PSY2PRM
An ANCOVA is most similar conceptually, to: Stepwise Regression Logistic Regression Hierarchical Regression Forward Regression
Hierarchical Regression
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? (A) In a forward regression, X1 was not included in the final equation because its contribution was redundant to other predictors that entered the equation. (B) In a backward regression, X1 was not included in the final equation because its contribution was redundant to other predictors that entered the equation. (B) In a stepwise regression, X1 was not included in the final equation because its contribution was redundant to other predictors that entered the equation. (D) In a hierarchical regression, X1 was not relevant to the hypothesis being tested.
(A) In a forward regression, X1 was not included in the final equation because its contribution was redundant to other predictors that entered the equation.
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? In the study described in (2), if you were select a predictor to enter in the first step, which would you select? (A) Initial weight (B) Motivation to lose weight. (C) Duration of training period. (D) Amount of exercise
(A) Initial weight
Which one of the following does not increase the power of a test of r between X and Y? (A) Standardising the scores of X and Y. (B) Adopting a larger Type I error rate. (C) Conducting a one-tailed test instead of a two-tailed test. (D) Increasing the magnitude of r.
(A) Standardising the scores of X and Y.
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? (A) Weight loss after training. (B) Motivation to lose weight. (C) Duration of training period. (D) Amount of exercise.
(A) Weight loss after training.
Balancing nuisance factors (e.g., sex, age) neutralizes their potential effects on the internal validity of an experiment, but nuisance factors can still (A) decrease sensitivity and increase external validity. (B) decrease both sensitivity and external validity. (C) increase sensitivity and decrease external validity. (D) increase both sensitivity and external validity.
(A) decrease sensitivity and increase external validity.
A statistically significant result means that the obtained probability (p) of the test statistic is (A) less than .05. (B) equal to .05. (C) greater than .05. (D) greater than 1.
(A) less than .05.
Use of a control group, plus random assignment of participants to treatment, would allow us to avoid: (A) selection bias. (B) attrition. (C) experimenter bias. (D) effects arising from testing
(A) selection bias.
The simple correlations of X1, X2, and X3 with Y are .30, .20, and .10 respectively. A multiple regression of X1 and X2 predicting Y was just statistically significant at p = .04. If X3 is added to the equation predicting Y, we cannot be sure that: (A) the p value will decrease. (B) the R value will increase. (C) the tolerances of X1 and X2 will decrease. (D) the semi-partial correlations of X1 and X2 will decrease
(A) the p value will decrease.
The external validity of an experiment can be improved by: (A) using a representative sample of the population. (B) holding constant the effect of nuisance variables. (C) balancing the effect of nuisance variables. (D) random assignment of participants to treatment groups.
(A) using a representative sample of the population.
In the four bivariate relationships given below, in terms of Pearson product-moment correlations (r), the strongest relationship is the one for which r is: (A) .69. (B) -.70. (C) -.10. (D) .01
(B) -.70.
In a conventional experiment involving five planned comparisons, the Bonferroni method would have set the error rate to: (A) < .01 (B) .01 (C) < .05 (D) .05
(B) .01
In a two-way ANOVA, the number of possible F ratios is: (A) 4 (B) 3 (C) 2 (D) 1
(B) 3
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? In the study described in (2), which of the following regression methods would allow the most sophisticated test of a theory on weight loss? (A) Multiple regression. (B) Hierarchical regression. (C) Stepwise regression (D) Backward regression
(B) Hierarchical regression.
In clinical trials, it is ethically responsible to use a sample size that is sufficient to detect an effect but not too large as to inconvenienced the patients any longer than necessary. Which sample size should the researcher use to ensure that she has sufficient power? (A) N = 15 (power of .70) (B) N = 22 (power of .80) (C) N = 30 (power of .90) (D) N = 37 (power of .95)
(B) N = 22 (power of .80)
In between-subjects ANOVA, the error term is the unexplained variance of the dependent variable. A second factor when included in a one-way ANOVA, making it a two-way ANOVA, has the effect of: (A) controlling another source of variance, and increasing the error term. (B) controlling another source of variance, and decreasing the error term. (C) adding another source of variance, and increasing the error term. (D) adding another source of variance, and decreasing the error term.
(B) controlling another source of variance, and decreasing the error term.
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 (A) negligible. (B) small. (C) medium. (D) large.
(B) small.
In multiple regression, the effect size of the prediction is given by the: (A) multiple correlation coefficient. (B) squared multiple correlation coefficient. (C) sum of the squared beta coefficients. (D) sum of the individual squared correlations between the independent variables and the dependent variable.
(B) squared multiple correlation coefficient.
The most conservative pairwise comparison between a set of four means is: (A) the t-test. (B) the Scheffe test. (C) the Student Newman-Keuls test. (D) the Tukey test
(B) the Scheffe test.
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? (A) the Student t-test. (B) the Student-Newman-Keuls test. (C) the Tukey HSD test. (D) the Tukey-Kramer test.
(B) the Student-Newman-Keuls test.
The defining characteristic of a true experiment is: (A) the high degree of control in testing. (B) the random assignment of subjects to experimental and control conditions. (C) the operationalisation of dependent variables. (D) the systematic manipulation of independent variables
(B) the random assignment of subjects to experimental and control conditions.
A scatterplot of the residuals is NOT used to check for: (A) normality of distribution of errors. (B) the relationship between variables. (C) the presence of influential cases. (D) homogeneity of variance of errors.
(B) the relationship between variables.
The WAIS intelligence quotient (IQ) is normally distributed with a mean of 100 and a standard deviation of 15. If an IQ between 85 and 115 is deemed normal, what percentage (approximately) of the general population would be considered normal? (A) 30% (B) 50% (C) 70% (D) 90%
(C) 70%
When a regression line is fitted to the data, the lack of fit is best indicated by: (A) A negative correlation coefficient. (B) A random distribution of the residuals. (C) A non-random distribution of the residuals. (D) A small unstandardised regression coefficient.
(C) A non-random distribution of the residuals.
Which of the following statements about a set of orthogonal contrasts between five groups is FALSE? (A) The coefficients of a linear contrast sum to 0. (B) The cross-products of coefficients of any pair of contrasts sum to 0. (C) There can, at most, be five contrasts in the set of orthogonal contrasts. (D) There are more than one set of orthogonal contrasts
(C) There can, at most, be five contrasts in the set of orthogonal contrasts.
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? (A) Predictors in Subset A are not correlated with predictors in Subset B. (B) Predictors in Subset A account for more variance in Y than predictors in Subset B. (C) Total R² is the sum of the changes in R² due to the entry of Subset A and Subset B. (D) Total R² is the sum of the squared correlations between Y and each predictor.
(C) Total R² is the sum of the changes in R² due to the entry of Subset A and Subset B.
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: (A) history. (B) selection bias. (C) attrition. (D) regression to the mean
(C) attrition.
The ANOVA tests for differences between (A) variances of the groups. (B) standard deviations of the groups. (C) means of the groups. (D) individuals of the groups
(C) means of the groups.
In a standard multiple regression of X1, X2 and X3 predicting Y, the tolerance value of X1 is the proportion of variance in X1 that is: (A) shared with X2 and X3 but not with Y. (B) shared with Y but not with X2 and X3. (C) not shared with X2 and X3. (D) shared with X2 and X3
(C) not shared with X2 and X3.
Residual analysis is conducted in regression to: (A) get a more significant statistical result (i.e., t value) (B) get a higher r value. (C) reduce the influence of misrepresentative cases. (D) reduce the sample size
(C) reduce the influence of misrepresentative cases.
When a significant F ratio is found for interaction in ANOVA of two factors (A and B) the next step is to test: (A) main effect of factor A. (B) main effect of factor B (C) simple main effects of A and B. (D) association of A and B.
(C) simple main effects of A and B.
When values of X do not exactly predict values of Y, the R2 value is: (A) less than 0. (B) 0. (C) somewhere between 0 and 1. (D) 1
(C) somewhere between 0 and 1.
In standard multiple regression where X1 and X2 are used to predict Y. The square of the multiple correlation (R2), is: (A) the sum of the correlations between Y and X1 and between Y and X2. (B) the sum of the squared correlations between Y and X1 and between Y and X2. (C) the proportion of variance in Y uniquely and jointly predicted by X1 and X2. (D) the proportion of variance in Y uniquely predicted by X1 plus the proportion of variance in Y uniquely predicted by X2
(C) the proportion of variance in Y uniquely and jointly predicted by X1 and X2.
A researcher performed a two-way between-subjects ANOVA - the p value obtained for the first factor (AGE) was .02, the p value obtained for the second factor (GENDER) was .06 and the p value for the interaction (AGE x GENDER) was .04. The most appropriate conclusion to make at this point, without further testing, is: (A) AGE is statistically significant and GENDER is not statistically significant. (B) AGE is statistically significant under some level of GENDER. (C) GENDER is statistically significant under some level of AGE. (D) (B) or (C), or both.
(D) (B) or (C), or both.
Which case will not be considered an influential in a regression of Y on X? (A) A case with a mean of X and a large Y. (B) A case with a small X and a mean of Y. (C) A case with a large X and a small Y. (D) A case with a mean of X and a mean of Y.
(D) A case with a mean of X and a mean of Y.
In a standard multiple regression, X1, X2 and X3 were used to predict Y. The obtained F statistic had a probability of .001. The unstandardised equation of predicted Y was 0.8X1 + 0.4X2 + 0.3X3+ 0.5. The semi-partial correlations of X1, X2 and X3 were .15, .25, and .05, respectively. We can conclude that: (A) The set of predictors did not significantly predict Y. (B) X1 is a stronger predictor of Y than X2 or X3. (C) X3 alone would not significantly predict Y. (D) X2 makes the best unique contribution to the prediction of Y.
(D) X2 makes the best unique contribution to the prediction of Y.
If assumptions of the ANOVA are violated, the actual Type I error rate will (A) be greater than α. (B) equal α. (C) be smaller than α. (D) be smaller or greater than α.
(D) be smaller or greater than α.
Planned orthogonal contrasts should be used : (A) for setwise comparison of means. (B) for pairwise comparison of means. (C) following a significant F in the one-way ANOVA. (D) in place of the overall F test in ANOVA.
(D) in place of the overall F test in ANOVA.
In correlational analysis, skewed variables are transformed to be normally distributed because (A) this increases the relationship between variables. (B) this decreases the relationship between variables. (C) this removes the effect of influential points. (D) this might invalidate the test.
(D) this might invalidate the test.
Cohen's d suggests that: .2 is a small effect scores >1 are not possible .5 is a medium .8 is a large
.2 is a small effect
An ad hoc inference is: A 'gut' or intuitive inference based on recent past experience Based on scientifically captured data As reliable as a statistical inference A scientific judgement
A 'gut' or intuitive inference based on recent past experience
When performing a Forward regression compared to a standard multiple regression using the same number of predictors.... A larger N would be required for the Forward regression A smaller N would be required for the Forward regression A smaller p criterion is set The same N would be required
A larger N would be required for the Forward regression
A 'dummy variable' is: A variable created to recode a polytomous predictor A scalable polytomous variable A polytomous predictor A variable that cannot be used in regression
A variable created to recode a polytomous predictor
The main reason we use ABBA designs over ABAB designs is: ABAB designs are more likely to falsely suggest treatment effects than ABBA designs ABBA designs are shorter duration ABAB designs are good for practice but not fatigue effects ABBA were a great band
ABAB designs are more likely to falsely suggest treatment effects than ABBA designs
If we are looking for evidence to support the hypothesis that 3 psychological techniques predict sporting performance, we would hope that: The three predictors are highly correlated with each other All three independent variables are uniquely related to the dependent variable The tolerance statistic of several independent variables is < .10 The dependent variable is not too highly correlated with the independent variables
All three independent variables are uniquely related to the dependent variable
A 4 x 4 ANOVA would be: the product of each column wise contrast (e.g., contrast 1 x contrast 2) sums to 0 An example of a 8 factor ANOVA An example of a two way ANOVA An example of a sixteen way ANOVA
An example of a two way ANOVA
Post-hoc tests of a simple main effects analysis: Would be used if you had a non-significant simple main effect Are used after a significant main effect when there are more than 2 levels in the factor Are generally considered redundant Are not necessary when you have 3 or more levels in your factor
Are used after a significant main effect when there are more than 2 levels in the factor
Three types of statistical regression are: Backward, Stepwise, Forward Stairwise, Forward, Reverse Hierarchical, Forward, Backward Reverse, Forward, Statistical
Backward, Stepwise, Forward
Two-way ANOVA differs from One-way ANOVA: Because you can analyse the data two ways Because you can calculate main effects and an interaction effect Because Two-way ANOVA is for two groups Because one uses a F test and the other dies not
Because you can calculate main effects and an interaction effect
The assumptions that need to be satisfied for Pearson correlation are: Heteroscedascity and independence That the data comes from a valid and reliable sample Bivariate normality and independence Bivariate normality and heteroscedascity
Bivariate normality and independence
Heterogeneity of subsamples can be best assessed by: By using a hierarchical regression and assessing if the slopes for each subsample differ from parallel By assessing if slopes 'cross-over' each other Studying separate residual plots for each subgroup By using a hierarchical regression and assessing if the intercepts differ between subsamples
By using a hierarchical regression and assessing if the slopes for each subsample differ from parallel
A Type I error refers to: Cases when the alternate hypothesis is correctly accepted Cases when the null hypothesis is incorrectly rejected Cases when the null hypothesis is correctly rejected Making an error in calculating the statistic (No, this is not correct, though this may cause a Type 1 error)
Cases when the null hypothesis is incorrectly rejected
Simple linear regression: Is best used for estimating curvilinear relationships Gives an indication of the strength of the relationship between variables and is used for assessing straight line associations Can involve measuring the association of several independent variables with the dependent variable Does not give an indication of the strength of a relationship between variables
Gives an indication of the strength of the relationship between variables and is used for assessing straight line associations
When we 'test' participants in an experimental setting, internal validity may be compromised due to: non-applicability to real-world settings AND small sample Hawthorne effects non-applicability to real-world settings small sample
Hawthorne effects
What is the correct list of these tests from most to least powerful? Scheffe, Tukey, SNK t-test, Tukey, SNK Bonferroni, Holms, SNK, Tukey Holms, SNK, Tukey, Scheffe
Holms, SNK, Tukey, Scheffe
It is NOT possible to use a measure as an independent variable if the format is: La Trobe University, Murdoch University, Melbourne University Height in millimetres < 20 years old, 20-29 years, 30-39 years Voting Yes/No
La Trobe University, Murdoch University, Melbourne University
Hierarchical regression is not useful for: Covariance analysis Hypothesis testing Reducing predictors in a data set Removing the effects of nuisance variables
Reducing predictors in a data set
Sample statistics are represented by Roman letters
Sample statistics differ from population parameters as: Sample statistics are represented by Roman letters Population parameters refer to a subset from the sample Sample statistics cannot be used in statistical formulae Population parameter are represented by Greek letters
The Mauchly test is an assessment of: Sphericity Homogeneity of slopes Normality How much I hate stats!
Sphericity
You have been exposed to various forms of regression so far- including: Logistic regression Reduction regression Stepwise regression Poisson Regression
Stepwise regression
The p value associated with an r value: Tells us if the p value and r value are related Tells us if the r value significantly differs from the null hypothesis Tells us if the direction of the relationship is statistically significant Tells us if the strength of the relationship is strong
Tells us if the r value significantly differs from the null hypothesis
The advantage of reporting standardised statistics is: That a person unfamiliar with the variable properties can still interpret the effect size That it explains less variance than the unstandardized form That it explains more variance than the unstandardized form That it tells us if the variables are significantly related to each other
That a person unfamiliar with the variable properties can still interpret the effect size
If we assessed if the use of various psychological techniques were related with improved sporting performance and the finding was that the set of 3 techniques predicted performance R² = .41, F(3, 25), p = .04. We could state: That there were 4 independent variables That there were 25 independent variables That there were 29 scores in the data set That the finding was non - significant
That there were 29 scores in the data set
The difference between a A One way ANOVA and a post hoc test is: Post-hoc tests are more powerful than ANOVA A one way ANOVA has several factors and the the post hoc-test has only one The ANOVA is exploratory and the post hoc tests are based on theory The ANOVA tests if the within variance is significantly less than the between group variance
The ANOVA tests if the within variance is significantly less than the between group variance
R² is derived from... The combination of unique contributions from each predictor The combination of unique and shared contributions of the X's with Y The combination of the shared variances between all Y's with the X The combination of the shared variances between all X's with the Y
The combination of unique and shared contributions of the X's with Y
If the R²change is not significant in a hierarchical regression we can say that: None of the variables were related to the DV The predictors shared too much variance The group of predictors in this step did not improve the model fit The N was too small
The group of predictors in this step did not improve the model fit
Tau is a statistically contrived indication of: How people respond to changes in the environment The normal fluctuations we see in an individual over time An assessment of individual and systematic variation Between-subject variation
The normal fluctuations we see in an individual over time
An interaction effect is achieved when: When the partial eta square is > .14 When the main effects are not significant When the plot of the group means shows that the lines crossover The p value for the F test is significant
The p value for the F test is significant
The ρ symbol refers to: The population r* The sample correlation value The direction of the relationship between variables The probability of the r value being significant
The population r*
Incomplete within-subjects counterbalancing designs may be used in preference to a complete-within-subject counterbalancing design because: The researcher wants to allocate more participants to each sequence The researcher wants to use ABBA counterbalancing The researcher want to test all subjects with the same controlled sequence The researcher wants to achieve higher scientific rigor
The researcher wants to allocate more participants to each sequence
When Mauchly's test of sphericity is non-significant: We have violated the assumption of compound symmetry There is not a significant interaction between individuals and treatment and we can have confidence the F test is valid We know that there are no differences between groups Compound symmetry assumption is not violated but sphericity is violated
There is not a significant interaction between individuals and treatment and we can have confidence the F test is valid
Within subject ANOVA separates the within subject variability from the between treatment variability: Because compound symmetry needs to be violated Because we only want to assess the within group effect To allow a comparison of between and within sources variation Because this is a source of error that when removed decreases the chance of a statistically significant result
To allow a comparison of between and within sources variation
Planned comparisons: Use the Bonferroni or Holm's methods to distribute the .05 alpha level Use the Scheffe test to control for Type 1 error Use the Tukey test to control for Type 1 error Are used after you have a significant ANOVA finding
Use the Bonferroni or Holm's methods to distribute the .05 alpha level
If our 2 x 2 ANOVA shows we have a significant interaction effect: We should report the main effects in preference The homogeneity of the test is questionable We would need to conduct further analyses to understand how factors impacted upon each other There is no need to do additional analyses to understand the results
We would need to conduct further analyses to understand how factors impacted upon each other
Post-hoc setwise comparisons: are used after you have found none of the pairwise comparisons are significant differ to pairwise comparisons because they are less conservative When you wish to compare several group means against another mean(s) use Tukey or Student-Neuman-Kuels tests to calculate critical difference scores
When you wish to compare several group means against another mean(s)
In a 2 x 8 ANOVA, a simple main effects analysis: Would involve 2 main effects analyses Would involve 10 simple main effects analyses Is only used when you have more than 2 levels in a factor Would involve 16 main effects analyses
Would involve 10 simple main effects analyses
You are more likely to attain a higher R² when: Your set of independent variables share substantial variance with each other Your dependent variable is not related with the independent variables Your independent variables are highly correlated Your independent variables capture substantial unique components of the dependent variable
Your independent variables capture substantial unique components of the dependent variable
Multicollinearity is..... a necessary element to proceed with a multiple regression a problem as highly correlated independent variables violates the assumption for multiple regression an assumption that needs to be satisfied to proceed with a multiple regression a problem as highly correlated dependent variables violates the assumption for multiple regression
a problem as highly correlated independent variables violates the assumption for multiple regression
Multiple regression involves: a problem as highly correlated dependent variables violates the assumption for multiple regression a necessary element to proceed with a multiple regression a problem as highly correlated independent variables violates the assumption for multiple regression an assumption that needs to be satisfied to proceed with a multiple regression
a problem as highly correlated independent variables violates the assumption for multiple regression
A correlation coefficient is: determines is a causes b Used only to determine the direction of relationship between variables Unable to determine the strength of the relationship bounded between -1 and 1
bounded between -1 and 1
A scatterplot.... can be used to identify heterogeneous subsamples and outliers in the data set is used as a last resort to identify outliers in the data set uses a formula to calculate the p value can be used to assess if two variables are significantly correlated
can be used to identify heterogeneous subsamples and outliers in the data set
Outliers: increase the strength of a correlation can increase, reduce or have no substantive impact on the strength of a relationship Have no impact on the relationship between variables reduce the strength of a correlation
can increase, reduce or have no substantive impact on the strength of a relationship
X is the independent variable and Y is the .. predictor variable mediator dependent variable slope variable
dependent variable
The Solomon group design is useful as it allows for an assessment of: the effects of pre-testing difference scores between groups AND the effects of pre-testing the effects of post-testing difference scores between groups
difference scores between groups AND the effects of pre-testing
'Fanning' of the data points to one side of the scatterplot may indicate: non-normality linearity heterogeneity of variance homogeneity of variance
heterogeneity of variance
Trend analysis is useful: if you want to compare if a course of anti-inflammatory treatment reduces depressive symptoms between very high, high, medium and low and very low socio-economic groups if you want to determine if differences in IQ between autistic, Downes syndrome or Fragile X populations follow a quadratic trend if you want to compare if a course of anti-inflammatory treatment reduces depressive symptoms between very high, high, medium and low and very low socio-economic groups AND if you want to test a theory that the positive relationship between the number of psychology treatment sessions and improved self-esteem are not linear if you want to test a theory that the positive relationship between the number of psychology treatment sessions and improved self-esteem are not linear
if you want to compare if a course of anti-inflammatory treatment reduces depressive symptoms between very high, high, medium and low and very low socio-economic groups AND if you want to test a theory that the positive relationship between the number of psychology treatment sessions and improved self-esteem are not linear
Random allocation to groups helps control for: participant mortality small effect sizes individual differences experimenter bias
individual differences
When using an ANCOVA, the homogeniety of slopes: are assessed using a tw-way ANOVA is an assumption that needs to be satisfied before proceeding is less important than heterogeneity of slopes is not a problem if you do not block the covariate
is an assumption that needs to be satisfied before proceeding
An appropriate covariate reduces the error in an ANCOVA model and this: Is necessary to satisfy the assumption of normality makes finding a significant model more likely Increases the Type 1 error rate makes finding a significant model less likely
makes finding a significant model more likely
A data point that is has a Z residual score of 2.23.... may be trimmed to a lower value so it is no longer considered an outlier would be considered significant would also have a leverage value suggesting it was an outlier would also have a covariance ratio value suggesting it was an outlier
may be trimmed to a lower value so it is no longer considered an outlier
Removing outliers from the dataset... may reduce the ability to report a significant finding as the sample size will decrease is inappropriate as you are altering the dataset reduces the strength of the association between variables increases the strength of the association between variables
may reduce the ability to report a significant finding as the sample size will decrease
When choosing a post-hoc test after a within group ANOVA, we DON'T need to consider: the error rate our sample size how many means are being compared the error rate
our sample size
What makes an experiment an experiment? random sampling AND controlled extraneous variables controlled extraneous variables random allocation of participants to groups random sampling
random allocation of participants to groups
Internal validity may be improved by: using a control group reducing measurement error reducing measurement error AND using more homogenous samples AND using a control group using more homogenous samples
reducing measurement error AND using more homogenous samples AND using a control group
When conducting a one-way independent ANOVA with three levels on the independent variable, an F-ratio that is large enough to be statistically significant tells us: that there is a significant three-way interaction that one or more of the differences between means is statistically significant but not where the differences between groups lie that all of the differences between means are statistically significant that the model fitted to the data accounts for less variation than extraneous factors, but it doesn't tell us where the differences between groups lie
that one or more of the differences between means is statistically significant but not where the differences between groups lie
A multiple regression may not be statistically significant due to: the R² being too small the p value being too small the R² being too large the sample being too large
the R² being too small
The obtained F value of a study that uses a one-way ANOVA to test for differences between 4 means is 2.30, this means that: the groups differ the between group variance was greater than the within group variance, but we don't know if it is significantly greater as it is over 1, you would need to conduct post-hoc testing the result is significant
the between group variance was greater than the within group variance, but we don't know if it is significantly greater
The critical difference score attained after a post-hoc Tukey test tells us: the maximum distance apart the means can be nothing until it is compared to the Q table the difference between means that needs to be matched or exceeded to accept the alternate hypothesis the effect size of the comparison
the difference between means that needs to be matched or exceeded to accept the alternate hypothesis
A one -way ANOVA produces an eta square effect size of .17. This means: the effect size is large the effect is medium strength the result will be significant as the effect is large the effect is likely due to chance
the effect size is large
The ability to generalise our findings from an experimental design is governed by: the sample of participants in the study the sample of participants in the study AND how the variables have been operationalised the sample of participants in the study AND a cross sectional design how the variables have been operationalised
the sample of participants in the study AND how the variables have been operationalised
Which one of the following measures cannot be used as a predictor in a multiple regression? the sample of participants in the study the sample of participants in the study AND how the variables have been operationalised the sample of participants in the study AND a cross sectional design how the variables have been operationalised
the sample of participants in the study AND how the variables have been operationalised
Multiple t-tests are not recommended because: they inflate the experiment wise error rate they increase the Type 1 error rate they increase the family-wise error rate they inflate the experiment wise error rate AND they increase the family-wise error rate AND they increase the Type 1 error rate
they inflate the experiment wise error rate AND they increase the family-wise error rate AND they increase the Type 1 error rate
The Student Neuman-Keuls test is used in preference to the Tukey when: when the Tukey test is too conservative we have 3 group means we have small effect sizes we have 3 or more group means
we have 3 group means
In order to ensure that our contrasts are linear and orthogonal: we need to have no more than K-1 contrasts the product of each column wise contrast (e.g., contrast 1 x contrast 2) sums to 0 we need to have no more than K-1 contrasts AND the product of each column wise contrast (e.g., contrast 1 x contrast 2) sums to 0 AND the sum of all coefficients in a single contrast sum to 0 the sum of all coefficients in a single contrast sum to 0
we need to have no more than K-1 contrasts AND the product of each column wise contrast (e.g., contrast 1 x contrast 2) sums to 0 AND the sum of all coefficients in a single contrast sum to 0
