GRE Subject Test: Psychology : Statistical Procedures
Study concepts, example questions & explanations for GRE Subject Test: Psychology
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Example Questions
Example Question #35 : Measurement & Methodology
What is the mode of the following set of scores:
The mode is the number that occurs most often in the data set. In this case the mode is:
Example Question #36 : Measurement & Methodology
Which of the following z-scores demonstrates the least significant deviation from the mean?
A z-score represents the distance away from the mean for a particular score, expressed in terms of standard deviations. Thus, a z-score of 0 is equal to the mean, and a z-score of 1.4 is equal to 1.4 standard deviations above a given mean.
Example Question #37 : Measurement & Methodology
A researcher is performing a statistical analysis of data in order to draw conclusions. They are attempting to perform an analysis of variance (ANOVA), but are stymied when all the deviations from the mean sum to zero. The researcher most likely forgot which of the following?
Forgot to convert the differences from interval to ratio scale
Forgot to include adjustments for experimental error (EE)
None of these
Forgot to calculate one average for the negative values and one for the positive values
Forgot to square each difference before summing
Forgot to square each difference before summing
When calculating the sum of squares (SS) as part of an ANOVA, each individual difference from the mean must be squared before summing. This makes intuitive sense when one realizes that the average of all scores which came together to make a mean in the first place must be that mean!
Example Question #21 : Statistical Procedures
What is the appropriate inferential statistic to use when your data are from two or more groups of scores, with each score a measure of the same interval/ratio variable, and there are two independent variables and one dependent variable present?
Multiple regression analysis
Chi-squared analysis
Two-factor ANOVA
MANOVA
T-test
Two-factor ANOVA
A two-way analysis of variance is the most appropriate tool for determining simultaneously the effects of two (or more) independent variables on a dependent variable and the strength of the interaction (if any) between the independent variables.
Example Question #22 : Statistical Procedures
When conducting a two-way analysis of variance, the degrees of freedom (df) for each factor is best expressed as __________.
one less than the number of trials for that factor
None of these
one less than the number of levels for that factor
one less than the number of participants (n) in each trial
one less than the number of levels for all factors
one less than the number of levels for that factor
The df for each independent variable or factor is determined by how many different levels of that factor are included in the analysis. This can create a problem for calculating sums of squares and ultimately for calculating post hoc analyses, as the number of levels in a factor can be very large.
Example Question #23 : Statistical Procedures
Which statistical procedure is best used when there are two or more independent variables, the variables can be held fixed with respect to each other, and a conditional expectation of dependent variable given the independent variables is required?
Spearman's rank-correlation coefficient
All of these
Regression analysis
Two-way ANOVA
Analysis of covariance (ANCOVA)
Regression analysis
A regression analysis, so long as all but one independent variable at a time can be held fixed, is able to produce a predictive window called a conditional expectation that allows for prediction of dependent variable values given set independent variable levels.
Example Question #24 : Statistical Procedures
The primary difference between a sequential sum of squares and an adjusted sum of squares is that the adjusted sum of squares __________.
does not depend on the order in which factors are entered into the model
only works for models with small values for n
None of these
depends on the order in which factors are entered into the model
only works for models with large values for n
does not depend on the order in which factors are entered into the model
An adjusted sum of squares is a model that determines the amount of remaining variance in a model is explained by a factor given that all the other factors are already in the model. For a sequential sum of squares, the amount of variance in a model is explained only given the factors which are already in the model. Thus, the first factor inserted in a sequential sum of squares carries the most weight.
Example Question #25 : Statistical Procedures
All of the following are true about the General Linear Model (GLM) except which of the following?
It incorporates F-testing, t-testing, linear regression, and ANOVA models
All of these
It works irrespective of the error terms in each matrix
It can test hypotheses using either multivariate or multiple independent univariate tests
It generalizes multiple linear regression to more than one dependent variable
It works irrespective of the error terms in each matrix
The general linear model, expressed as 
Example Question #26 : Statistical Procedures
One appropriate use of a biased ANOVA term for expected mean squares might be which of the following?
None of these
If the factors are expected to have an interaction
If the population mean cannot be estimated
If one or more terms are nonparametric in the model
If the population means are not equal to one another (the null hypothesis is false)
If the population means are not equal to one another (the null hypothesis is false)
A biased EMS is most commonly used when random terms are part of the factors in the model (and thus, when the population means cannot be reasonably assumed to be equal irrespective of treatment effect).
Example Question #27 : Statistical Procedures
All of the following are true about Pearson's r except which of the following?
Sample r is extremely accurate at estimating population correlation coefficients if sample size is large
All of these
Confidence intervals cannot be established for weak values of r
Strength of correlation ranges from -1 to 1
Sampling distribution can be made to fit Student's t-distribution
Confidence intervals cannot be established for weak values of r
Constructing a confidence interval for Pearson's r can be done through a process called "bootstrapping", which involves re-sampling with replacement a certain number of values in each correlation a large number of times.
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