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EPPP > Stats > Flashcards

Stats Flashcards

1
Q

IV

A

manipulated by researcher, presumed to be the agent of change

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2
Q

DV

A

measured by researcher to determine if IV has an effect

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3
Q

Quasi-independent variable

A

IV in quasi-experiemtne (using existing groups rather than random assignment in determining condition)

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4
Q

Variance

A

Sum of squared deviations from the mean, divided by N-1. Less susceptible to extreme values/outliers

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5
Q

Standard deviation

A

Square root of the variance

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6
Q

r-squared (single predictor), R-squared (multiple predictors)

A

Proportion of variation accounted for in one variable through linear relationship with another (or others). Not good for sample-to-sample comparisons. Reflects a reduction in error.

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7
Q

Eta-squared

A

Proportion of variance accounted for in one variable thru relationship (not necessarily linear) with another (or others)

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8
Q

Squared factor loading

A

Proportion of variance accounted for in one variable by a factor

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9
Q

Beta weight

A

Standard regression coefficient

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10
Q

Coeffeicient of Nondetermination

A

One minus r-squared; proportion of variation in the dependent variable not associated with independent variables

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11
Q

Chi-square: Cramer’s phi

A

Strength of relationship between two variables in a contingency table

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12
Q

t-test: Cohen’s d

A

Difference between two group means in terms of a standard deviation (control group or pooled)

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13
Q

ANOVA: eta-squared, omega-squared

A

Proportion of variation in the DV accounted for by the IV

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14
Q

Correlation: r-squared

A

Proportion of variation in one variable accounted for by the linear relationship with another

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15
Q

p value

A

The level of significance, or the probability that the null hypothesis is false

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16
Q

Kappa Coefficient

A

Used to evaluate inter-rater reliability

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17
Q

Coefficient Alpha

A

Stats used to assess the internal consistency reliability

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18
Q

Pearson’s r

A

A correlation stat used primarily for two sets of data that are of the ratio or interval scale; it is the most commonly used correlational technique

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19
Q

Pooled variance

A

The weighted average of two sample variances. Provides better estimate of population variance than either sample alone.

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20
Q

Mean Squared Within (MSW)

A

A measure of error variation used in ANOVA

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21
Q

Moderator variable

A

A variable that affects the magnitude of direction of the relationship between the independent variable and the dependent variable

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22
Q

Mediating variable

A

A variable explaining the process by which the IV affects the DV (therapy affects depression by creating a more positive self-image, which then lessens depression)

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23
Q

Outcome variable

A

The dependent variable for a prediction in an experiment; it should be clinically relevant

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24
Q

Suppressor variable

A

Lowers or covers the relationship between variables

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25
Q

Criterion contamination

A

Occurs when the operational or actual criterion includes variance that is unrelated to the ultimate criterion.

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26
Q

Chi-Square test

A

Examines frequency distribution of categorical variables such as political party affiliation or eye color. Non-parametirc, does not require normality.

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27
Q

Goodness-of-fit

A

One-way Chi-Square test for examining frequency distribution of one IV. May use expected frequencies (like expected percentage)

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28
Q

Test for independence

A

Two-way Chi-Square test for examining contingency table for 2 variables to determine wether they are independent (un-related). Requires counts, not percentages and requires a count of at least 5.

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29
Q

T-test

A

An inferential statistical procedure used to test whether the means of two groups are equal to each other.

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30
Q

The t-test is more powerful (more likely to reject the null hypothesis) when:

A

Larger sample size(s); larger mean difference; smaller sample variation

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31
Q

One-sample t-test

A

Tests they hypothesis that a single sample mean is different than a specific hypothesized value

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32
Q

Independent-samples t-test

A

Test the hypothesis that two unrelated samples are different from each other

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33
Q

Related or dependent-samples t-test

A

Tests the hypothesis that the difference between two related samples (pre/post scores, scores of siblings) is not equal to 0 (samples have different means)

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34
Q

Main effect

A

Arising from ANOVA terminology; represents the effect of an independent variable on Y averaged across the (main or interaction) effects of other independent variables.

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35
Q

Interaction

A

The circumstance in whciht the impact of one variable on y is conditional on (varies across) the values of another predictor

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36
Q

Kolmogorov

A

An uncommon stat that utilizes ordinal or ranking data.

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37
Q

Power

A

The probability of rejecting a null hypothesis that is false

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38
Q

ANOVA post-hoc tests

A

If a significant difference exists, a post-hoc test will be a more focused examination of which means differ from which. Scheffe (conservative) Tukey’s HD, Fisher’s LSD (liberal)

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39
Q

Factorial or n-way ANOVA

A

n represents the number of IVs or factors. Used when examining the effects of two or more IVs.

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40
Q

Mixed-design ANOVA

A

Multiple IVs including both within-subjects (time) and between-subjects (condition) factors. Ex. pre/post test with control condition.

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41
Q

Kruskal-Wallis

A

An alternative test to the one-way ANOVA that can be used to compare two or more independent groups

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42
Q

Randomized Block ANOVA

A

A statistical test that controls for the effects of extraneous variables by grouping (“blocking”) the subjects based on the variable and then assigning each subject to one of the interventions; the confounding variable is therefore handled as if it were an independent variable.

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43
Q

Cluster sampling

A

Sampling technique involving naturally occurring groups (clusters)

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44
Q

Stratified sampling

A

Sample drawn from each stratum; main objective is improved precision

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45
Q

Multistage sampling

A

More complex form of cluster sampling. Population divided into start at highest level, then sample s drawn and stratified at lower level; procedure repeated until at lowest hierarchical level

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46
Q

Systematic sampling

A

A simple, random sampling of each stratum of the population. Additional variables may also be stratified, such as gender.

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47
Q

Random selection

A

Drawing a sample from a population in such a way that each member has an equal probability of being selected

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48
Q

Experimental Design

A

Researc in whcih random assignment is used to place subjects in groups that will receive different aspects of the variable in question.

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49
Q

Proband

A

AKA patient zero; the first family member to seek professional attention for a disorder

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50
Q

Normal distribution

A

mode = median = mean

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51
Q

Positively skewed distribution

A

mode < median < mean

52
Q

Negatively skewed distribution

A

men < median < mode

53
Q

ETS scores

A

mean of 500 and standard deviation of 100

54
Q

Distribution of scores to either side of the mean

A

50% falls to either side of the mean. 34% between the mean and one standard deviation, 14% between the one and two standard deviations, and 2% between two and three standard deviations from the mean

55
Q

What percentage of scores falls within one standard deviation from the mean?

A

84%

56
Q

What percentage of values are within two standard deviations of the mean?

A

95%

57
Q

Uniform distribution

A

Equal frequencies across distribution (i.e. a block)

58
Q

J-shaped distribution

A

Skewed, but without a tail on distribution with a mode

59
Q

Actuarial Data

A

Grove and Meehl: demographic data related to risk calculation of births and deaths. (10% better than clinical judgment)

60
Q

Larger sample sizes are preferred in order to:

A

Ensure sample adequately represents population; reduce sampling error; increase statistical power

61
Q

Standard Error

A

Standard deviation of a sampling distribution

62
Q

Sampling Error

A

Difference between an obtained sample statistic and the corresponding population parameter

63
Q

Systematic Error

A

Measurement bias leading to measured values being systematically too high or too low.

64
Q

Standard Error of the Mean

A

Estimates how much the sample mean will deviate from the population mean due to sampling error. When population size goes up, sample size goes down, and the Standard Error of the Mean will go up

65
Q

Standard Error of Measurement

A

Constructs confidence intervals for test scores. How much the score is expected to vary from the person’s actual ability. Standard deviation of observed values around predicted values on a regression line (measure of prediction error). Higher standard deviation is higher SEE.

66
Q

Central Limit Theorem

A

Increaseing the size of the random sample N drawn from a population will cause the distribution of the sample means to form a more normal distribution, with a mean equal to the population mean, and a standard deviation (i.e. standard error of the mean) equal to the sample standard deviation divided by the square root of N

67
Q

When a constant is added to or subtracted from a variable

A

Measures of central tendency (median, mean) change similarly; measures of variability (range, standard deviation) remain the same.

68
Q

When you multiply or divide by a constant

A

measures of both central tendency and variability change

69
Q

When you add, subtract, multiply, or divide by a constant…

A

the shape of the distribution remains the same; correlations with other variables remain the same

70
Q

Attenuation

A

Decrease in correlation coeffecient reflecting relationship between two variables due to measurement error in one (or both)

71
Q

Attenuation correction formula

A

Estimates true correlation between two variables; requires correlation coefficient and a reliability coefficient for each variable. If absolute value of result is greater than one, round to one (Pearson’s r ranges from -1 to 1)

72
Q

Measurement Error

A

Error in the employed values of a variable due to the presence of distorting influences on the assessment, such as momentary distractions, error in recording or understanding, influences of other variables on responses to particular items

73
Q

2 types of construct validity

A

Convergent (tests correlated with tests of similar trait) and divergent (tests not correlated w/tests of unrelated traits)

74
Q

2 types of criterion-related validity

A

Concurrent validity (test correlated with criterion measured at same time; SAT and high school GPA) and predictive validity (test correlated with criterion variable measured at a future time; SAT and college GPA)

75
Q

Content validity

A

Adequacy of test in measuring all facets of a construct or trait

76
Q

External validity

A

Extent to which experimental findings may be generalized from the lab to the world at large

77
Q

Internal validity

A

Ability to assert that observed effects are attributable to an independent variable rather than confounding variable

78
Q

5 main threats to external validity

A

Interactions of different treatments; interaction of testing and treatment; interaction of selection and treatment; interaction of setting and treatment; interaction of history and treatment

79
Q

Threats to internal validity

A

History, maturation, testing, instrumentation (changes in measure may cloud results), regression to the mean, selection, mortality (dropouts), interactions with selection (any of above threats may interact with selection and be mistaken for treatment effects) and ambiguity about the direction of causation

80
Q

Confidence interval

A

Range of values centered at sample statistic used to estimate the population parameter with a confidence of (1-a) percent

81
Q

Standard Error of Estimate

A

An index of the degree of variability of the data points about a regression line, determined to be the square root of the sum of squared deviations of the points about the line divided by (N-2)

82
Q

Familywise alpha

A

running multiple tests, each with its own alpha. Results in a familywise alpha for the entire set approximately equal to the sum of all the alphas. May correct (Bonferroni-adjustment) or run alternative test (MANOVA to replace multiple ANOVAs)

83
Q

Changing-Criterion Design

A

A single-case experimental design that demonstrates the effect of an intervention by showing that performance changes in increments to match a performance criterion

84
Q

Reliability

A

The extent to which a measure or test is consistent and repeatable. Necessary, but not sufficient, for validity.

85
Q

Determination of reliability with respect to internal consistency

A

Reliability coefficient, split-half, Cronbach’s Alpha (Coefficient Alpha), Kuder-Richardson’s Formula (KR20)

86
Q

Determination of reliability in respect to consistency between alternative forms

A

Coefficient of equivalence

87
Q

Reliability in respect to test-retest consistency

A

Coefficient of stability

88
Q

What is the Kappa Statistic used for?

A

To determine inter-rater reliability. For use with nominal or ordinal data.

89
Q

Eta (n)

A

A universal measure of relationship that can be used regardless of the from of the relationship

90
Q

Incremental validity

A

In psychometrics, the degree to which a test improves on decision than can be made from existing information, such as the base rate of the attribute being measured and other measures that are available.

91
Q

p-value

A

In statistical hypothesis testing, the p-value is the probability of obtaining a result at least as extreme as a given data point, under the null hypothesis.

92
Q

Validity coefficient

A

The correlation between the predictor test and the criterion variable that specifies the degree of validity of that generalization

93
Q

ABAB Design

A

A single-subject design in which a baseline measure of the DV (depression) id obtained (A) before treatment introduced (B), removed (A), and reintroduced (B)

94
Q

Multiple-Baseline Design

A

A single-case experimental design strategy where the intervention is introduced across different behaviors, individuals, or situations; the key distinction between this and ABAB design is that this design no treatment is withdraws; rather, the treatment is applied to multiple settings, behaviors, or subjects

95
Q

Counterbalancing

A

A method of arranging conditions or tasks for the subjects so that a given condition or task is not confounded by the order in which it appears.

96
Q

Randomized Block Design

A

This requires that the researcher divide the sample into relatively homogenous subgroups or blocks (analogous to strata in stratified sampling), and then the chosen experimental design is implemented within each block

97
Q

Solomon Four-Group Design

A

An experimental design used to evaluate the effect of pre-testing; a combination of the pre-test/post-test control-group design and a post-test-only design in which a pre-test and the experimental intervention are combined.

98
Q

Split-plot design

A

An experimental design that includes both randomized group designs and randomized block (repeated measures designs)

99
Q

Matched subjects design

A

Each participant in one sample is matched with a participant in another sample with respect to a specific variable (SES)

100
Q

Repeated measures design

A

A research design in which participants appear in each condition

101
Q

Systemic variance

A

Variance due to the IV

102
Q

Experimental variance

A

Variance due to the DV

103
Q

Systemic bias

A

The tendency of a process to favor a particular outcome

104
Q

Factorial design

A

A group design in which two or more variables are studied concurrently. For each variable, two or more levels are studied. The design includes the combinations of the variables so that maine effects as well as interaction effects can be evaluated.

105
Q

Cross-sectional design

A

The most commonly used version of a case-control design in clinical psychology, in which subjects (cases and controls) are selected and assessed in relation to current characteristics (different from events that happened in the past or the future)

106
Q

Interrupted time-series design

A

Repeated measurements are made on participants both before and after a manipulated intervention of a naturally occurring event

107
Q

Nonequivalent Control Group

A

A group used in quay-experiments to rule out or make less plausible specific threats to internal validity; the group is referred to as nonequivalent because it is not formed through random assignment in the investigation

108
Q

Protocol analysis

A

Qualitative data analysis method involving verbalization of thoughts occurring while completing a given task.

109
Q

Event coding

A

A technique used to record the events that led up to the subject’s thinking process

110
Q

Retrospective debriefing

A

The act of having a subject describe how he or she determined the solution after working on a problem

111
Q

Structural Equation Modeling (SEM)

A

Technique for building and testing statistical models. Uses factor analysis, path analysis, and regression. Two step process: validates measurement model with confirmatory factor analysis and tests structural model with path analysis

112
Q

Q-Technique Factor Analysis

A

A statistical analysis that determines how many types of people a sample represents

113
Q

Discriminant function analysis

A

Classifies people into criterion groups based on their scores or statuses on two or more predictors.

114
Q

Partial correlation

A

Correlation between x and y after removing variation in each shared with a third variable z

115
Q

Semi-partial correlation

A

Correlation between x and y after removing variation in x (and only x) shared with a third variable z

116
Q

phi

A

2 dichotomous variables

117
Q

tetrachoric

A

2 artificial dichotomous variables

118
Q

contingency

A

two nominal variables

119
Q

Spearman’s rho

A

two ordinal variables

120
Q

Linear discriminant function analysis

A

Used to analyze the relationship between variables when there are multiple x variables and one y variable that is categorical

121
Q

Path analysis

A

The application of correlational analysis to test models of causality

122
Q

Logic Analysis

A

A multivariate technique that uses two or more categorical variables to predict the status of a single categorical variable

123
Q

Multiple Discriminant Analysis

A

Several independent variables are used to predict group membership

124
Q

Cluster analysis

A

Data is gathered on a number of DVs and statistically analyzed for naturally occurring subgroups without using an “a priori” hypothesis

125
Q

Communality

A

Each test in a factor analysis has a communality, which indicates the total amount of variability in test scores that has not been explained by the factor analysis - i.e. by all of the identified factors.

126
Q

Factor loading

A

The correlation between a single test and an identified factor.

EPPP (12 decks)

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