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The WAIS intelligence quotient (IQ) is normally distributed with a mean of 100 and a standard deviation of 15. I fan IQ between 85 & 115 is deemed normal, what percentage of the the general population would be considered normal?
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In the four bivariate relationships given below, in terms of person-product moment correlations (r), the strongest relationship is one for which r is:
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In correlational analysis, skewed variables are transformed to be normally distributed because:
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this increases the relationship between variables
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this decreases the relationships between variables
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the removes the effect of influential points
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this might invalidate the test
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Which on of the following does not increase the power of a test of r between X & Y?
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Standardising the scores of X & Y (Llinear transformation)
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Adopting a larger Type 1 error rate
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Conducting a one tailed test instead of two tailed
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Increasing the magnitude of r
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In clinical trials, it is ethnically responsible to use a sample size that is sufficient enough to detect an effect but not too large as to inconvenience the patient any longer than necessary. Which sample size should the research use to ensure that she has sufficient power?
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N = 15 (power of .70)
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N = 22 (power of .80)
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N = 30 (power = .90)
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N = 37 (power of .95)
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In clinical trials, it is ethnically responsible to use a sample size that is sufficient enough to detect an effect but not too large as to inconvenience the patient any longer than necessary. Which sample size should the research use to ensure that she has sufficient power?
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N = 15 (power of .70)
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N = 22 (power of .80)
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N = 30 (power = .90)
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N = 37 (power of .95)
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What is a type 1 error?
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Accepting the null hypothesis when it is false
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Rejecting the null hypothesis when it is true
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Accepting the null hypothesis when it is true
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Rejecting the null hypothesis when it is false
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A scatterplot of the residuals is NOT used to check for:
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normality of distribution of errors
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the relationship between variables
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a non-random distribution of the residuals
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homogeneity of variance of errors
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Which case will not be considered an influential in a regression of Y on X?
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A case with a mean of X and a large Y
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A case with a small X and a mean of Y
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A case with a large X and a small Y
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A case with a mean of X and a mean of Y
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When a regression line is fitted to the data, the lack of fit is best described by:
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A negative correlation coefficient
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A random distribution of the residuals
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A non-random distribution of the residuals
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A small unstandardised regression coefficient
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If the assumption of heteroscedasticity has been violated, the residual while display...?
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A random distribution of the residuals
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A curvilinear distribution of the residuals
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A denser portion of residuals above the line
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A fanning out of residuals to one side
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In multiple regression, the effect size of the prediction is given by the:
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multiple correlation coefficient
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squared multiple correlation coefficient
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sum of the squared beta coefficients
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sum of the individual squared correlations between the independent variable and the dependent variable
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In standard multiple regression where X1 & X2 are used to predict Y, the square of the multiple correlation (R2) is:
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the sum of the correlations between Y and X1 and between Y and X2
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the sum of the squared correlations between Y and X1 and between Y and X2
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the proportion of variance in Y uniquely & jointly predicted by X1 & X2
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the proportion of variance in Y uniquely & jointly predicted by X1 plus the proportion of variance in Y uniquely predicted by X2
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In a standard multiple regression, X1, X2 & X3 were used to predict Y. The obtained F statistic had a probability of .001. The unstandarised equation of predicted Y was .8X1 + .4X2 + .3X3 + .5. The semi-partial correlations of X1, X2 & X3 were .15, .25 & .05 respectively. We can conclude that....
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The set of predictors did not significantly predict Y
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X1 is a stronger predictor of Y than X2 or X3
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X3 alone would not significantly predict Y
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X2 makes the best unique contribution to the prediction of Y
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In a standard multiple regression of X1, X2 & X3 predicting Y, the tolerance value of X1 is the proportion of variance in X1 that is...
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In a table of intercorrelations, predictor X1 had the highest, and statistically significant, correlation with Y (the dependent variable). Which one of the following statement is FALSE?
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In a forward regression, X1 was not included in the final equation because its contribution was redundant to other predictors that entered the equation
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In a backward regression, X1 was not included in the final equation because its contribution was redundant to other predictors that entered the equation
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In a stepwise regression, X1 was not included in the final equation because its contribution was redundant to other predictors that entered the equation
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In a hierarchical regression, X1 was not relevant to the hypothesis being tested
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In a study that had collected initial weight, weight loss after training, motivation to lose weight, duration of training period, and amount of exercise, what is likely to be the dependent variable?
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Weight loss after training
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Motivation to loose weight
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Duration of training period
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Amount of excercise
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In the study described in (2), which of the following regression methods would allow the most sophisticated test of a theory on weight loss?
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Multiple regression
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Hierarchical regression
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Stepwise regression
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Backward regression
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In the study described in (2), if you were select a predictor to enter in the first step, which would you select?
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In a hierarchical regression predicting Y, predictors X1 and X2 as Subset A were first entered by the researcher, followed by predictors X3 and X4 as Subset B. Which one of the following statements is definitely true?
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Predictors in subset A are not correlated with predictors in subset B
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Predictors in subset A account for more variance in Y than predictors in subset B
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Total R2 is the sum of the changes in R2 due to the entry of Subset A and Sunset B
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Total R2 is the sum of the squared correlations between Y and each predictor
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Which one of the following is most likely a polytomous variable>
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Gender
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Age
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Marital status
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Income
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A researcher collected data on religious affiliation and involvement in gambling. Religious affiliation was a polytomous variable with four categories; namely, Christian, Buddhist, Muslim, and “No religion”. Three dummy variables (0,1), called Christian, Buddhist, and Muslim, were created to represent Christian, Buddhist, and Muslim affiliations, respectively. In a stepwise multiple regression to predict gambling, Christian and Buddhist were the two predictors selected. In interpreting the effect of Christian in this regression, which one of the following statements is definitely true?
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C tests the effect of Christian versus Buddhist on gambling
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C tests the effect of Christian versus Buddhist and Muslim on gambling
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C tests the effect of Christian versus Muslim and “No religion” on gambling
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C tests the effect of Christian versus “No Religion” on gambling
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In a large sample (N = 500), removing a poor fitting case with a large standardized residual (i.e., ZRE_1) is likely to
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Decrease R2 a little
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Decrease R2 a lot
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Increase R2 a little
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Increase R2 a lot
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The magnitude of the pearson product-moment correlation (r) between X & Y is not biased by:
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The external validity of an experiment can by improved by:
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using a representative sample of the population
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holding constant the effect of nuisance variables
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balancing the effect of nuisance variables
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random assignment of participants to treatment groups
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The external validity of an experiment can by improved by:
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using a representative sample of the population
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holding constant the effect of nuisance variables
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balancing the effect of nuisance variables
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random assignment of participants to treatment groups
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Balancing nuisance factors (e.g., sex, age) neutralizes their potential effects on the internal validity of an experiment, but nuisance factors can still
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decrease sensitivity and increase external validity
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decrease both sensitivity & external validity
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increase sensitivity & decrease external validity
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increase both sensitivity & external validity
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The defining characteristic of a true experiment is:
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the high degree pf control in testing
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the random assignment of subjects to experimental & control conditions
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the operationalisation of dependent variables
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the systematic manipulation of independent variables
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A sample of 100 participants were randomly allocated into two equal-sized weight loss programmes - one that focussed on dieting and the other that focussed on exercise. At the end of 20 weeks of training, 32 participants remained – 20 in the dieting group and 12 in the exercise group. Results show that the weight lost by the dieting group was significantly less than that of the exercise group. A criticism of this experiment is that the findings may be biased by the effect of:
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history
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selection bias
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attrition
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regression to the mean
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Use of control group, plus random assignment of participants to treatment, would allow us to avoid:
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selection bias
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attrition
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experimenter bias
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effects from testing
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The ANOVA tests for differences between
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A statistically significant result means that the obtained probability (p) of the test statistic is
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less than .05
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equal to .05
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greater than .05
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greater than 1
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If assumptions of the ANOVA are violated, the actual Type 1 error rate will...
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A Brain Training software company claims that its 20-week training programme can increase a person’s IQ by five points. Given that the normative mean of IQ is 100, with a standard deviation of 15, the magnitude of the claimed effect is
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negligible
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small
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medium
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large
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A Brain Training software company claims that its 20-week training programme can increase a person’s IQ by five points. Given that the normative mean of IQ is 100, with a standard deviation of 15, the magnitude of the claimed effect is
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negligible
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small
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medium
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large
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Upon obtaining a significant F test result in an ANOVA comparing mean weekly gambling frequency of Catholics, Protestants and Non-Christians, what test(s) would you use to compare the group means?
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In between subjects ANOVA, the error term is the unexplained variance of the dependent variable. A second facotr when included in a one-way ANOVA, making it a two-way ANOVA, has the effect of:
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controlling another source of variance, and increasing the error term
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controlling another source of variance, and decreasing the error term
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adding another source of variance, and increasing the error term
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adding another source of variance, and decreasing the error term
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In a two-way ANOVA, the number of possible F ratios is
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When a significant F ratio is found for interaction in ANOVA of two factors (A & B) the next steps is to test:
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A researcher performed a two-way between-subjects ANOVA - the p value obtained for the first factor (AGE) was .020, the p value obtained for the second factor (GENDER) was .060 and the p value for the interaction (AGE x GENDER) was .040. The most appropriate conclusion to make at this point, without further testing, is:
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age is statistically significant and gender is not statistically significant
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age is statistically significant under some level of gender
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gender is statistically significant under some level of age
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b or c, or both
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Planned orthogonal contrast should be used:
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for setwise comparison means
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for pairwise comparison means
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following a significant F in the oneway ANOVA
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in place of the overall F test in ANOVA
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The most conservative pairwise comparison between a set of four means is:
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In a conventional experiment involving five planned comparisons, the bonferroni method would have a set error rate to:
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Which of the following statements about a set of orthogonal contrasts between five groups is FALSE?
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the coefficients of a linear contrast sum to 0
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the cross products of coefficients of any pair of contrasts sum to 0
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the can at most be five contrasts in the set of orthogonal contrasts
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there are more than one set of orthogonal contrasts
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In an analysis of covariance (ANCOVA) on the final weight of participants in a group focussing on diet and those in a group focussing on exercise, it is important to partial out the effect of:
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initial weight
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age
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gender
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group
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In an one-way analysis of covariance (ANCOVA), there is the dependent variable (DV), the independent variable (IV), and the covariate (CV). The purpose of ANCOVA is to test:
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differences between the IV on the DV that is not explained by the CV
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differences between the IV on the CV that is not explained by the DV
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differences between the IV on the DV and the CV
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differences between the IV on the CV and the DV, after adjusting for each other
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In within-subjects ANOVA, the Greenhouse-Geisser epsilon-correction procedure is employed to handle an untenable assumption about the
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homogeneity of variances
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homogeneity of covariances
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independence of observations
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normality of distribution of observations.