participant observation, interviews, and the analysis of art, texts, and oral histories. It is commonly used to analyze culture and is the most common method of anthropology.
Case Study (Within-case methods): A category of methods used in the social sciences that offer insight into the determinants of a particular phenomenon for a particular case. For this, they analyze the processes and characteristics of the case.
Ideographic explanation: Causal explanations that explore the causes of a particular case. Such explanations are not meant to apply to a larger set of cases and commonly focus on the particularities of the case under analysis.
Comparative methods: Diverse methods used in the social sciences that offer insight through cross-case comparison. For this, they com- pare the characteristics of different cases and highlight similarities and differences between them. Comparative methods are usually used to explore causes that are common among a set of cases. They are commonly used in all social scientific disciplines.
Statistical methods: The most common subtype of comparative methods. It operationalizes variables for several cases, compares the cases to explore relationships between the variables, and uses probability theory to estimate causal effects or risks. Within the social sciences, statistics uses natural variation to approximate experimental methods. There are diverse subtypes of statistical methods.
Experimental methods: The most powerful method used in the social sciences, albeit the most difficult to use. It manipulates individuals in a particular way (the treatment) and explores the impact of this treat- ment. It offers powerful insight by controlling the environment, thereby allowing researchers to isolate the impact of the treatment.
1. Basic versus Applied Research
Goal of describing, predicting, & explaining fundamental principles
of behavior vs. solving real-life problems
Research in controlled laboratories vs. uncontrolled or real-life contexts
Descriptive & inferential statistics vs. narrative analysis (e.g., case studies, observational research, interviews)
Variable: Something that the researcher/experimenter can measure.
Independent Variable: The variable the experimenter has control over, can change in some way to see if it has an effect on other variables in the study.
Dependent Variable: The variable that is measured to see if a change takes place:
Control Variable: The variable that is not manipulated that serves as a comparison group from the other variables in the study. This third variable is used to ensure that the independent variable, when manipulated is actually having an effect on the dependent variable. For example, if a similar change occurs in the control variable as the dependent variable, this indicates that the change may not be the result of the independent variable manipulation and may be a natural change in the variable.
Replication even on small scale over time over sample of a study.
Threats to Construct Validitymost harmful or the most likely to occur in psychological and/or sociological research?
Threats to Construct Validity : Inadequate Pre-operational Explication of constructions. We didn’t clearly define things before we started. Mono-operational Bias. We use a single measure of a construct that is not complete. Mono-method Bias. Using only one approach to measuring a construct. We only use surveys to assess employee engagement they are self-report and subject to bias as self-report. Hypothesis Guessing. Participants try to guess what we are looking for an act differently. Participants learn they are in a pilot program aimed at improving success so they work harder or report better results.
Internal Validity: Confidence in cause and effect Requirements. Difference in Dependent Variable. Independent variable before Dependent Variable. Extraneous factors (alterative rival hypotheses In theory: Two identical groups Pretest-posttest design. makes sure the interactions selection, settings and history. Interactions selection and treatment. Selected have a different reaction to our program than other programs. Make sure you select high potential for our program. Interactions setting and treatment. Results in one setting may not work in other settings. Interactions History and treatment The results we see today may not be true for other times.
Due: External validity; Randomly select people to study (not randomly assigned). Replication even on small scale over time over sample of a study. Clear about how you select people, how do we get people to this discreetness time settings.
Social Science Research Methods
Threats to Validity, Part 2
Social Science Research Methods
Threats to Validity, Part 2
Professor Gavin Ryan Shafron, M.A.
Threats to Internal Validity
Internal Validity – How valid a study is: the more we avoid confounding variables (variables that could interfere in attaining the true results of our study) the higher the level of internal validity.
Threats to Internal Validity
Attrition Threat – In a longitudinal study where you need to measure responses from a participant at multiple time points, attrition refers to when participants drop out or leave a study, causes problems as it reduces available data, in particular when it is different across groups.
Threats to Internal Validity
Attrition: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Threats to Internal Validity
Treatment Diffusion - When one group becomes aware of the other’s treatment and the effects of the treatment.
Threats to Internal Validity
Treatment Diffusion: How do we fix it?
Isolation of participants
Threats to Internal Validity
Testing Threat -- Changes in a test score due to taking it more than once, through familiarization, recall.
Threats to Internal Validity
Testing Threat: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Threats to Internal Validity
History Threat -- Events outside experimental manipulation influence collective or individual participation
“Many change-producing events may have occurred between Observation 1 and Observation 2 “ (Source: Internet Presentation)
“History is more …[likely] the longer the lapse between the pretest and posttest.” (Source: Alliant International University - Internet Presentation)
Threats to Internal Validity
History Threat: How do we Fix it?
Consistent methods / measurements in longitudinal studies from one time point to the next.
Threats to Internal Validity
Instrumentation Threat -- Changes in the measurement or procedure over the course of a study (e.g., with observations, coding systems over time, drift systematically).
Threats to Internal Validity
Instrumentation threat: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Or, use tests rather than observation, ratings, or interviews.
Threats to Internal Validity
Selection Threat -- Differences in groups that exist before treatment or intervention begins. Especially problematic when participants selected for belonging to a specific group. Differences may be due to initial differences in groups – not treatment or intervention.
Threats to Internal Validity
Selection Threat: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Threats to Internal Validity
Maturation Threat - Normal developmental changes in participants between pre and post tests. Gains/Losses over time may be due to normal maturation.
Threats to Internal Validity
Maturation Threat: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Threats to Internal Validity
Inequitable Treatments Threat -- When participants in one group outperform or underperform relative to another group as a result of study expectation of a certain performance.
Threats to Internal Validity
Inequitable Treatments Threat: How do we fix it?
Isolating groups.
Blind studies.
Threats to Internal Validity
Special Treatment Threat -- When groups are treated differently as a result of being in the study – or one group is treated differently from another group b/c of their treatment status.
Threats to Internal Validity
Special Treatment Threat: How to fix it?
Training of treatment administrators and personnel associated with the study.
Threats to Internal Validity
Statistical Regression Threat -- The tendency to drift towards the mean as one takes a test multiple times. Especially problematic when choosing “extreme” scores which can “regress” toward the mean due to influences other than treatment.
Threats to Internal Validity
Statistical Regression Threat: How to fix it?
The problem is with single assessments. Two randomly assigned groups should equally regress toward the mean.
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Threats to Internal Validity
Interaction with Selection (with other factors)
Also known as: Additive and interactive effects of threats to internal validity. --
When certain participants (selection) experience drop out, historical effects, testing issues as a group.
Threats to Internal Validity
Interaction with selection with other factors threat: How to fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time
Threats to Internal Validity
Ambiguous Directionality -- When the independent variable is not manipulated, the direction of the influence is not always clear (e.g., impact of a therapist empathy on client outcome – does therapist get warmer b/c client improves or vice versa?)
Threats to Internal Validity
Ambiguous Directionality: How to fix it?
Unless the temporal order is clear, the directionality is difficult to determine.
Be clear in statements of causality and describe when causality is unclear.
Social Science Research Methods
Threats to Validity, Part 3
Threats to External Validity
What is a threat to External Validity?
This is when the results found are not generalizable to the population of interest.
You may see an effect, but its not the effect you wanted to measure or it doesn’t generalize outside of the small group you are studying!
Threats to External Validity
Selection (Interaction of Causal Units): This is distinguished from selection threat to internal validity. This is the selection threat to external validity. You can think of this one as the interaction of causal units. This is a threat when the participants used in a study are of a specific type such that the results found with them might not apply (be generalizable) to other participants, but researchers imply this.
Example: An article entitled “Depression among Asian Americans” studies depression, but the participants were only from Japan and China, and were upper-middle class. This is a problem because researchers are trying to find out information about Asian Americans in general, but only used Japanese and Chinese upper SES people. The results are not generalizable to all Asian Americans, and depression may work differently with other types of Asians and Asians of other SES’s. This WOULD NOT be a problem, however, if the article was called “Depression among wealthy Japanese and Chinese Americans”, since researchers then don’t intend for the results to be generalized to all Asian Americans.
Selection (Interaction of Causal Units): This is a threat when the participants used in a study are of a specific type such that the results found with them might not apply (be generalizable) to other participants, but researchers imply this.
Threats to External Validity
Remedy: Either try to have participants that are very representative (don’t miss out on certain people by only doing college students or white people or high SES etc), or don’t try to generalize to other people.
Risk: EXTREMELY HIGH. This happens ALL THE TIME in studies.
Look in: PARTICIPANTS to see who were used for the study. Then look at the title, abstract, and/or discussion to make sure you know who they are trying to generalize the results to and talk about.
Threats to External Validity
Interaction of the causal relationship over treatment variations: This is a problem when the research study has a TREATMENT, but a weird variation of the treatment is used, and results found may not be generalizable to other variations of that treatment.
Threats to External Validity
Remedy: Use the whole treatment as it was meant to be given.
Risk: Low. Researches would likely admit that they administered a treatment differently than it was supposed to be administered.
Look in: Methods, measures, and Limitations
Threats to External Validity
Interaction of the causal relationship with outcomes: This is similar to the idea of mono operation bias, but just for the DV. This is a threat when the outcome (usually just one Dependent Variable) was measured a certain way (usually with only one measure). It is a problem when measuring the outcome in a different way could have given different results.
Threats to External Validity
Remedy: Use more than one way to measure the DV.
Risk: Medium. Look for just one DV with just one measure that could have been measured differently. (If the one outcome was height and they used a ruler, do NOT apply this threat, because there is no other way to measure height and get different results).
Look in: Methods
Threats to External Validity
Interaction of causal relationship w/settings: This is a threat when the research was done in a particular setting (environment), and results may not be generalizable to a different setting. By setting we are talking about specific research setting such as laboratory, school, home, internet, ect. NOT geographic location like Texas or New York. Geographic locations are more of a selection threat because we are talking about different types of people with results that may not generalize to other types of people. Here we are talking about a setting where results may not generalize to other settings.
Threats to External Validity
Remedy: Try to do studies in the appropriate types of settings you are trying to generalize to or in more than one setting.
Risk: HIGH. Only 1 setting is used in most studies.
Look in: Methods in Procedure
Threats to External Validity
Context-dependent Mediation: This is a threat when a mediating variable may not mediate the same way in a different situation (different setting, or participants, or treatment, or task, ect). This is only when the study has a mediating variable, and this relationship (the variable mediating the relationship between two other variables) may not be the same in a different situation.
Threats to External Validity
Remedy: Try to use generalizable situations.
Risk: RARE. The study must have a mediating variable in it.
Look in: Methods, Results, and Conclusions
Threats to External Validity
Interaction of History and Treatment: This is different than the Internal Validity threat History. That is when an event affects participants and causes the results found. THIS one is when results found from a study are not generalizable to other time periods. This usually has to do with whole eras. It mostly applies to old studies, when times were different, and results found may not be the same today.
Threats to External Validity
Remedy: Replicate studies over time to see if they still apply.
Risk: Rare.
Look in: Date of publication, Methods
Hi class, For this week we will be continuing our discussion of validity and in particular threats to ensuring validity. As we’ve reviewed, when designing a study of any kind, we want to ensure that as much reliability and validity can be secured to be able to state that what we are observing is in fact more likely than not what happens in the general population, rather than some artifact that is not the result of our treatment or intervention we are studying. So when considering this we have to review some of the basic concepts of what can go wrong when we implement a study.
Now as we might remember, internal validity refers to how well an experiment is done, especially whether it avoids confounding (more than one possible independent variable [cause] acting at the same time). The less chance for confounding in a study, the higher its internal validity is.
Therefore, internal validity refers to how well a piece of research allows you to choose among alternate explanations of something. A research study with high internal validity lets you choose one explanation over another with a lot of confidence, because it avoids (many possible) confounds.
Attrition Threat – In a longitudinal study where you need to measure responses from a participant at multiple time points, attrition refers to when participants drop out or leave a study, causes problems as it reduces available data, in particular when it is different across groups.
“The differential loss of individuals from treatment and/or comparison groups. This is often a problem when research participants are volunteers. Volunteers may drop out of the study if they find it is consuming too much of their time. Others may drop out if they find the task to be too arduous.” (Source: Internet presentation)
It also can be thought of as“…refer[ing] to the drop out of subjects in an experiment…if people drop out of an experiment they do so for a reason…[perhaps indicates] a problem with either a noxious or an inadequate independent variable” (Kroth, 1988, p. 60).
Attrition: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Attrition: How do we fix it?
The best way to account for this is to randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Treatment Diffusion - This occurs when a comparison group learns about the program either directly or indirectly from program group participants. In a school context, children from different groups within the same school might share experiences during lunch hour. Or, comparison group students, seeing what the program group is getting, might set up their own experience to try to imitate that of the program group. In either case, if the diffusion of imitation affects the posttest performance of the comparison group, it can have an jeopardize your ability to assess whether your program is causing the outcome. Notice that this threat to validity tend to equalize the outcomes between groups, minimizing the chance of seeing a program effect even if there is one.
References
Treatment Diffusion - When one group becomes aware of the other’s treatment and the effects of the treatment.
Treatment Diffusion: How do we fix it?
Isolation of participants.
Treatment Diffusion: How do we fix it?
By isolating one group of participants from the other, this mitigates the likelihood that demand characteristics of the contrast between treatment conditions could interfere with the results and threaten the internal validity of the study in this way.
Testing Threat -- Changes in a test score due to taking it more than once, through familiarization, recall. Testing Threat -- Changes in a test score due to taking it more than once, through familiarization, recall.
Also called “pretest sensitization,” this refers to the effects of taking a test upon performance on a second testing. Merely having been exposed to the pretest may influence performance on a posttest. Testing becomes a more viable threat to internal validity as the time between pretest and posttest is shortened.” (Source: Internet Presentation)
The impact of “taking a test, and whether or not any special circumstances existed which may have influenced sores on a subsequent test…taking a test may cause anxiety, stress or self-consciousness the first time one takes it, but such emotions may be absent at posttest time [causing an improvement that is not simply due to the treatment, or IV]”
(Kroth, 1988, p. 60). Testing Threat: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Testing Threat: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
History Threat -- Events outside experimental manipulation influence collective or individual participation
“Many change-producing events may have occurred between Observation 1 and Observation 2 “ (Source: Internet Presentation)
“History is more …[likely] the longer the lapse between the pretest and posttest.” (Source: Alliant International University - Internet Presentation)
History Threat -- Events outside experimental manipulation influence collective or individual participation
“Many change-producing events may have occurred between Observation 1 and Observation 2 “ (Source: Internet Presentation)
“History is more …[likely] the longer the lapse between the pretest and posttest.” (Source: Alliant International University - Internet Presentation)
This was described best by Kroth, 1988 who stated:
“Things happen between a pretest and posttest, especially when there may be 6 months to a year between the two…These …events, or outside influences [confounding variables] may cause changes we see between pretest time and posttest time may be due to these outside factors, not the independent variable” (Kroth, 1988, p. 60)
History Threat: How do we Fix it?
Consistent methods / measurements in longitudinal studies from one time point to the next.
Instrumentation Threat -- Changes in the measurement or procedure over the course of a study (e.g., with observations, coding systems over time, drift systematically
Changes in the measurement or procedure over the course of a study (e.g., with observations, coding systems over time, drift systematically).
“Changes in the way a test or other measuring instrument is calibrated that could account for results of a research study (different forms of a test can have different levels of difficulty). This threat typically arises from unreliability in the measuring instrument. Can also be present when using observers.”
(Source: Internet Presentation)
Another possibility is whether the instrument tends to…[vary] all by itself, registering…a spurious increase or decrease which is unrelated to what the good old independent variable is trying to do” (Kroth, 1988, p. 60).
Instrumentation threat: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Or, use tests rather than observation, ratings, or interviews.
Selection Threat -- Differences in groups that exist before treatment or intervention begins. Especially problematic when participants selected for belonging to a specific group. Differences may be due to initial differences in groups – not treatment or intervention. Selection Threat -- Differences in groups that exist before treatment or intervention begins. Especially problematic when participants selected for belonging to a specific group. Differences may be due to initial differences in groups – not treatment or intervention.
“This can occur when intact groups are compared. The groups may have been different to begin with. If three different classrooms are each exposed to a different intervention, the classroom performances may differ only because the groups were different to begin with.” (Source: Internet Presentation)
“…the biases which result from comparing one group of complex and unique people to another group of complex and unique subjects” (Kroth, 1988, p. 60).
Selection Threat: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Maturation Threat - Normal developmental changes in participants between pre and post tests. Gains/Losses over time may be due to normal maturation. Maturation Threat - Normal developmental changes in participants between pre and post tests. Gains/Losses over time may be due to normal maturation.
“Changes in physical, intellectual, or emotional characteristics, that occur naturally over time, that influence the results of a research study.” (Source: Internet Presentation)
“Internal changes occurring between the pretest … and posttest… which can cause changes in the dependent variable. These are internal changes, like becoming tired or heavier or weaker, less anxious, or more inattentive with the passage of time.” (Kroth, 1988, p. 60)
Maturation Threat: How do we fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Inequitable Treatments Threat -- When participants in one group outperform or underperform relative to another group as a result of study expectation of a certain performance.
Inequitable Treatments Threat: How do we fix it?
Isolating groups.
Blind studies.
Special Treatment Threat -- When groups are treated differently as a result of being in the study – or one group is treated differently from another group b/c of their treatment status.
Special Treatment Threat -- When groups are treated differently as a result of being in the study – or one group is treated differently from another group b/c of their treatment status.
Special Treatment Threat: How to fix it?
Training of treatment administrators and personnel associated with the study.
Special Treatment Threat: How to fix it?
Training of treatment administrators and personnel associated with the study.
Statistical Regression Threat -- The tendency to drift towards the mean as one takes a test multiple times. Especially problematic when choosing “extreme” scores which can “regress” toward the mean due to influences other than treatment.
Statistical Regression Threat -- The tendency to drift towards the mean as one takes a test multiple times. Especially problematic when choosing “extreme” scores which can “regress” toward the mean due to influences other than treatment.
“Occurs when individuals are selected for an intervention or treatment on the basis of extreme scores on a pretest. Extreme scores are more likely to reflect larger (positive or negative) errors in measurement (chance factors). Such extreme measurement errors are NOT likely to occur on a second testing.” (Source: Internet Presentation)
This “refers to the fact that a group at the extreme ends of a normal curve has a greater probability of being “closer” to the mean of that curve the next time the group is measured” (Kroth, 1988, p. 60).
Statistical Regression Threat: How to fix it?
The problem is with single assessments. Two randomly assigned groups should equally regress toward the mean.
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time.
Interaction with Selection (with other factors)
Also known as: Additive and interactive effects of threats to internal validity. --
When certain participants (selection) experience drop out, historical effects, testing issues as a group.
One research scholar, Vanbeek states: “No threat is completely isolated, often one threat can lead to another or enhance another, often in vicious cycles.”
Interaction with selection with other factors threat: How to fix it?
Randomly assign participants to two groups, one treatment group & one control group.
Researchers can expect similar effects in treatment group versus control group over time
Ambiguous Directionality -- When the independent variable is not manipulated, the direction of the influence is not always clear (e.g., impact of a therapist empathy on client outcome – does therapist get warmer b/c client improves or vice versa?)
Ambiguous Directionality: How to fix it?
Unless the temporal order is clear, the directionality is difficult to determine.
Be clear in statements of causality and describe when causality is unclear.
Ambiguous Directionality: How to fix it?
Unless the temporal order is clear, the directionality is difficult to determine.
Be clear in statements of causality and describe when causality is unclear.
Page 5 of 3 treats to extern ….
Interaction of the causal relationship over treatment variations: This is a problem when the research study has a TREATMENT, but a weird variation of the treatment is used, and results found may not be generalizable to other variations of that treatment.
Example: Researchers test a manualized CBT treatment. This CBT treatment is meant to be 12 weeks, but researchers did a 7 week version of it, so the results may be different. Effects found with this treatment are not necessarily the same at results that would have been found with the treatment they were trying to test (the 12 week treatment). OR Researchers combined CBT with another treatment, which may find different results. OR They only used part of the treatment, such as the Cognitive portion of the treatment.
Example: Unintelligent researchers wanted to measure if taking Prozac effects basketball players’ performance. They give them the drug, then measure the percentage of shots they make during a game, and compare this to their old shot percentage. The effects found may have been different if they had measured a different outcome than shot percentage. If they had included assists, blocks, passes, coaches ratings of them, ect, the different results may have been found about the players performance.
Example: Researchers wanted to test children’s reactions to their parents instructions. They tested this in a room at a local elementary school, asking parents to tell their kids to complete certain tasks. The kids were super compliant. HOWEVER, the setting here was a school, where kids are accustomed to being compliant. If the study were done at children’s homes, different results may have been found. You cannot generalize this to how children’s reaction to their parents instructions in general, only how they react to them at a school.
Example: Suppose a study used only males to study depression. They found that owning more video games makes them super excited and causes sleeplessness, which later causes depression in these people. So, sleeplessness mediates the relationship between video games and depression. However, this may not be the case if females were used. Owning video games may bore them, which may make them depressed, but it doesn’t cause sleeplessness because they aren’t excited about the games. So the video games cause depression for them, but this isn’t mediated by sleeplessness. Or if different video games were used, then sleeplessness may not mediate the relationship the same way.
Interaction of History and Treatment: This is different than the Internal Validity threat History. That is when an event affects participants and causes the results found. THIS one is when results found from a study are not generalizable to other time periods. This usually has to do with whole eras. It mostly applies to old studies, when times were different, and results found may not be the same today.
Piaget’s
Theory of Development Involving Human Intelligence Incorporates Schemas
Esther
Barros-Garcia
Abstract
Piaget’s theory
of development involving human intelligence incorporates the concept of
schemas. Schemas are mental representations of ideas, concepts, and objects. As humans we make great efforts to achieve
or obtain something to be in a state of understanding and equilibrium. When
information is not understood we move into a state of disequilibrium, a feeling
of discomfort from unfamiliar information, which drives us to assimilate and
accommodate our schemas to return to a state of equilibrium. To Piaget,
development = increase and increase complexity of schemata which are the force
that keeps us motivated through learning. We strive to be at equilibrium, we do
not like frustration of dealing with unfamiliar knowledge. Equilibrium: when a child’s schema is capable
explaining what he/she perceives form outside world. Disequilibrium: when child
experiences new information/ stimuli for the first time. Unsure how to process
information and begins to create or expand existing schemas.
Piaget’s Theory of
Development Involving Human Intelligence Incorporates Schemas
Schemas
are mental representations of ideas, concepts, and objects. An important aspect
to the concept of schemas is assimilation
which is using an existing schema to deal with/understanding new objects,
situations, and information. Also equally important is the concept of
accommodation which involves altering existing schemas to develop more complex
ones or even brand new schemas altogether to deal/with understanding new
information. Lastly, the concept equilibrium is when a schema is fully capable
of explaining and interpreting information that is perceived form the outside
world. As humans we strive to be in a state of understanding and equilibrium.
When information is not understood we move into a state of disequilibrium, a
feeling of discomfort from unfamiliar information, which drives us to
assimilate and accommodate our schemas to return to a state of equilibrium. To
Piaget, development equaled an increased complexity of schemas or schemata .
Piaget’s
theory of development also includes four specific stages of development that
are biologically universal to all children. The first stage is the sensorimotor
stage (0-2 years). Children in this stage have a cognitive system that is
limited to the motor reflexes while infants
are busy discovering relationships between their bodies and the environment. The second stage is the preoperational
stages (2-6) during this stage, children start to use mental imagery and
language. Children here are very egocentric. Piaget claims children in this
stage are not able to comprehend cardinality and ordinality (the ability to
realize equal quantities) The third stage is the concrete operational (7-11
years) at this stage the child can see and reason with concrete knowledge but
still can not see the summary side of things and fully develop all the possible
outcomes. They can understand conservation of number like the measure of mass,
weight, area and volume. Lastly, the fourth stage is the formal operational
(11+ years) this stage is where children are definitely able to think logically
and theoretically. They could use symbols that are related to the concepts and
easily how problems would be solved. ” To Piaget, this was the ultimate stage
of development. He also believed that even though they were here, they still
needed to revise their knowledge base. Children by this stage are
self-motivators. They learn from reading
and trying out new ideas as well as form helping friends and adults. Piaget
believed that not everyone reaches this stage of development.
Definition of
numbers, Piaget’s idea of a child‘s
ability to understand number includes the capability to compare sets – child’s
ability to give the correct answer of equality when items are positioned in
one-to-one ratio and if child was able to judge equality when there were fewer
then 4-5 items in a set. (Intuitive numbers 1-5). Also important, was the
concept of counting sets. Children would count and recount items in a row using
words that represented numbers such as “one, two, three” etc. known as counting
words. Children learned that the last word used was the expected value outcome
of the set. So although the children were able to give the appropriate (number)
(#) word as their response regardless of the changing appearance of a row,
Piaget believed this did not prove comprehension of number. A child being able
to repeat the counting word as the correct answer did not guarantee that the
child realized the quantity is equal
both times. Tests regarding the abilities stated above were designed to see if
children had an understanding of the cardinal property of number,
but Piaget’s theory of what it means for a child to comprehend number is more
than just a test of cardinality. Later work by one of Piaget’s collaborators
incorporated the study of ordinality, a child’s ability to understand equality
using continuous as well as discontinuous item (qualities). Ordinal included
having a child agree that a set of 30 blocks was larger than a set of 6 blocks
(discontinuous amount) blocks from the large set are dumped down a slide, and
children are unable to recognize that the new pile forming at the bottom will
eventually contain the exact same quantity as the original small set of 6
blocks. They were unable to relate the equality of the new continuous set being
formed to the discontinuous set of 6.
Point one: Critics of Piaget claim that he did
not play an influential role in the development of child psychology and they
could not be more wrong. His critics are wrong because for one, they over
simplified Piaget’s theory of child understanding of number. Critics conducted
their own research (Gelmen, 1972; McGarrigle &Donaldson, 1975 Mehler &
Bever 1967) and found that children as young as 3 were not deceived by the
changing appearance of a set and were able to give the correct answer regarding
equality. This opposed Piaget’s research, however, although these young
children who were still in the preoperational stage were able to provide the
correct answer there is no agreement regarding which operational level was
required to perform these new conservation tasks. Children involved in post-Piaget
research could have easily counted the items in the sets because fewer items
were used in these sets as compared to the sets in Piaget’s research. Children
could have also relied on they’re natural ability to perceive small numbers
(intuitive numbers) (Benat, Lehalle,
& Joven 2004). Without
agreement as to which operational level was required to complete the tasks,
children from different operational stages could have completed the tasks
making the post-Piaget research incomparable to Piaget’s.
The second argument used by Piaget’s critics is
that many young children still in the preoperational stage of development had
the ability to count in general. Having mastered the ability to count meant a)
to always use same sequence of counting words, b) use only one counting word
per object, c) using the last counting word to represent the total (quantity of
items in set), d) realizing that any set of objects could be counted, and e)
understanding that objects could be counted in any order. The ability of the child
to count and repeat counting word as the answer became a learned social
convention, or learned response, when questioned by the researchers it did not
prove comprehension that quantities were equal. When questions were rephrased
to the children asking them hand the total number of items to the researchers,
they did not know how many items was the correct amount to give.
Critics do not have proper understanding of
Piaget’s writing (1998 Lourenco and Mechado 1996) (Bond & Tryphon 2007). Post-
Piaget research “works” in proving children have the ability to understand
value of cardinal numbers ONLY because they do not involve the Piaget
definition of what number is: a necessary synthesis of both ordinality and
cardinality. Critics did not include Piaget definition of number in their research;
therefore, their arguments against Piaget are invalid. (Desrochers, 2008)
Causality: Piaget’s
critics further misunderstand his work regarding a child’s understanding of
physical causality by making the mistake of only referring to his earlier books
The Child’s Conception of the World (1929/1930) and
The Child’s Conception of Physical Reality (1927/1930). These books only organized children’s
explanations for natural occurring (physical) phenomena. His later books Understanding Causality (1971/1974), La Transmission des mouvements (1972), and
Epistemology and Phycology of Functions (1968/1977) were the books that
involved his ideas of how children reasoned out mechanical causation. Not only
were his critics using the wrong material to compare research but Piaget’s
ideas of causality were not fully developed until during the 1960’s at the
International Centre for Genetic Epistemology (ICGE). So without a clear model
to compare, post-Piaget critics have no valid argument to go against Piaget.
Genevan
researchers = (Piaget) do not recognize that young children still in the
preoperational stage of development can fully process all aspects of mechanical
causation and they are correct. Children can form two-term cause and effect relationships
for example; two balls colliding. When one ball moves at a higher rate of speed
children can see that it causes the second ball to be projected further. This
understanding is very basic and can be represented by a formula y = f(x)
distance of projected ball = f (amount of force from the first ball). If any
other terms are involved preoperational children have trouble explaining the
relationship of the added term. Only older children in the concrete operational
stage can easily understand a three-term relationship, Piaget said these
children have reached a level of understanding composition of functions leading
to a basic understanding of more sophisticated models. The research of Piaget’s critics (1980’s) state
that Piaget is wrong, and preoperational children can understand causality
because in their experiments Shultz (1982) was able to see that children
understood which apparatus caused a certain effect, for example choosing a lamp
as the cause of a spot of light. However, this understanding of causality
relates only to the simple formula Piaget discovered in preoperational
children, it does not mean the children have reached an understanding similar
to the concrete operational children who understand three-term cause and effect
relationships. When all the aspects of Piaget’s (Genevan) developmental model
are properly taken into account it is difficult to relate the work of
post-Piaget’s critics. Again, it is necessary to understand that the ideas of
Piaget were not fully carried out until later research completed from 1955-1980
by his supporters at the ICGE. Research done by his supporters became known as
“The Genevan Models” and should be taken into account when evaluating how
relevant Piaget actually was in the understanding of child cognitive
development.
Finally, in
an article written by Armando Machado (1996) ten of the most common criticisms
to Piaget’s ideas are tackled and corrected proving that Piaget was and still
is a crucial part to the understanding of children’s cognitive skills and
development. One incorrect argument that is often citied is that Piaget’s
theory establishes age norms and the new post-Piaget research disconfirms these
norms. This is a huge misconception of Piaget’s theory; age is not a criterion
to defining a developmental level. According to Piaget the key element was the
sequence of cognitive transformations - starting first from sensorimotor, then
moving to preoperational, followed by operational, and then finally reaching
the developmental level of formal thinking – age as merely an indicator as to
which developmental level the child currently possesses it is not the element
of which the current level is based on. Critics of Piaget thought that if they
were able to show that children who were below the age 11-12 can demonstrate
deductive reasoning skills, this would constitute formal thought.(Ennis, 1982)
Critics thought that is children who were below 11-12 could possess formal
thought patterns this would disprove Piaget. Researchers established tests that
showed children of age 5-6 could in fact show simple reasoning and deductive
skills. Children were able to correctly conclude that “Mary is at school” from
the following reasoning exercise: “If John is at school, then Mary is also at
school. John is at school; what can we say about Mary?” Piaget himself refuted this argument stating
that the ability to solve these problems based on perceived logic does not
prove a child is using formal operations because when using formal operations
the subject must show the ability to comprehend, envision, and select the
correct answer from all possible outcomes. Perhaps on the surface it may appear
that Piaget’s critics have on occasion disproved him with they’re contrary
research, but when examined more closely and when all major aspects of Piaget’s theory of development are
incorporated it is clear to see that the findings of many post-Piaget research
is not comparable to Piaget’s and in no way dismiss his contribution to the
world of psychology
References
Bullock, M., Gelman, R.,
& Baillargeon, R. (1982). The development of causal reasoning. In W. J.
Friedman (Ed.), The development psychology of time (pp. 209–254). New York : Academic
Press.
Cowan, R. (1987). When do
children trust counting as a basis for relative number judgements? Journal
of Experimental Child Psychology, 43, 328–345.
Desrochers, S. (2008). From Piaget to Specific
Genevan Developmental Models. Child Development Perspectives, 2(1),
7-12. doi:10.1111/j.1750-8606.2008.00034.x
Gelman, R., Bullock, M.,
& Meck, E. (1980). Preschoolers’ under- standing of simple object
transformations.Child Development, 51, 691–699.
Greco,
P. (1960). Recherches sur quelques formes d’infe ́ rences arithme ́tiques et
sur la compre ́hension de l’ite ́ration nume ́rique chez l’enfant. In P. Gre
́co, J. B. Grize, S. Papert, & J. Piaget (Eds.), Proble`mes de la
construction du nombre (pp. 149–213). Paris , France
Lourenço, O., & Machado, A. (1996). In
defense of Piaget's theory: A reply to 10 common criticisms. Psychological
Review, 103(1), 143-164. doi:10.1037//0033-295X.103.1.143
People Do Need Self-Esteem
Esther Barros-Garcia
Abstract
People Do Need Self-Esteem
Crocker
and Nuer’s response to Pyszczynski’s study on terror management theory (TMT)
and why people need self-esteem is incorrect. They claim that self-esteem is not needed by individuals and
that self-esteem only gets in the way of people ultimately achieving their real
goals. TMT takes the assumption that
everyone needs self-esteem but questions why we need it. Crocker uses this as
the base of her argument and asks the question if we even need self-esteem at
all?
People Do Need Self-Esteem
It
is argued by Crocker and Nuer that pursuing self-esteem actually creates
anxiety rather than becoming a means to reduce anxiety. When challenged and
when a person’s self-esteem is on the line people will be motivated to do
better and will often push themselves succeed, but this creates further anxiety
on that person altogether. It is true that when people accomplish certain goals
their self-esteem receives a temporary boost, but the issue with Crocker is
that this boost is only temporary and anxiety will once again return when
life’s next challenge comes up. Crocker is mistaken in the idea that all
anxiety is bad. The anxiety and stress that is brought upon by everyday
challenges is good; this stress is brought upon by the need to remain in
homeostasis-being in balance-and is a natural response for all animals when faced
with challenges (Sapolsky 2000). The only issue with dealing with stress to
stay in balance is that sometimes people may stress over issues that do not
deserve stress and that may cause biological and psychological harm. What is an
understandable but still an incorrect assumption by Crocker is that people always
handle all stress in the same way. Not all stress is as serious as certain
other stress. Stress relating to life and death is a very serious issue for the
person who is thinking about it, this sort of stress is not relatable to the
stress of passing an academic class, buying a new car, or having a new romantic
relationship (everyday stress). Everyday stress comes and goes, and increases
and decreases on a regular basis, but the stress over death is a much heavier
burden to deal with and although everyday stress may cause people to feel
depressed at times subconsciously we remain positive and optimistic with some
self-esteem; it is the only way to wake up every morning and find value in
everyday accomplishments. Without any
sort of positive self-esteem a person would not find reason to live (since
death is inevitable) and would likely kill themselves. Crocker claims
self-esteem is a way to deal with anxiety over death, but it does not get us
anywhere. The fact that we live another day to experience life is a good enough
result of maintaining a positive self-esteem.
Crocker
and Nuer’s second point to why people don’t need self-esteem is that
self-esteem only gets in the way of people achieving goals; it does not help
accomplish them. Crocker brings up the valid point that on occasion people tend
to get into bad habits of self-handicapping themselves (creating obstacles and
impossible situations as an excuse for failure) to maintain their self-esteem.
However, that is only one part of the story. Yes, many people create their own
obstacles and create excuses, but if a goal is important enough people will
find a way to accomplish it no matter what. Sometimes protecting the Ego is
more important, but when people realize that their other goals are more
important it does not matter what they look like to other people, they will
ignore other’s opinions and set their Ego aside to work hard for the goal they
want. Crocker again assumes that people do either one of two things; one,
immediately concentrate on preserving their self-esteem over their goals and
two, only attempt their goals because it is rewarding to the Ego and gives a
“high” once accomplished. Some people may fail at first and give excuses, but
some people may recognize the importance of the goal immediately and give it
their all no matter how foolish it looks to the outside world. Other goals are
rewarding yes, but are done because they must be done; a mother learning to
finally breast feed her child for instance or temporarily working a minimum
wage job to make sure the goal of paying the rent is accomplished.
Crocker
and Nuer’s third point to why self-esteem isn’t necessary is that people can
create secure attachments in order to deal with anxiety. Crocker uses the
example of a child receiving the attention of a caregiver as a secure
attachment. Although to some extent Crocker is correct; healthy attachments do
help ease anxiety. These attachments are meant to support, but are also meant
to show a child how to manage anxiety on his/her own one day. Constant care or
over protection can cause dependency. If a child is fully dependent on a
caregiver that child will then find anxiety when that caregiver is not
available. So, if a person becomes dependent to someone else (secure
attachment) there will still be anxiety over death when that person is not
available. This attachment not being available can cause someone to again have
anxiety over death, but may feel even worse about it because they do not have
their secure attachment to depend on.
Croker
and Nuer further argue that only by setting clear inclusive goals can someone
achieve goals without any self-esteem. The example of public speaking was used
and the fact that many people who hate or fear speaking publically do it every
day. Crocker states that only by letting go of having an Ego or self-esteem can
one say “So what?” and face the fear of public speaking without caring if
he/she looks foolish. Crocker is again incorrect because the act of doing
something that is embarrassing takes a lot of courage and self-esteem. In fact
only people who have accomplished their fears (public speaking) can look at
themselves and can realize that they were overreacting to their fears. Once a
person knows they are able to accomplish goals with/without fear some
self-esteem is created. People may still have anxiety but the more times this
goal is accomplished (public speaking) the easier it will become and
self-esteem will continue to grow.
Crocker
and Nuer continue to challenge TMT by questioning whether death is the real
cause of anxiety in the first place and if there is an alternative view to
death. Crocker argues that it is not death but it is the desire to find meaning
and purpose that cause anxiety. This is a very good point, yet, one cannot
argue one without the other. Death is a certainty, but what happens to us, what
we feel and experience (if anything) after death is not. It is true that some
people may view death as an inspiration to work harder and enjoy the precious
moments we have now, but the fact is that anxiety comes to us because we all
want to find a purpose and it also involves finding that purpose before we die. We do not know
what will happen after death and not finding our purpose before we die is the
real cause of anxiety. Both fears are related, and even after discovering what
we think is our purpose we may still find ourselves in fear or having anxiety
because we are not sure we will accomplish them before we die.
In
conclusion, terror management theory is still a valid hypothesis for why we
experience and how we manage anxiety. Death is still involved as the main
reason for our anxiety and although it is possible for us to use death as a
positive motivator to accomplish our goals, the fact still remains that death
is the cause of our anxiety and maintaining some level of self-esteem is the
only way to manage this fear and anxiety.
References
Crocker,
J., & Nuer, N. (2003). The relentless quest for self-esteem. Psychological
Inquiry, 14,31-34.
Crocker, J., & Park, L. E. (in press). The
costly pursuit of self-esteem. Psychological Bulletin.
Crocker, J., Sommers, S. R., & Luhtanen, R.
K. (2002). Hopes dashed and dreams fulfilled:
Contingencies
of self-worth and admissions to graduate school. Personality and Social
Psychology Bulletin, 28, 1275-1286.
Pyszczynski, et al. (2004)
Lecture slides for this week
|
|
Lecture slides for this week
|
|
Lecture slides for this week
|
|
Social Science Publication Format: APA
|
|
Terms list to
guide your preparation for next week's exam.
|
|
Please select your articles from this folder. If you would like to
utilize outside articles, you are welcome to do so but they must be scholarly
in nature and published by peer reviewed psychology journals. I strongly
advise those who seek outside articles to clear their article with me before
they use them in their paper. Using non-academic articles will result in a
significant detrimental impact to your grade.
|
|
Guide for writing your term paper
|
|
Slides for Lecture 8
|
|
Don't worry, you do not need to memorize all of this (no formulas),
but as you move through this course and your Statistics courses this
"cheat sheet" will be helpful for your reference.
Very best,
Prof. Shafron.
|
|
Use as a reference to study for the Final Exam
|
|
Use as practice for potentially similar questions on the Final Exam
|
|
Threats to Validity part 2 Slides
|
|
Threats to Validity part 3 Slides
|
|
Threats to Construct Validity Lecture Slides
|
|
Threats to Statistical Conclusion Validity Slides
|
|
study guide for the final exam
|
Lecture Slides
Week 4
Midterm Study
Guide
Handout of Basic
Statistical Concepts 
No comments:
Post a Comment