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PSYCH 225: Research Methods > Midterm > Flashcards

Midterm Flashcards

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1
Q

availability heuristic

A

things that come to mind easily tend to guide our thinking

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2
Q

present/present bias

A

we often fail to think about what we cannot observe (ex: coincidences)

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3
Q

confirmation bias

A

tendency to look only for info that agrees with what we already believe

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4
Q

bias blind spot

A

belief that we are unlikely to be biased

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5
Q

empiricism

A

using evidence from the senses (or instruments that assist the senses) as the basis for conclusions (ideas/intuitions checked against reality)

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6
Q

research question

A

question researcher seeks to answer (expressed in terms of the variables)

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7
Q

inspiration for research questions:

A

-Informal observations
-Practical problems
-Previous research

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8
Q

theory

A

a coherent explanation or interpretation of one or more phenomena
-Functional (why)
-Mechanistic (how)

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9
Q

theory-data cycle

A

theory => research question(s) => research design => hypotheses => data => supports & strengthens OR doesn’t support & revises theory/design

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10
Q

hypothesis

A

an empirically testable proposition about some fact/behavior/relationship, usually based on theory, that states an expected outcome resulting from specific conditions or assumptions

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11
Q

basic research

A

conducted primarily to gain a better understanding of phenomena

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12
Q

applied research

A

conducted primarily to address a practical problem

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13
Q

translational research

A

uses the lessons from BASIC research to develop & test APPLICATIONS to healthcare, psychotherapy, treatments, or interventions

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14
Q

basic-applied research cycle

A

Basic research => Applied research => Translational research

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15
Q

peer review cycle

A

1) Author submits manuscript of journal (can suggest certain people to review or not to review)
2) Editors assess the manuscript (rejects, transfers, or sends to reviewers)
3) Reviewed (single-blind, double-blind, transparent, open)
4) Editor addresses comments => Author makes revisions => Editor assesses again
5) Finally rejected, transferred, or accepted

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16
Q

empirical papers

A

-Report of an original study
-Abstract, intro, methods, results, & discussion
-Quantitative info

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17
Q

review article

A

-Qualitative review of the scholarly lit on a topic
-Draw conclusions about trends, controversies, & future directions
-“Review” in title

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18
Q

meta-analysis

A

-Quantitative review of the evidence on a topic (statistical techniques to evaluate weight of evidence)
-“Meta-analysis” in title
-May be one component of a paper

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19
Q

theoretical article

A

-Describes a theory or model of a psychological process in detail
-Integrates empirical & theoretical findings to show how a theory of a model can help guide future research

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20
Q

opinion/perspective/thought piece

A

-Drawing on recent empirical research
-Formulates an opinion about a controversy, important findings, or a disagreement in a theoretical foundation, methodology, or application

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21
Q

questions for evaluating a research question

A

-Is it ethical?
-Is it interesting?
-Is it important?
-Is it feasible?

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22
Q

conceptual variable

A

abstract concept/construct

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23
Q

operational variable

A

describes the way of measuring or manipulating the variable

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24
Q

operationalization

A

process of starting with a conceptual variable & creating an operational variable

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25
Q

measured variable

A

variation is observed & recorded

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26
Q

manipulated variable

A

variation is controlled by researcher

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27
Q

what determines if a variable is measured or manipulated?

A

-Some can ONLY be measured
-Some cannot be manipulated ETHICALLY
-Some can be either measured OR manipulated

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28
Q

frequency claims

A

describe the rate or degree of a single, measured variable

contains a percentage, number, or rate/time phrase

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29
Q

association claims

A

argues that one level of a variable is likely associated with the particular level of another variable (probabilistic)

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30
Q

causal claims

A

argues that one variable is responsible for changing the other

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31
Q

requirements to support a causal claim:

A

-Covariance (change in 1 associated with change in other)
-Temporal precedence (directionality)
-Internal validity (are other explanations ruled out?)

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32
Q

causal claim variables

A

independent variable (manipulated)
dependent variable (measured)

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33
Q

association claim variables

A

predictor variable (~IV)
criterion/outcome variable (~DV)

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34
Q

construct validity

A

how well is a conceptual variable operationalized? are you measuring what you think you are?

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35
Q

external validity

A

how well do the results generalize?
-To other people
-To other settings/situations/contexts

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36
Q

statistical validity

A

how well does the data support the conclusions? what is the likelihood that the results were found by chance?

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37
Q

internal validity

A

are alternative explanations sufficiently ruled out by the study’s design?

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38
Q

naturalistic observation

A

observing individuals’ behavior in the environment in which it typically occurs

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39
Q

case studies

A

in-depth examinations/observations of an individual (or a few)

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40
Q

structured observation

A

observations made of specific behaviors in a somewhat controlled setting

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41
Q

ethogram

A

inventory of operational definitions of behaviors, used when collecting observation data

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42
Q

state (observations)

A

recording the duration of a behavior

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43
Q

event (observations)

A

record the number of occurrences (behavior treated as instantaneous)

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44
Q

focal sampling

A

record observations of ONE individual
-good for obtaining info of subtle or rare behaviors

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45
Q

scan sampling

A

recording behaviors of multiple individuals at once
-predetermined interval

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46
Q

reactivity

A

individuals change their behaviors when they know they’re being watched

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47
Q

observer/expectancy effects

A

observers subconsciously change the behavior of those they are observing

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48
Q

observer bias

A

observer’s expectations influence their interpretation of behaviors

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49
Q

validity

A

accuracy & reliability

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50
Q

reliability

A

necessary for validity, but not sufficient

consistency of measurements

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51
Q

face validity

A

measure is subjectively a plausible operationalization of the conceptual variable

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52
Q

content validity

A

measure captures all parts of the defined construct

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53
Q

criterion validity

A

measure is associated with a concrete behavioral outcome that is logical

54
Q

known-groups paradigm

A

test whether scores can discriminate among groups whose behavior is already confirmed

55
Q

convergent validity

A

measure is most strongly relate to measures of similar constructs

56
Q

discriminant validity

A

measure is not strongly associated with measures of dissimilar constructs

57
Q

interrater reliability

A

the degree to which 2+ coders/observers give consistent ratings of a set of targets

58
Q

fixes for low interrater reliability:

A

-Revised codebook/ethogram
-More training
-Throw out inconsistent behaviors

59
Q

test-retest reliability

A

assesses whether scores are consistent each time they’re measured

60
Q

internal reliability

A

assesses whether answers are consistent no matter how the question is phrased

61
Q

cohen’s kappa

A

common measure for interrater reliablity

62
Q

cronbach’s alpha

A

correlation measure typically used for internal reliability

63
Q

when to assess validity & measures

A

Before used to test a hypothesis

64
Q

pros & cons of surveys:

A

Pros
-Can be very accurate
-Sometimes the only way to assess a variable

Cons
-Can be sensitive to the way that the questions are asked (order, phrasing, scales)

65
Q

forced-choice questions

A

need to choose between 2+ options (only one)

66
Q

open-ended questions

A

can answer in a free-write way

67
Q

likert scale questions

A

strongly agree to strongly disagree

68
Q

semantic differential questions

A

number rating from one adjective to another

69
Q

primacy effect

A

more likely to remember words at the beginning of a list

70
Q

recency effect

A

most recently presented items will most likely be remembered best

71
Q

leading questions

A

biases people to answer in a certain way

72
Q

double-barreled questions

A

actually asking 2 questions

73
Q

negatively worded questions

A

uses double-negative phrasing (confusing)

74
Q

how should questions be ordered?

A

Broad to focused

75
Q

response sets/non-differentiation

A

people respond the same way to ALL questions

76
Q

acquiescence response set

A

responding with “agree” or “strongly agree” to everything

solved with reverse-worded questions

77
Q

fence-sitting response set

A

respondent “plays it safe” by always answering in the middle of the scale

solved with no neutral option, even number of response options, or forced-choice questions

78
Q

socially desirable responding

A

respondents give answers to make them “look better” than they really are

solved with anonymity, removing based on target questions

79
Q

biased sample

A

some members of the population of interest have a higher probability of being included in the sample than others

80
Q

confidence interval (CI)

A

a range of values, indicated by a lower & upper value, that is designed to capture the population value for an estimate (describes the uncertainty of an estimate)

81
Q

margin of error

A

half the width of the entire confidence interval

82
Q

correlational statistics

A

can be used in studies testing all types of claims

83
Q

correlational design

A

tests an association claim

84
Q

bivariate correlation

A

an association involving 2 variables

85
Q

common uses of correlational designs:

A

-How 2 variables relate within individuals
-How 2 variables relate between different individuals
-How a variable of an individual relates to a variable of the environment

86
Q

statistical validity topics for association claims:

A

-Strength
-Precision
-Significance (statistical)
-Replication
-Outliers
-Restriction of range
-Curvilinear

87
Q

assessing relationship strength

A

Direction (+, -, or 0)
Strength (magnitude of r)
R^2 (variance of Y that is accounted for by X)

88
Q

measures of precision:

A

-Confidence interval
-Margin of error

89
Q

primary indicator of precision

A

sample size (larger = more precise)

90
Q

probability estimate (p)

A

what is the likelihood of finding this correlation by chance?

91
Q

null hypothesis significance testing (hypotheses)

A

Hypothesis => Effect of manipulation; Difference between groups, Correlation btwn variables

Null Hypothesis => No effect; No real difference; No correlation

92
Q

NHST possible scenarios

A

-True positive (data indicates hypothesis is true & it is)
-False negative/Type II error (data indicates hypothesis is false, but it is true)
-True negative (data indicates the null is true, & it is)
-False positive/Type I error (data indicated the null is false, but it is true)

93
Q

outlier

A

a score that is either much higher or much lower than most of the other scores in a sample

can drastically change r & have larger effect when small sample size

94
Q

causes of extreme values:

A

-Chance
-Measurement error
-Instrument error
-Human error
-Unmeasured (third) variable
-Incomplete theoretical foundation

95
Q

how to deal with extreme values:

A

-No definitive rules for what is an outlier
-Quantitative ways to test if a single point has a disproportionate influence on an association
-Can report results of statistical analyses with & without outliers
-May talk about in results & discussion sections

96
Q

restriction of range

A

is there isn’t a full range of scores in one variable, the correlation can appear smaller than it truly is

97
Q

how to solve restriction of range:

A

-Recruit individuals at the ends of the spectrum
-Statistical techniques can help correct

98
Q

multiple/multivariate regression

A

calculates the proportion of total variability that is due to the effect of different variables

helps rule out third variables/control for them

99
Q

beta

A

similar to r, but describes the strength & direction between 2 variables when one or more variables are controlled for

100
Q

regression tables

A

-Show beta values of all predictors
-Can compare relative importance of variables
-No standard guidelines for strong/moderate/weak
-p-value of beta => probability that the beta came from a population in which the relationship is 0

101
Q

third variables

A

A & B only appear related because C causes both A & B

102
Q

mediation variables

A

a variable that helps explain the relationship between 2 other variables (A & B are related because A leads to C which leads to B)

103
Q

moderation variables

A

A variable that, depending on its level, changes the relationship between 2 variables (A & B are related for one type of C, but not for another type of C)

104
Q

covariates

A

variables being “controlled for”

105
Q

when to use scatterplots:

A

correlation between two QUANTITATIVE variables

106
Q

when to use histograms/bar graphs:

A

correlation between a categorical & a quantitative variable

107
Q

when to use a double bar graph/histogram:

A

correlation between 2 CATEGORICAL variables

108
Q

longitudinal designs

A

provide evidence for temporal precedence by measuring the same variables in the same subjects/participants at several points in time

109
Q

associations calculated in longitudinal designs:

A

-Cross-sectional
-Autocorrelations
-Cross-lag

110
Q

cross-sectional correlations

A

test whether 2 variables, measured at the same point in time, are correlated

111
Q

problems with cross-sectional correlations:

A

-Don’t establish temporal precedence
-Cohort effects

112
Q

cohort effects

A

differences in generations/ages due to time-periods

113
Q

autocorrelations

A

test the correlation between one variable & itself, tested at 2 different time points

114
Q

cross-lag correlations

A

a correlation between an earlier measure of one variable & a later measure of another variable

115
Q

why can’t longitudinal & multiple regression establish causation?

A

-Multiple regression lacks temporal precedence
-Longitudinal designs still have a third-variable problem

116
Q

evidence-based treatments

A

therapies based on research

117
Q

replication

A

study conducted again to test reliability

118
Q

falsifiability

A

a hypothesis that, when tested, could fail to support therapy

119
Q

universalism

A

claims are evaluated according to merit, independent of researcher’s credentials/reputation

120
Q

community (scientific norm)

A

scientific knowledge is created by a community & its findings belong to said community

121
Q

disinterestedness (scientific norm)

A

scientists strive to discover the truth & it will not be swayed by a scientist’s own beliefs

122
Q

organized skepticism (scientific norm)

A

question everything, including own theories, widely accepted ideas, & “ancient wisdom”

123
Q

self-report measure

A

recording people’s answers to questions about themselves in a questionnaire/interview

124
Q

observational measure

A

recording observable behaviors or physical traces of behaviors

125
Q

physiological measures

A

recording bological data

126
Q

categorical variable

A

categories; nominal

127
Q

quantitative variables

A

coded with meaningful numbers

128
Q

ordinal scale

A

numerals represent a rank order

129
Q

interval scale

A

numerals represent equal intervals between levels with no “true zero”

130
Q

ratio scale

A

numerals have equal intervals & a “true zero”

131
Q

effect size

A

strength of relationship between 2+ variables

132
Q

parsimony

A

degree to which a scientific theory provides the simplest explanation of some phenomenon

PSYCH 225: Research Methods (3 decks)

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