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EPPP or bust > Research Methods & Stats > Flashcards

Research Methods & Stats Flashcards

1
Q

Item response theory/latent trait theory

A

used to calculate to what extent a specific item on a test correlates with an underlying construct. Can be used to equate scores from non-parallel measures. Used to develop individually tailored adaptive tests.

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2
Q

classical test theory

A

individual’s test score is the sum of true score variability and error score variability

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3
Q

empirical criterion keying

A

Used in MMPI development, items chosen based on their ability to discriminate group membership.

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4
Q

ipsative measures

A

yield information only on the individual and not on how that individual compares to others

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5
Q

Partial correlation

A

correlation between two variables when the effects of a third variable have been partialed out (removed) from both variables

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6
Q

Semi-partial correlation

A

correlation between two variables when the effects of a third variable have been partialed out (removed) from one of the variables

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7
Q

coefficient of determination

A

proportion of variance shared by two variables and is the square of the correlation efficient

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8
Q

Interval recording

A

a type of behavioral sampling used when there is no clear beginning, middle, and end of a behavior. Breaking up a period of time into smaller parts and recoding whether or not behavior took place during each interval. A type of time sampling

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9
Q

Standard error of measurement

A

average amount of error in predictor variable

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10
Q

Cluster sampling

A

identifying naturally occurring groups and then randomly selecting certain groups and sampling all within group or random sampling of group

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11
Q

When is pooled error term used?

A

there is homogeneity of variance, i.e., homoscedasticity

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12
Q

When is a separate error term used

A

there is heteroscedasticity, that is variance is not equal

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13
Q

1-way ANOVA variables

A

1 IV, 1 continuous DV

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14
Q

MANOVA variables

A

multiple DVs

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15
Q

Chi-square variables

A

nominal DV

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16
Q

2-way ANOVA
variables

A

2 IVs, 1 DV

17
Q

3-way ANOVA variables

A

3 IVs, 1 IV

18
Q

Factorial ANOVA

A

More than one IV, 1 DV

19
Q

Correlations are weaker when

A

range is restricted

20
Q

Assumptions for chi test

A

independence of observation (e.g., cannot be used when repeated measures are made)

21
Q

When to use MANOVA?

A

Multiple DVs

22
Q

formula for standard error of the mean

A

= population standard deviation divided by the square root of sample size

23
Q

analysis of covariance (ANCOVA)

A

combines standard analysis of variance (ANOVA) with the technique or partial correlation. Allows effects of an extraneous/moderator variable to be statistically removed (partialed out).
exclusively used when partialing out the effects of a confound variable from research looking for differences between groups, not relationships among variables

24
Q

When is a t-test appropriate?

A

when there is only one IV and only two groups are being compared

25
Q

assumptions of factorial ANOVA

A

more than one IV
all IVs are between groups
data are independent

26
Q

define homoscedasticity

A

there is equal variance among groups

27
Q

Define partial correlation

A

a statistical procedure that investigates the relationship between two variables while controlling (partialing out) the relationship of a 3rd variable

28
Q

Percentage that falls within 1 SD of the mean (normal curve)

A

68%

29
Q

Percentage that falls within 2 SD of the mean (normal curve)

A

95%

30
Q

Percentage that falls within 3 SD of the mean (normal curve)

A

99.7%

31
Q

What are z-scores?

A

Measure the distance for raw data from the mean in terms of standard deviations

32
Q

formula for z-scores

A

Z= (x-Mean) divided by SD

33
Q

What stanine score (of raw scores) is roughly equivalent to the mean?

A

5

34
Q

Type I error

A

rejecting the null hypothesis when it’s actually true (concluding results are significant when in reality they came about by chance)
alpha

35
Q

Type II error

A

Not rejecting the null hypothesis when it’s actually false; failing to conclude there was an effect when there actually was (perhaps due to lack of power)
Beta

36
Q

What level of statistical power is considered sufficient

A

80% or higher

37
Q

How is statistical power determined?

A

1-beta

38
Q

What are beta weights?

A

standardized regression coefficients

39
Q

What do beta weights signifiy?

A

the strength of the relationship between the predictor and criterion in standardized regression

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