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Psychology GRE > Research Design > Flashcards

Research Design Flashcards

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1
Q

Predictive value

A

the degree to which an independent variable can predict a dependent variable

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2
Q

Cohort effects

A

the effects that might result when a group is born and raised in a particular time period.

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3
Q

Demand characteristic

A

when subjects act in ways they think the experimenter wants or expects

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4
Q

Experimenter bias (another name)

A

Rosenthal effect

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5
Q

Hawthorne effect

A

when subjects alter their behavior because they’re being observed

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6
Q

Selective attrition

A

when the subjects that drop out of an experiment are different from those that remain. Remaining sample is no longer random

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7
Q

Social desirability

A

subjects do and say what they think puts them in a positive light.

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8
Q

Frequency distributions

A

explain how the data in a study looked. Might show how often different variables appeared.

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9
Q

Nominal variables

A

male/female; Republican/democrat

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10
Q

Ordinal variables

A

implies order; not necessarily equally spaced (marathon finishers)

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11
Q

Interval variables

A

show order and space because equal spaces lie between the variables, but do not include a real zero (temperature)

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12
Q

Ratio variables

A

order, equal intervals, and a real zero (age)

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13
Q

Frequency polygon

A

plotted points connected by lines; used to plot continuous variables (categories without clear boundaries)

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14
Q

Histogram

A

vertical bars; sides touch; discrete variables with clear boundaries and interval variables with some order

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15
Q

Standard deviation

A

(1) subtract mean from each; (2) square each; (3) add; (4) divide by original number of scores; (5) take square root

If standard deviation is large – scores highly dispersed
If small, scores very close together

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16
Q

Unimodal

A

only one hump

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17
Q

Z-score

A

how many standard deviations a score is from the mean; -3 to +3 for normal distributions

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18
Q

T-score

A

transformation of a z-score in which the mean is 50 and the standard deviation is 10; T = 10(z) + 50

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19
Q

Standard normal distribution

A

same as normal distribution but standardized so that the mean for every distribution is zero and the standard deviation is one

20
Q

platykuric distributions

A

flat top/same sides

21
Q

Pearson r correlational coefficient

A
  • 1 to +1
22
Q

Spearman r correlational coefficient

A

used only when data is in the form of ranks – used to determine the line that describes a linear relationship

23
Q

Statistical regression

A

allows you to not only identify a relationship between two variables but also make predictions about one variable based on another variable

24
Q

Parameters

A

refer to numbers that describe populations

25
Q

statistically significant

A

describes a real pattern

26
Q

Alpha level

A

significance level; < .05 or < .01 (5/100 or 1/100)

27
Q

Type 1 error

A

incorrectly reject the null hypothesis (thought findings were significant but they weren’t)

28
Q

Type 2 error

A

wrongly accept the null hypothesis (say no relationship, but there is)

29
Q

T-test

A

compare the means of two different groups to see if the two groups are truly different; used to analyze means on continuous data; particularly useful when samples are small; cannot test for differences between two groups

30
Q

Chi-square tests

A

used when n-cases in a sample are classified into categories (cells); tells us whether the groups are statistically significant in size; analyze categorical or discrete data; can be used on small samples; can assess goodness of fit of distribution or whether the pattern is what would be expected

31
Q

ANOVA (Analysis of variance):

A

analyzes differences among means of continuous variables; can analyze difference among more than two groups

32
Q

One-way ANOVA

A

tests whether the means on one outcome or dependent variable are significantly different across groups

33
Q

Two-way ANOVA

A

tests effects of 2 independent variables or treatment conditions at once

34
Q

Factorial analysis of your variance

A

Used when an experiment involves more than one independent variable; can isolate main effect; can identify interaction effects

35
Q

Analysis of Covariance (ANCOVA)

A

• Tests whether two groups co-vary; can adjust for preexisting differences between groups

36
Q

Linear regression

A

Use correlational coefficients as a predictive measure; based on a line fit with the least-squares method

37
Q

standardized

A

tried out on huge groups of people to create norms

38
Q

Criterion-referenced tests

A

measure mastery in one particular area (final exam)

39
Q

Domain-referenced tests

A

attempt to measure less defined properties (like intelligence); need to be checked for reliability and validity

40
Q

Reliability

A

how stable the measure is

41
Q

Split-half reliability

A

comparing an individual’s performance on two-halves of the same measure (odd vs. even); reveals internal consistency

42
Q

Item-analysis

A

how a large group responded to each item on a measure

43
Q

Concurrent validity

A

whether scores on a new measure positively correlate with other measures known to test the same construct
• Cross-validation

44
Q

Construct validity

A

whether the test really taps the abstract concept being measured

45
Q

Content validity

A

whether the content of the test covers a good sample of the construct (not just a part of it)

46
Q

Face validity

A

test whether the items simply look like they measure the construct

47
Q

Donald Campbell and Donald Fiske

A

multi-trait-multi-method technique to determine the validity of tests

Psychology GRE (10 decks)

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