FInal Exam Flashcards
(266 cards)
1
Q
Empiricism
A
The use of verifiable evidence as the basis for conclusions, collecting data systematically and using it to develop, support or challenge a theory
2
Q
Theory
A
Set of statements, describe principles about how variables relate to one another
3
Q
Hypothesis
A
Prediction
Specific outcome the researcher will oberving a study if the theory is accurate
4
Q
pre registered
A
Before collecting any data, the researcher has stated publicly what the study’s outcome is supposed to be
5
Q
Replication
A
The study is conducted again to test whether the results consistent
6
Q
weight of the evidence
A
- Collection of studies, including replication of the same theory
- How scientists evaluate their theories
7
Q
Falsifiability
A
A feature of scientific theory, in which it is possible to collect data that will indicate that the theory is wrong
8
Q
Universalism
A
- Scientific claims are evaluated according to merit, independent of the researcher’s credentials or reputation. The same pre established criteria apply to all scientists and all research
- Even a students can do science you don’t need an advanced degree or research position
9
Q
Communality
A
- Scientific knowledge is created by a community and its finding belong to the community
- Scientists should transparently and freely share the results of their work with other scientists and the public
10
Q
Disinterestedness
A
- Scientists strive to discover the the truth, whatever it is, they are not swayed by conviction, idealism, politics or profits
- Scientists should not be personally invested in whether their hypotheses are supported by the data
10
Q
Organized skepticism
A
Scientists question everything including their own theories widely accepted ideas and “ancient wisdom”
Scientists accept almost nothing at face value, nothing is sacred they always ask to the see the evidence
11
Q
Self correcting
A
A process in which scientists make their research available for peer review, replication, and critique with the goal, with the goal of identifying eros and correcting things in the research
12
Q
Applied research
A
- Research is conducted in local, real world context
- Research whose goal is to find a solution to a real world problem
13
Q
Basic research
A
Research whose goal is the enhance the general body of knowledge
Rather than address a specific, practical problem
Ex understand the structure of the visual system, capacity of human memory
14
Q
Translational research
A
- Use of lessons from basic research to develop and test application to health care, psychotherapy or other forms of treatment and intervention.
- Represents a bridge from basic to applied research
15
Q
comparison group
A
A group in the experiment whose levels differ from those of the treatment group in some intended and meaningful ways
Enable us to compare what would happen both with and without the thing we are interested in
16
Q
Confound
A
Alternative explanation
17
Q
Confederate
A
Actor playing a specific role for the experiment
18
Q
Probabilistic
A
Research findings do not explain all cases all the time, instead the conclusions of research are meant to explain a certain proportion of the possible cases
19
Q
availability heuristic
A
- Things that pop up easily in our mind tend to guide our thinking
- When events or memories are vivid, recent or memorable tend to come to mind more easily
- May lead us to wrongly estimate how much something happens or the number of something
20
Q
present/present bias
A
A bias in which people incorrectly estimate the relationship between an event and its outcomes focusing on times the event an outcome are present, while failing to consider evidence that is absent and harder to notice
21
Q
confirmation bias
A
Tendency to look only at information that agrees with what we want to believe
22
Q
bias blind spot
A
Tendency in for people to think in comparison to others they themselves are less likely to engage in biased reasoning
23
Q
empirical journal article
A
Report for the first time the result of an empirical research study
Contains details about the study’s method, statistical test, and results
24
review journal article
Summarize and integrate all the published studies that have been done in one research area
25
Meta-analysis
Quantitative technique
Combines the results of many studies and gives a number that summarizes the magnitude
26
effect size
Magnitude
Strength of relationship between the two variables
27
Disinformation
- Deliberate creation and sharing of information known to be false
- Takes many forms
- Those who spread disinformation include haste groups
- Cloaked false, racist stories, websites disguised as real
28
Variable
something that varies
Must have at least two levels or values
29
levels
value or conditon
30
constant
Something that could potentially vary but that has only one level in the study in question
31
measured variable
One whose levels are simply observed and recorded
Height, IQ, gender hair color
32
manipulated variable
Variable a researcher controls, usually by assigning study participants to the different level of that variable
Ex: 10 milligrams of medication vs 20 mg
33
Construct
A variable of interest
Stated at an abstract level usually defined as part of a formal statement of a psychological theory
34
conceptual variable
Name of the concept being studied
Ex - “satisfaction with life”
35
operational definition
How the construct is measured or manipulated in an actual study
Ex - five questionnaire items on the satisfaction with life scale
36
operational variable
Specific way in which a concept of interest is measured or manipulated as a variable in the study
37
operationalize
Turn a concept of interest into a measured or manipulated variable
38
Claim
Argument someone is trying to make
39
frequency claim
Describe a particular rate or degree of a single variable
Claims that mention the percentage of a variable, the number of people who engage in some activity or a certain group’s level ojn a variable can all be frequency claims
Identified because they focus on only one variable
Variables are always measured and not manipulated
Ex - “ 31% of texans admit to texting while driving”
40
association claim
Association claim argues that one level of a variable is likely to be associated with a particular level of another variable
Variables that are associated sometimes said to correlate/covary
“Girls more likely to be compulsive texters”
41
Correlate
When one variable changes, the other variable tends to change too
More simply may be said to be related
42
correlational study
Two variables
Variables are measured and the relationship between them is tested
Correlational study support association claims
43
positive association
- high goes high
- low goes low
44
scatterplot
A graph in which one variable is plotted on the y axis and the other variable is plotted on the x axis
45
negative association
High goes with low
Low goes with high
46
Zero association
No association between the variables
47
Causal claim
Argues that one of the variables is responsible for changing the other
Use verbs like “link, associate, correlate, predict, tie to, and be at risk for”
“Could, may, seem, suggest, sometimes, potentially”
48
Validity
Appropriateness of a concussion or decision and in general a valid claim is reasonable a accurate and justifiable
49
construct validity
How well a conceptual variable is operationalized
When you ask how well a study measured or manipulated a variable?
50
Generalizability
Important question with frequency claims
Extent to which the population in the study represents the the population they are intended to represent
How did the researcher choose the study’s participants and how well did the participants represent the sample
51
External validity
How well the results of a study generalize to, or represent, people, contexts beside those original studies
52
Point estimate
A single estimate of some population value based on data from a sample
In frequency claim usually a percentage
52
Statistical validity
Extent to which a study’s statistical conclusion are precis, reasonable nad replicable
How well do the numbers support hte claim?
53
53
Confidence interval
In frequency claim
This is how pt estimate is measured
A given range indicated by a lower and upper value this is designed to capture the population value for a pt estimate
Contains 0, not statistically significant
Doesn’t contain 0 is statistically significant
54
3 criteria for causation
- covariance
- temporal precedence
- internal validity
55
Temporal precedence
Method was designed so casual variable clearly comes first in time, before the effect variable, to make the claim
55
Covariance
Extent to which two variables are observed to go together
First criterion a study must satisfy in order to be a causal claim
56
Internal validity
Study’s ability to eliminate alternative explanations for the association
57
Experiment
One variable is manipulated and the other is measured
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independent variable
manipulated varaible
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dependent variable
measured variable
60
random assignment
- the use of random methods to assign participants into different experimental groups
61
debriefed
To inform participants after about a study’s true nature, details, and hypotheses
62
principle of respect for persons
1. Individual potentially involved in research should be treated as autonomous agents, they should be free to makeup their own minds about whether they wish to participate in a research study
Every participant is entitled to informed consent
63
informed consent
Each person learns about the research project, considers its risks and benefits and decides whether to participate
64
principle of beneficence
Researchers must take precautions to protect participants from harm, and to ensure their well being
Researchers carefully assess the risks and benefits of the study they plan to conduct, also consider how it might harm of benefit a community
Ex tuskegee syphilis study
65
confidential study
Researchers collect some identifying information but prevent it from being disclosed
65
anonymous study
Researchers do not collect any potentially identifying information, including names, birthdays, and so one,
66
principle of justice
Fair balance between the kind of people who benefit from it
67
institutional review board (IRB)
Committee response; for interpreting ethical princip;es and ensuring that research using human participants is conducted ethically
68
Deception
Researchers withheld some details of the study from participants = deception through omission
Researchers actively lied to participants - deception through commission
69
70
data fabrication
When instead of recording what really happened in a study, researchers invent data that fit their hypothesis
71
Plagiarism
Representing the ideas or words of others as one’s own
Violation of ethics
72
data falsification
Occurs when researchers influence a study’s results perhaps by selectively deleting observations from a data set or by influence their research subjects to act in the hypothesized way
73
Self-plagiarism
Researchers recycle their own texts
73
conceptual definition
Or construct
Researchers definition of the variable in question at a theoretical level
74
self-report measure
Operationalizes a variable by recording people’s answers to questions about themselves in a questionnaire or interview
Ex five item scale, self report measures about life satisfaction
75
observational measure
Operationalizes a variable by recording observable behaviors or physical traces of behaviors
Ex operationalizing happiness = how many times a person smiles
76
physiological measure
Operationalize a variable by recording biological data, such as brain activity, hormone levels of heart rate
Usually requires the use of equipment, FMRI, ekg
76
Operational variables classified as
categorical variable,
Levels = Categories
Ex = gender, levels are male and female
77
quantitative variable
Levels are coded with meaningful numbers
ex : height and weight
78
three types of quantitiative variables
- ordinal scale
- interval scale
- ratio scale
79
ordinal scale
A measurement applies when the numerals of a quantitative variable represent a ranked order
Intervals may be unequal
Ex top 10 books, what is difference between book 1 sales, and book 3
Ex - gold, silver, bronze
80
interval scale
Numerals of a quantitative variable that meet two conditions
Numerals represent equal intervals between levels
No true zero
Ex - IQ no true zero, because person with IQ zero does not mean a person has no intelligence, shoe size
81
ratio scale
Numerals of a quantitative variable have equal intervals
Value of 0 truly means none of the variable is measured
Ex test grades, height
82
Construct validity of a measure has two aspects
Reliability
How consistent the results of a measure are
Validity
Whether the operationalization is measuring what it is supposed to measure
83
thee types of reliability
- test retest reliability
- interrater reliability
- internal reliability
84
test-retest reliability
A study participant will get pretty much the same score each time they are measured with it
85
interrater reliability
Consistent scores are obtained no matter who measures the variable
86
internal reliability
A study participant give a consistent pattern of answers, no matter how the researchers phrase the question
87
correlation coefficient
R
Single number
Indicates how close the dots, or points on a scatter plot are to a line drawn through them
88
slope direction
Can be positive, negative or zero
89
strength
Relationship is strong when dots are close to the line
Weak when dots are spread out
90
Average inter - item correlation (AIC)
Average of all these correlations
An AIC between .15 and .50 means that the items go reasonably well together
91
Cronach’s alpha
Mathematically combines the AIC and the number of items in the scale
Closer to 1.0 the better the scale’s reliability
92
Face validity
Subjectively considered to be a plausible operationalization of the conceptual variable in the question
93
Content validity
The extent to which a measure captures all parts of a defined construct
94
Known groups paradigm
Researchers see whether scores on the measure can discriminate among two or more groups whose behavior is already confirmed
95
Criterion validity
Evaluates whether the measure under consideration is associated with concrete behavioral outcome that it should be associated with, according to the conceptual definition
96
convergent validity
A theoretical test of the extent to which a self report measure correlates with other measures of a theoretically similar construct
97
Discriminant validity
A theoretical test of the extent to which a self report measure does not correlate strongly with measures theoretically dissimilar constructs
98
Survey
A method of posing questions to people on the telephone in person etc
Also means Poll
98
Open ended question
A survey question format that allows respondent to answer any way they like
99
Forced choice question
A survey question format in which people give their opinion by picking the best of two or more options
100
Likert scale
People are presented with a statement and are asked to use a rating scale to indicate their degree of agreement
Each response value is labeled with specific terms, strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree
101
Semantic differential format
A survey question using a response scale whose numbers are anchored with adjectives
example = rate my professor
Prof is bad 1 2 3 4 5 great professor
101
Leading question
Wording of a question leads people to a particular response
Weakens construct validity
102
Double barreled question
Asks two questions in one
Have poor construct validity because ppl might be responding to the first half the question, the second half or both
“Do you agree that the Second Amendment to our United States Constitution guarantees your individual right to own a gun and that the Second Amendment is just as important as your other Constitutional rights?”
103
Negatively worded question
A question in a survey or poll that contains negatively phrased statements, making its wording complicated or confusing and potentially weakening its construct validity
Example abortion should never be restricted
104
Acquiescence
Potential response set
Yea saying
Say yes or strongly agree to ever item
104
Response set
A shortcut respondent may use to answer survey questions
A consistent way of answering all the questions
105
Fence sitting
Playing it safe by answering in the middle of the scale, especially when survey items are controversial
May try to get ppl to not do this by taking away the middle option
106
Socially desirable responding
When respondent give answers that make them look better than they really are
Responses decrease construct validity
107
Faking bad
Giving an answer on surveys that make one look worse than they really are
107
Faking good
Idea that respondents are embarrassed, shy or worried about giving an unpopular opinion, they will not tell the truth
108
Observational research
When a researcher watches people or animals and systematically records how they behave or what they are doing
109
Observer effect
Observer inadvertently change the behavior of those they are observing, such that participant behavior changes to match observers expectations
109
Observer bias
Occurs when observer’s expectations influence their interpretations of the participants behavior or the outcome of the study
110
Masked design
In which the observers are unaware of the purpose of the study and the conditions to which participants have been assigned
111
Reactivity
Change to behavior when study participants know another person is watching
Might react by being on their best behavior or worst
112
Bivariate Correlations
Associations that involves exactly 2 variables
3 types of associations
1. postive
2. Negative
3. zero
112
Unobtrusive observation
Make yourself less noticeable
Sit behind a one way mirror
Act like a causal onlooker
113
Correlation coefficient
r indicates direction and strength of the strength
- Direction (postive or negative)
-strength (how closely related the 2 variables are)
----more closely related r=1.0 or -1.0
----weaker = closer to 0
- good for two quantitative variables
114
Scatterplot
- can be used for categorical variables
- values fall in one catagory or another
ex= martial satisfaction and online dating
- meets spouse online of offline
- marital satisfaction is quantitative
- can use scatterplot
- one variable plotted on x axis other on y axis
- one person = dot
115
Bar graph
- can also be used for categorical variable
- levels of the bar reflect the mean (average) within each group
- examine the difference between the groups average to see whether there is an association
116
Correlational Study
a study is correlational if it has two measured variables
- the data can be plotted as a scatter plot or a bar graph
- the reported results may be a correlation coefficient or a difference between means
ex = meaningful conversation linked to happier people, dating apps are making marriages stronger
117
interrogating association claims
2 MOST IMPORTANT WITH Association
1. construct validity - how well is each variable measured?
2. Statistical Validity - how well does the data support the conclusion? the extent to which statistical conclusions are precise, reasonable and replicable
118
point estimate
the value that is the result of your analysis
119
Effect size (statistical validity Question 1)
describes the strength of an association
- all else being equal, larger effect sizes are ore important, but small effect sizes can compound and also be important
(0.5. -0.5) - very small or very week
(.20, -.20) moderate
(0.40, -0.40) - unusually very large in psychology, either very powerful or possibly too good to be true
120
Confidence Interval (statistical validity question 2)
- margin of error of the estimate
- how precise is the estimate?
- a range designed to include the true population value a high proportion of the time (usually 95%)
- does the confidence interval include zero?
- if it does not the relationship is statistically significant
- if it does include zero, the relationship is NOT statistically significant
- smaller sample sizes result in wider CIS (less precise)
- larger sample sizes result in narrower CIs (more precise)
121
Has it been replicated? (statistical validity question 3)
- conducting the study again, making sure replications prove the same thing
122
Could outliers be affecting the assoc.? ( Statistical validity question 4)
- outlier- an extreme score, single case that stands out from the rest of the data
- outliers can make correlations appear stronger
- more problematic when they have extreme values on both variables
123
Is there a restriction of range (statistical validity question 5)
- when there is not a full range of scores on one of the variables
- can make the association appear weaker than it really is
- is there a relationship between
exercise and well being?
- study people in a runner's club to see whether time spent running associated with higher levels of happiness
- ppl in running club are going to run a certain amount per week
- relationship between SAT scores and college GPA
- already SAT scores restricted to get into college
123
Is the association curvilinear? (Statistical validity question number 6)
- curvilinear association -correlation coefficient is close to zero, relationship between 2 variables is not a straight line, positive up to a point then negative
- can be detected using scatterplots
124
Internal Validity and association claims
- not necessary to interrogate internal validity for an association claim, but we need to protect ourselves from temptation to make a causal inference
need 3 things for causal claim
1. covariance
2. temporal precedence
3. internal validit
125
Potential third variables
- you may be able to identify serveral third variables that could potentially explain a bivariate association
- the third variable must correlate with both variables in the association
ex = association between height and hair lenght
- gender may be impact, taller= boys, less hair
126
Spurious association
- bivariate correlation is there but only because of some 3rd variable
126
Causal Claim
- use powerful verbs like, makes influences, and effects, stating something about interventions and treatments
127
Experiment
researchers manipulated at least one variable and measured another, can take place just about anywhere
128
manipulated variable
variable that is controlled, researchers assign participants to random value or variable
- self reports, behavioral observation, psychological measures
129
independent variable
manipulated variable
130
dependent variable
- measured variable, outcome variable, how partcipants are recorded on depent variable based on assigned indep.
131
control variables
any variable held constant on purpose
132
how do experiment support causal claims
- 1. covariance - comaprison group vs control group
2. temporal precedence - casual variable first
3. internal validity - explored alternative explanations
133
confounds
- alternative explanations, threats to internal validity
134
design confound
experimenters mistake, occurs when a 2nd variable happens to vary alongside indep. variable
= poor internal validity, cannot support a causal claim
135
unsystematic variability
certain babies like music more, okay if not all in the same gorup systematically
136
selection effects
can happen when let participants choose groups, or when if experimenters assign one type of person to a specific group
137
Random assignment
used to avoid selection effects, a way of desystematizing types of participants that end up in each group
ex - match groups, top partcipants paired, 2 best paired, and so on x
138
independent group design (also called between subjects design or bw groups design)
- separate groups of participants are placed into different levels of the participants
2 basic forms of indep group design
1. posttest only design
2. pretest/post test design
139
within groups design/within subjects design
- each person is presented with all levels of the independent variable
1. repeated measures design
2. concurrent measures design
140
pretest/ post test design
- participants are randomly assigned to at least 2 groups, and are tested on the key dependent variables twice
- one before, once after exposure to the independent variable
- may use a pretest.post test design when they want to be sure random assignment made groups equal
- can be absolutely sure, no selection effects
- enables researchers to track people's change in performance over time
141
post test - only design
- participants are randomly assigned to independent group and are tested on the dependent variable once
141
repeated measures design
- participants are measured on a dependent variable more than once after each exposure to each level of the IV
- participants experience both levels
one group -> tast chocolate with the confederate -> rate chocolate-> taste chocolate alone -> rate chocolate
142
concurrent measures design
- participants are exposed to all levels of the IV, at roughly the same time, a single attitudinal behavioral preference is the DV
one group -> shown female face and male face -> track looking preference
143
Advantages of within groups design
- ensures participants in the 2 groups will be equivelant because same partcipants
- matched groups design can be treated as within groups
- requries fewer participants
144
Within groups design make casual claims?
1. covariance?
within groups design allow researchers to manipulate an IV and incorporate comparison groups
2. Temporal precedence?
researcher controls IV, can make sure it comes first
3. internal validity
don't have to worry about selection effects because participants exactly the same in the 2 conditions
- do need to avoid design confounds
- order effects
145
Order effects
- when exposure to one level of IV, influences responses to the next level
- behavior at later levels may be caused not by experimental manipulation but rather sequence
- can include practice effects and carryover effects
145
Practice effects
- long sequence may lead participants to get better at a task or to get bored or tired at the end
146
carryover effects
- some form of contamination carry over from one to another condition
147
Avoid order effects by counterbalancing
- present the levels of IV to participants in difference sequences, with counterbalancing any order effects should cancel each other out
ex - full counterbalancing, partial counterbalancing
148
full counterbalancing
- all possible conditions are represented, repeated measures design w/2 conditions is easy to counterbalance (A -> B) (B-> A)
- as number of conditions increase so does combo
149
Partial counterbalancing
which only some of the possible condition orders are represented
- present conditions in random order
- latin square
150
Disadvantage of within groups design
1. repeated measures have potential for order effects
- can threaten internal validity
- can usually control by counterbalancing
2. within groups design may not possible
ex - once taught a skill can't reteach the skill
3. when people see all levels of IV, and then change the way they would normally act
demand characteristic
151
151
Demand Characterisitc
- a cue that can lead participants to guess an experimenters hypothesis
152
pretest/post test vs repeated measures
- pretests vs posts test - participants see only one level of IV, not all levels
repeated measures- participants exposed to all levels of meaningful IV
- levels can be counterbalanced
153
interrogating causal claims w/4 big validities
1. construct validity
construct validity of dependent variable
- ask how well researcher measured the dependent variable
construct validity of indep. variables
- ask how well the researchers manipulated or operationalized them
- manipulation check
- pilot study
2. External validity - you ask whether the casual relationships can generalize to other, people, places, and times how were participants
how were participants recruited?Random sampling?
3. Statistical validity - asking about effect size, precision of the estimate, and replication
4. Internal Validity - often the priority, if a study had internal validity --> causal claim
if a study has confounds -> Not a casual claim
154
Manipulation check
extra dependent variable that researchers can insert into an experiment to convince them that their experiments manipulation worked
155
pilot study
- simple study, using a separate group of participants, that is completed before/after the primary test, to confirm effectiveness of manipulation
156
Internal Validity - Priority, how many threats?
12 internal validity threats
1. design confounds
2. selection effects
3. order effects
157
4. Maturation threat
- a change in behavior that occurs/emerges more or less spontaneously ver time
preventing: pretest/post test design, included age, comparison, etc
158
5. History Threats
- "historical" or external factors that systematically effect most members of the treatment group, at the same time as the treatment itself, making it unclear if change is caused by the treatment received
preventing - comparison groups can help
159
6. Regression threats
refers to a specific concept called regression to the mean, an unusually good performance is likely to regress down, towards the mean, unusually bad performance likely to regress up to the mean
- can occur only when a group is measured twice, and a group had an extreme score in the pre test
preventing - comparison groups, careful inspection of the pattern of results
159
7. Attrition threats
- pretest/post test, a threat that occurs when a systematic type of participant drops out of the study before it ends
- preventing = when participant drops out of a study, remove there pre - test score
160
8. Testing threat
- change in the participants as a result of taking a test more than once, people may have become more practiced with the test, or more bored/fatigued
- preventing= abandon a pretest all together, only do post test, or if they do a pre test use alternative forms of measurement
161
9. instrumentation threat
- occurs when a measuring instrument changes over time, ex = a person coding behavior changes, or use different forms of pretest/post test that are not equivalent
preventing = researchers switch to post test only design, or make sure pretest/post test is equivalent
162
combo threats selection history threat
an outside event or factor only affects those at one level of the IV
163
combo threat - selection attrition threat
- only one of the experimental groups experiences attrition
164
observer bias
researchers expectations influence there interpretations of the results, can threaten internal validity and construct validity
solution = double blind study, or masked design
165
double blind study
neither the participants or researcher who evaluates them knows who is the treatment group and who is the comparison group
165
Three potential internal validity threats to ANY study even with clear comparison groups
1. observer bias
2. demand characteristics
3. placebo effect
166
masked design
participants know what group they are in, but the observer does not
167
double blind placebo control study
- neither people treating or participants, know whether they are in the real group or placebo
167
placebo effect
- people receive a treatment and really believe they improve, but only because participants believe they are experiencing real treatment
solution = double blind placebo control study
168
Null effects
finding that an independent variable, did not make a difference int eh dependent variable, no significant covariance between the 2
169
reasons for the null effects
- weak manipiulations
- insensitive measures
- ceiling effect
- floor effect
- noise
- measurement error
170
weak manipulations
not enough to matter, important that researcher operationalized the indep. variable well
171
insensitive measures
- sometimes researchers have not operationalized the dependent variable with enough sensitivity, important to have detailed, quantitiative increments
172
ceiling effect
- all the scores are squeezed together at the high end
173
floor effect
- all the scores are squeezed together at the low end
174
noise
unsystematic variability among the members of a group in an experiment
175
measurement error
- a human or instrument factor that can randomly inflate or deflate a persons true score
solution 1 = use reliable, precise tools
solution 2 = measure more instances
176
solutions to individual differences
- change the design
- add more participants
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situation noise
- external distractions
- carefully control environment
- one of the reason lab are sterile and boring
178
power
- aspect of statistical validity
- likelihood that a study will return an accurate result when the independent variable really had an effect
179
Multivariate design
Involved more than two measured variables
180
Longitudinal design
A study in which the same variable are measured in the same people at multiple points in time
180
Cross sectional correlation
See whether two variables measured at the same point in time are correlated, ex overvaluation time 1
Narcissism time 1
181
Autocorrelation
Correlation of one variable with itself measured at two times
narccism time 1 - narccism time 2
182
Multiple regression
A statistical technique that computes the relationship between a predictor and a criterion variable controlling for other predictor variables
Can help rule out third variables
Addressing internal validity concerns
182
Cross lag correlation
Shows whether the earlier measure of one variable is associated with the later measure of the other variable
This is what researchers are usually most interested in
183
Control for
Holding a potential third variable at a constant level, while investigating the association between two other variables
184
Criterion variable
Dependent variable
Variable in a multiple regression analysis that the researchers are most interested in understanding or predicting
185
Predictor variable
Independent variables
Variable in multiple regression that is used to explain the variance in the criterion variable
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parsimony
Degree to which a scientific theory provides the simplest explanation of some phenomenon,
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Interaction effect
A result from a factorial design, in which the difference in levels of one IV changes depending on the levels of the other IV
A difference in differences
Also called interaction
Look for lines to cross
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Mediator
A variable that helps explain the relationship between two other variables
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Factorial design
A study in which there are two or more independent variables (factors), researchers cross the two independent variable, and study each possible combination of the iV
Ex - phone use (yes no), age (old, young)
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Cell
Condition in an experiment, a cell represent one of the level of the IV
participant variable
Variable whose levels are secluded (measured), not manipulated
Ex - age, gender, ethnicity
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Marginal means
Arithmetic means for each level of the IV, averaging over level sof the other IV
If the sample size in each cell is exactly equal marginal mean is simple average, is sample size is not equal, weighted average
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Main effect
The overall effect of one IV on the DV, averaging over the levels of the other IV
Simple difference
In factorial design with two independent variable there are two main effects
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Population
Entire set of people or products in which you are interested in
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Sample
Smaller set taken from the population
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census
A set of observations that contain all members of the population interest
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Biased sample
Unrepresentative sample
Some members of the population of interest have a higher probability than other members of being included in the sample
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Unbiased sample
Representative sample
All members of the population have an equal chance of being included in the sample
Only unbiased samples allow us to make inferences about the population of interest
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Convenience sample
Biased sample
Using a sample of people who are easy to contact and readily available to participate
Ex - psychology professors using psych students in class
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Self selection
Biased sample
When a sample is known to contain only people who volunteer to participate
Can cause a serious problem for external validity
Leave review on amazon, rate my professor, stronger opinions
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Probability sampling
Category name for random sampling
Random sampling
Every member of the population has an equal chance of being selected
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simple randoms sampling (random sampling 1)
Most basic form of probability sampling
Sample is chosen at random from population of interest
Ex - out of a hat, or random name generator
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systematic sampling (random sampling 2)
Probability sampling technique in which the researcher uses a randomly chosen N and counts off every Nth member of population to achieve a sample
Count the 4th person and then pick every 7th
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Multistage sampling (random sampling 4)
Two random sample are selected, a random sample of clusters, and then a random sample of people within those clusters
Ex - HS in PA, 100 chosen, and then pick 15 kids from each of the HS
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Cluster sampling (random sampling 3)
Option when people are already divided into arbitrary groups
Clusters of participants within a population of interest are randomly selected and then individuals in each selected cluster are used
Ex HS in PA, 100 hs chosen, use all people from 100 hs
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oversampling (random sampling 6)
Researcher intentionally overrepresents one or more groups
Adjusts the final results so members in the oversample group are weighted to their actual proportion in the population
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Stratified sampling (random sampling 5)
Researcher purposefully selects particular demographic categories, or strata, and theme randomly selected individual within each of the categories, proportionate to their assumed membership of the population
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Random assignment
Used only in experimental designs
When researchers want to place participants into two different groups they usually assign them at random
Random assignment enhances internal validity
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Non Probability sampling
Technique involve nonrandom sampling and result in biased results
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convenience sampling (non probability sampling 1)
Using participants who researchers have easy access to, example psych professors using psych students
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Purposive sampling
(non random sampling 2)
A bias sample technique in which only certain kinds of people are included in the sample
Ex - want to study smokers, only include smoker
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Quota sampling
(non probability sampling 4)
Researcher identifies subsets of the population of interest and then sets target number for each category in the sample
But unlike stratified random sampling they are selected by convenience or purposive sampling
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Snowball sampling (non probability sampling 3)
Participants are asked to recommend a few acquaintances for the study
Ex people with crohn's disease, recommend 2 people in support group
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Quasi experiment
- Differ from true experiments in that the researchers do not have full experimental control
- They start by selecting an IV and a DV, then they study participants who are exposed to teach level of the IV
- Researchers don’t randomly assigns participants to one level or another, teachers, political regulations, act or nature or even by their own choice assign them
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Quasi independent variable
A variable that resembles an IV but the researcher does not have full control over it
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Nonequivalent control group posttest only design
Participants were not randomly assigned to groups and were tested only once after exposure to one level of the IV or the other
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Nonequivalent control group pretest/posttest design
A quasi experiment that has at least one treatment group and one comparison group, in which participants have not been randomly assigned to the two groups and in which at least one pretest and one posttest is administered
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Interrupted time series design
A quasi experiment in which participants are measured repeatedly on a dependent variable before, during and after “interruption” caused by some event
Ex suicide rate measured before, after and during release of show 13 reasons why
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Waitlist design
A way researchers control for selection effects
All participants plan to receive treatment but are randomly assigned to do so at different times
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Small n design
A study in which researcher gathers information from just a few cases
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Stable baseline design
A type of small n design in which a researcher observes behavior for an extended baseline period before beginning a treatment or other intervention and continues observing behavior after intervention
If before treatment a baseline is stable can be more certain treatment had the effect
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Multiple baseline design
- Type of small n design
- Researchers stagger their introduction of an interventions across a variety of individual, times of situations to rule out alternative explanations
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Reversal design
- Researchers observe a problem behavior both with and without treatment, but take away the treatment for a while (reversal period) to see whether the problem behavior returns (reverses)
- Subsequently reintroduced the treatment to see if behavior improves
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Single N design
A study in which researchers gather information from only one animal or person
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Replicable
Get the same results if they conduct the same study again
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Direct replication
- Researchers repeat an original study as closely as they can to see whether the effect is the same in the newly collected data
- Problem - any threats to internal validity or flaws in construct validity in the original study, such threats would be repeated in the direct replication
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conceptual replication
Researchers explore the same research question but use different procedures, the conceptual variable in the study is the same but the procedures for operationalizing the variables are different
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Replication plus extension
Researchers replicate their original experiment and add variables to test additional questions
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File drawer problem
Idea that meta analysis may be overestimating the true size of an effect because negligible effects or even opposite effects, have not been included in the process
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Meta analysis
A way of mathematically averaging the effect sizes of all the studies that have tested the same variable to see what conclusions that whole body of evidence support
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HARking
Hypothesizing after the results are known
A questionable research practice in which researchers create an after the fact hypothesis about an unexpected research result, making it appear as if they predicted it all along
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P hacking
A family of questionable data analysis technique such as adding participants after the results are initially analyzed, looking for outliers, or trying new analyses in order to obtain a p value lower than 0.05
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Open science
The practice of sharing ones data, hypotheses and materials freely so others can collaborate, use and verify results
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Open data
Psychologists provided there full data sets on the internet so other researchers an reproduce the statistical results or even conduct new analyses on it
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Open material
Psychologists provide their studies full set of measures and manipulations on the internet so others can see the full design or conduct replication studies
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Preregistration
In a study before collecting any data, the researcher has stated publicly what the studies outcome is supposed to be
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Ecological validity
A study’s similarity to real word contexts
External validity
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Theory testing mode
A researchers intent for a study, testing association claims or causal claims to investigate support for a theory
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Generalization mode
The intent of researchers to generalize the findings from the samples and procedures in there studies to other populations or contexts
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Cultural psychology
Subdiscipline of psychology focusing on how cultural contexts shape the way a person thinks, feels and behaves
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Field setting
A real world setting for a research study
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Experimental realism
The extent to which a laboratory experiment is designed so that participants experience authentic emotions, motivation and behaviors
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