PSYC 2100WQ Terms, Lecture Exam #1

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54 Terms

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Empirical science

Science based in numerically represented observations of our five senses

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construct

provides a definition of what is to be observed before data collection

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Operationalization

how a construct is to be observed and numerically recorded

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statistic

numeric summary of numerically recorded observations

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Variable

event/object/attribute where numeric observations show variation

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covariation

the variation of one thing is shown to be related to the variation of another thing

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Simple covariation

Absence of specified order in covariation (use non-experimental designs)

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Ordered covariation

Demonstrates an independent/dependent variable relationship (aka causality, use experimental design)

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Data

numerically recorded observation

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Hypothesis

A formal and public statement/declaration about what a researcher things is true

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Knowledge claim

based on observations consistent with hypothesis

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causality

for every cause, there is an effect, and for every effect, there is a cause

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Population

unspecified N# of individuals for which we have numeric observations, numeric summaries only exist in theory

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sample

participants drawn from a population

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Descriptive statistic

Used to describe a sample

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Inferential statistics

Use numeric observations about the sample to make inferences about the population from which the sample was drawn

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skewness

in reference to a frequency distribution, when the line departs from what is bell shaped

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Standard deviation

indicates average variation of scores from the mean

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case study

non-experimental design where n=1, used when permission is difficult to obtain

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trial

indicates in advance where and when data are going to be collected

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Naturalistic observation

record observations in real-word (natural) settings (can include non-humans)

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Experimental designs

designs in which the independent variable is manipulated in order to identify causal relationships

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control group

group of participants not exposed to IVxperime

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experimental (treatment) group

group of participants exposed to IV

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4-group experimental design

use of two control groups and two experimental groups

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skepticism

suggestion of doubt about a knowledge claim

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Threat to validity

Is the design of the study (the basis of the claim) sufficiently valid for providing an empirical basis for the knowledge claim?

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To address internal validity threats…

…build better study design that addresses the something else

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Internal validity questions

Is the variation in the DV due to the IV, or due to something else (must be identified)?

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External validity question

Does the IV-DV relationship as observed in the study generalize beyond the study (to other settings, sample, points in time)?

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To address external validity threats…

…build a better study design that addresses the other areas

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Measurement

measure/scale/test used to assign numbers to observations (how we operationalize)

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Reliability of a measure

scores yielded by the scale (measure/test) show consistency over time, scaling formats, or with respect to the responses to items that comprise the scale

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Validity of a measure

scores yielded by the scale (measure/test) provide a correct representation of the low-high variation of the variable of interest (do they do what we say they do)

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1, 2, 3 of person’s correlation

Departure from zero indicates (1) - the presence of covariation (2) direction of covariation and (3) how much covariation

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Test-retest design

data collected at two points in time to examine the consistency of scores (covariation at two time points indicates consistency)

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Equivalent-forms design

data collected from multiple scaling formats to examine the consistency of scores (covariation across two scaling formats indicates consistency)

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consistency can be indicated by

covariation

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Internal consistency design

Answers to each item should correlate as highly as possible with every other item (as seen using the average of r across all items)

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Predictive Criterion-related design (validity of measure)

Couples two hypotheses. One states simple covariation between the variable of interest (predictor) and a second variable (criterion), and the other predicts that the variable of interest predicts the second. Demonstrates a predictor-criterion relationship.

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Convergent-discriminant validity design (of a measure)

Tests the variable of interest in relation to two other variables: one that goes with it/is related to (convergent) and one that doesn’t go with it/is not related to (discriminant)

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Correlation is derived from…

covariance

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Covariance can indicate _____ and ______, but not ______.

Presence of covariation, direction of covariation, how much covariation

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linear

in reference to a straight line

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best fitting line

a straight line closer to all points than any other straight line

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r = undefined, indeterminate

when there is variation in one variable but not the other, so covariation cannot be indicated

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Statistical rule of life about interpreting r

plot the data before interpreting (is there an absence of covariation or the absence of linear covariation?)

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when r=.00, there are two possibilities:

non-linear covariation or no covariation

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slope is…

the departure from the absence of slope (positively or negatively)

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r2

indicates the proportion of variation in X that has been observed in relation to the variation of Y(how much of the variation in the data is explained by this relationship)

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Venn Diagram

Each Venn indicates the proportion of variation of the variable. Overlap indicates the amount of shared variation (variation explained by this relationship)

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Based on an r value, one cannot tell…

the order of covariation (causality)

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Problems of range restriction/selection effect/small sample

creates an artifact through a limited range of variation (fix through larger sample)

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When your expected r vales doesn’t match your observed, you’re not an idiot. You should…

Check and see if the scatterplot suggests an artifact of range restriction/selection effect/a small sample