Choose the alternative that best completes the stem of each question.
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1 | | An advantage of using an experimental multivariate design over separate univariate designs is that using the multivariate analysis |
| | A) | allows you to look at more complex relationships than does the univariate strategy. |
| | B) | provides a more powerful test of your hypotheses. |
| | C) | allows you not to worry about meeting restrictive assumptions characteristic of univariate statistics. |
| | D) | both a and b |
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2 | | Correlational multivariate analyses include |
| | A) | diriminant analysis. |
| | B) | multiple regression. |
| | C) | canonical correlation. |
| | D) | all of the above |
| | E) | both a and b only. |
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3 | | The presence of outliers in your data |
| | A) | affects the magnitude of the correlations calculated but not the slope of the regression line. |
| | B) | affects the slope of the regression line but not the magnitude of the correlations calculated. |
| | C) | affects both the slope of the regression line and the magnitude of the correlations calculated. |
| | D) | is less of a problem for multivariate statistics than it is for bivariate or univariate statistics. |
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4 | | An effective way of detecting outliers in a multivariate data set is to |
| | A) | convert raw scores to z scores and evaluate the degree of deviance of the z scores. |
| | B) | conduct individual Pearson correlations on your data before conducting any multivariate test. |
| | C) | do nothing; outliers do not significantly affect multivariate statistics. |
| | D) | both a and b |
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5 | | __________ occurs when variables in your analysis are highly correlated. |
| | A) | Heteroscedasticity |
| | B) | Multicollinearity |
| | C) | Reflecting |
| | D) | Outlier bias |
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6 | | ____________ causes the observed value of a variable to differ to some extent from its true value. |
| | A) | Homoscedasticity |
| | B) | An outlier |
| | C) | Error of measurement |
| | D) | Multicollinearity |
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7 | | Generally speaking, multivariate analysis requires |
| | A) | fairly large samples. |
| | B) | small samples. |
| | C) | less concern over meeting assumptions than do univariate tests. |
| | D) | sampling from a population that is not normally distributed. |
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8 | | In a factor analysis, the correlation between an individual variable and an underlying dimension is a |
| | A) | discriminant function. |
| | B) | factor loading. |
| | C) | squared semipartial correlation. |
| | D) | canonical function. |
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9 | | The factors extracted in a factor analysis are made more clear and interpretable by |
| | A) | converting raw scores to z scores prior to analysis. |
| | B) | eliminating variables that have low correlations with other variables. |
| | C) | applying a square root transformation to the raw data prior to analysis. |
| | D) | statistically rotating factors. |
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10 | | According to Tabachnick and Fidell (2001), principal components analysis could be used to |
| | A) | help infer causality from correlational data. |
| | B) | extract as many factors as possible from your data prior to a factor analysis. |
| | C) | experiment with different communality values after an exploratory factor analysis. |
| | D) | determine the degree of contribution of a variable in a multiple regression analysis. |
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11 | | ____________ is a statistical technique used to evaluate the relationship between two variables statistically controlling the effects of a third. |
| | A) | Discriminant analysis |
| | B) | Canonical correlation |
| | C) | Partial correlation |
| | D) | Factor analysis |
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12 | | A statistical technique that involves entering multiple predictor variables into an equation according to a specified order determined by theory is |
| | A) | hierarchical regression. |
| | B) | simple regression. |
| | C) | stepwise regression. |
| | D) | none of the above |
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13 | | The use of stepwise regression techniques is frowned on because |
| | A) | only three predictor variables can be entered at a time. |
| | B) | it tends to be too sensitive to causal relationships among variables. |
| | C) | it tends to capitalize on chance and may be limited to a particular sample. |
| | D) | all of the above |
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14 | | If you have multiple predictor variables and a dichotomous dependent variable, the most appropriate multivariate test is |
| | A) | stepwise regression. |
| | B) | canonical correlation. |
| | C) | factor analysis. |
| | D) | discriminant analysis. |
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15 | | Loglinear analysis |
| | A) | is a nonparametric statistic. |
| | B) | works much like chi-square. |
| | C) | can be used in place of ANOVA, MANOVA, or multiple regression where your data are categorical. |
| | D) | all of the above |
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16 | | If you have two sets of variables to correlate, the most appropriate multivariate test is |
| | A) | stepwise regression. |
| | B) | canonical correlation. |
| | C) | factor analysis. |
| | D) | discriminant analysis. |
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17 | | Using a MANOVA in place of a univariate analysis for a within-subjects experiment is advantageous because MANOVA |
| | A) | allows you to circumvent some of the restrictive assumptions of the univariate within-subjects ANOVA. |
| | B) | allows you to include more than two independent variables in your analysis. |
| | C) | uses separate error terms to test effects rather than a pooled error term. |
| | D) | none of the above |
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18 | | In research situations in which you want to measure or manipulate categorical variables, an appropriate alternative to statistics such as ANOVA, MANOVA, or multiple regression would be |
| | A) | canonical correlation. |
| | B) | multiple t tests. |
| | C) | path analysis. |
| | D) | multiway frequency analysis. |
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19 | | According to your text, the statistic used to evaluate data in a loglinear analysis is |
| | A) | G². |
| | B) | F. |
| | C) | d. |
| | D) | Chi-square. |
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20 | | Path analysis is |
| | A) | a unique statistical test, allowing you to evaluate multiple dependent variables in one test. |
| | B) | an application of multiple regression to investigating causal relationships among variables. |
| | C) | not used to investigate causal relationships, but is a multivariate statistic. |
| | D) | an extension of the Pearson r to multivariate designs. |
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