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EPPP > Research Design and Statistics > Flashcards

Research Design and Statistics Flashcards

1
Q

True experimental research

A

At least one IV is manipulated and subjects are randomly assigned.

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2
Q

Quasi-experimental research

A

One IV manipulated, but no random assignment of subjects, typically because they’re already in groups.

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3
Q

Between groups design

A

Only compares groups that are independent. Ex: differences in reading levels of two different classes of second graders.

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4
Q

Within subjects design

A

Subjects are repeatedly measured. Ex: Looking at people’s ability to recall nouns/verbs/nonsense; all subjects are given the list of words to remember.

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5
Q

Analogue research

A

Evaluates treatment conditions that only resemble or approximate clinical situations.

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6
Q

Cross-sectional research

A

Looks at differences across sections (e.g., different ages) by sampling subjects from the age categories of interest at one point in time.

ex: How much time someone spends on the internet in groups of persons from different ages.

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7
Q

Longitudinal research

A

following a group of subjects over many years in order to understand the changes that take place as people age.

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8
Q

Simple random sampling

A

every member of the population has an equal chance of being selected.

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9
Q

Stratified random sampling

A

Population is first divided into strata (age, levels of income) and then random sample from each stratum is selected.

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10
Q

Threats to internal validity - History

A

Specific incidents that intervene between measuring points, either in or out of the experimental situation.

EX: An earthquake occurring right before implementation of a preparedness course.

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11
Q

Threats to internal validity - Maturation

A

Factors that affect the subjects’ performance because of the passing of time.

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12
Q

Threats to internal validity - Test practice

A

Familiarity with testing affects scores on repeated testing.

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13
Q

Threats to internal validity - Instrumentation

A

Changes in observers or the calibration of equpiment.

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14
Q

Threats to internal validity - Statistical regression

A

Regression to the mean; extreme scores tend to become less extreme over time.

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15
Q

Threats to internal validity - Selection bias

A

Caused by non-random assignment.

EX: if the first 20 subjects who volunteer for a study are assigned to one Tx, the next 20 to another, then we have selection bias.

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16
Q

Threats to internal validity - Diffusion

A

Occurs when the no-treatment group actually gets some of the treatment.

EX: Inadvertently having a discussion about cognitive strategies in a control group.

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17
Q

Construct Validity

A

Actually measuring what you intend to.

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18
Q

Threats to construct validity - Attention & Contact with Clients

A

Hard to tell whether the change in clients is due to the intervention or the attention/contact.

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19
Q

Threats to construct validity - Experimenter Expectancies

A

Cues/clues transmitted to the subjects by the experimenter ultimately affecting the data.

Rosenthal effect.

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20
Q

Threats to construct validity - Demand characteristics

A

Factors in the procedures that suggest how a subject should behave.

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21
Q

Threats to construct validity - John Henry Effect

A

Compensatory rivalry. Persons in the control group try harder than usual in the spirit of competition.

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22
Q

Threats to external validity - Contextual characteristics

A

Conditions in which the intervention is imbedded. Reactivity occurs when subjects behave in a certain way just because they are participating in research and being observed.

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23
Q

Standard deviation

A

Average deviation from the mean in a given set of scores. Square root of the variance.

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24
Q

Sampling error

A

samples drawn from populations are usually not perfectly representative of the population.

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25
Q

Central limit thoerem

A

Assuming an infinite number of equal sized samples are drawn from a population, the means will be normally distributed.

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26
Q

Type I error

A

rejecting the null when it’s really a mistake; significance is found but subsequent research finds no significance.

most likely in t-tests

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27
Q

Type II error

A

Accepting the null when you should reject it; significance is not found in original EXP, but subsequent studies find significance.

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28
Q

Power

A

Defined as ability to correctly reject the null.

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29
Q

Coefficient of determination

A

Squaring the correlation coefficient. Represents the amount of variability in Y that is shared with, xplained by, or accounted for by X.

EX: r = .5; coeff of det: .25. 5% of variability in Y is explained by X.

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30
Q

Assumptions of Bivariate correlations (3)

A
  1. Linear relationships
  2. Homoscedasticity: similar spread of scores across the entire scatter plot.
  3. Unrestricted range
31
Q

Zero-order correlation

A

Relationship between x and y when it is believed that no extraneous variables affect the r/s.

32
Q

Partial correlation

A

Looking at r/s b/w two variables while controlling for effect of a third.

33
Q

Part correlation

A

Examines r/s between predictor and criterion w/ the influence of the third variable removed ONLY for one of the variables.

34
Q

Multivariate tests

A

Tests of correlation and prediction involving several IVs and one or more DVs

35
Q

Multiple R

A

Correlation between two or more IVs and one DV, where the DV is always interval or ratio.

36
Q

Multiple regression

A

Allows the prediction of the DV based on the values of the IVs.

37
Q

Multicollinearity

A

Occurs in a multiple regression equation when the predictors are highly correlated with one another.

38
Q

Discriminant function analysis

A

Same as multiple regression, but the DV is nominal (categorical; pass/fail)

39
Q

Path analysis

A

Applies multiple regression to test a model that specifies causal links between variables.

40
Q

Factor analysis

A

Operates by extracting as many significant factors from the data as possible.

41
Q

Eigenvalues

A

Indicator of strength of a factor.

42
Q

Test retest reliability

A

Coefficient of staiblity; consistency in scores on same measure at different time points.

43
Q

Parallel forms of reliability

A

Coefficient of equivalence; Correlating scores obtained by the same group on two equivalent but not identical forms of the same test.

44
Q

Internal consistency

A

Consistency of scores within a test.

45
Q

Standard error of measurement

A

Standard deviation of a normal distribution of scores obtained by one individual on equivalent tests.

46
Q

Content validity

A

How adequately a test samples a particular content area

47
Q

Criterion related validity

A

How adequately a test score can be used to infer, predict, or estimate an outcome (e.g., how well does GPA predict SAT scores).

48
Q

Concurrent validity

A

Predictor and criterion are measured and correlated at the same time.

49
Q

Predictive validity

A

Using one score to predict another later on.

50
Q

Standard error of estimate

A

Average amount of error in estimating an outcome based on a predictor.

51
Q

Construct validity

A

How adequately a new test measures a construct or trait. Assessed with a factor analysis.

52
Q

Convergent validity

A

Correlation of scores on the new test with other available measures.

53
Q

Discriminant validity

A

Correlation of scores on the new test with scores on another test that meaures a different trait. Low discriminant validity between two diff measures means high construct validity.

54
Q

If a constant is subtracted from every score in a sample, what happens to mean/SD?

A

Mean: Decrease
SD: same

55
Q

Pooled error term is used in ANOVA when?

A

Sample size is unequal.

56
Q

Biggest issue with single subject research design is

A

Autocorrelation

57
Q

Cluster sampling

A

Researchers divide a population into smaller groups.

58
Q

Structural equation modeling

A

Multiple pathways involving multiple predictors and multiple criterion variables.

59
Q

Face validity

A

whether something seems like it’s measuring what it’s supposed to based on face value

60
Q

When to use ANCOVA

A

When unexpected differences are uncovered among treatment groups with regard to an extraneous variable.

61
Q

What is likely to happen when a kid who tested very high on the WISC takes it again in 3 years.

A

Lower.

62
Q

LISREL, a form of SEM, can be used to

A

test a causal model of relationships among variables

63
Q

To improve reliability on a new test you’re developing, what should you do?

A

Use a heterogeneous sample.

64
Q

How should you analyze your results if you’re looking at whether several variables can predict one nominal (pass/fail) outcome?

A

Discriminant analysis

65
Q

Sequential introduction of treatment is closest to what research design?

A

Multiple baseline.

66
Q

In normal distribution, bell curve, what is the percentile of +/- 1 SD from the mean.

A

+1: 84% and up
-1: 15.9% and down.
+2: 98%
-2: 2%

67
Q

How to increase chance of finding significance?

A

Increase sample size, use a one-tailed test, increase alpha.

68
Q

Kappa coefficient

A

Interrater reliability

69
Q

Split plot anova

A

used to test for differences between two or more independent groups whilst subjecting participants to repeated measures.

70
Q

Standard errors of the mean, measurement, and estimate express error in terms of _______

A

the Standard Deviation

71
Q

Relationships of the standard error of estimate

A

Direct relationship with the SD of the criterion.

Indirect relationship with validity.

72
Q

Significant differences in a 2-way ANOVA means what

A

Any combination of main effects and interactions.

73
Q

Relationship between education and income among clinical psychologists?

A

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EPPP (12 decks)

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