Quiz 4

A correlation coefficient close to -1 implies: A. an inverse relationship B. a positive relationship C. no correlation D. a significant difference of means between two groups

A

A correlation coefficient of 0.9 between two variables can be interpreted as: A. strong positive linear relationship B. moderate positive linear relationship C. no linear relationship D. correlation coefficients cannot be greater than 0.5

A

A difference of means test found that the mean difference in GPA between male and female students is 0.06 with a p-value of 0.000. This could be interpreted as: A. small in magnitude and statistically significant B. small in magnitude and not statistically significant C. large in magnitude and statistically significant D. large in magnitude and not statistically significant

A

A p-value of .03 means: A. that in only 3 in 100 times would a result as large as the one observed in a sample of data be observed if the null hypothesis were actually true for the population B. that the null hypothesis is correct in 3 of 100 times C. that the research hypothesis is true D. that 3% of the variation in the dependent variable is explained by the inclusion of an independent variable

A

Factorial ANOVA can help us explain more _________________ in DV and reduce the unexplained _______________. A. variance B. variables C. factors D. interaction effect

A

General question: What factors influence consumer purchase decision for energy drinks? RQ: How does carbonation, calories, and price impact consumer purchase decisions? What is IV1? A. Carbonation (2 levels: yes, no) B. Calories (3 levels: regular, low, zero) C. Price (2 levels: below $2.50, above $2.50) D. Purchase decision E. 2x3x2 (carbonation x calories x price)

A

IV (it designates the groups being compared) A. factor B. level C. factorial design D. 2 way ANOVA

A

In a _______________, High values of one variable are related to high values of other. A. positive relationship B. negative relationship C. no relationship

A

Null hypothesis: no correlation rxy=0 Research hypothesis: have correlation A. Step 1 B. Step 2 C. Step 3 D. Step 4

A

People who have high income tend to purchase more jewelry A. positive relationship B. negative relationship C. no relationship

A

People who have lower income tend to purchase less cars A. positive relationship B. negative relationship C. no relationship

A

Scenario: A researcher is studying how gender (1=male, 2=female) and age (1=below 25, 2=above 25) impact usage of Instagram. DV is measured based a 1 (least frequently) to 7 (most frequently) scale. What is the RQ? A. How does gender and age impact IG usage? B. Gender (2 levels: male, female) C. Age (2 levels: below 25, above 25) D. IG usage

A

The significance level is used to determine: A. the threshold at which we would reject the null hypothesis B. the probability of observing a particular result C. if there is a relationship between the two variables D. that two variables are not correlated in the general population

A

Which bivariate analysis is most appropriate for the following two variables: registered to vote (yes/no) and life satisfaction (from 1, not satisfied to 10, very satisfied)? A. Difference of means B. Correlation coefficient C. Descriptive statistics D. Data analysis

A

Which of the following investigates the relationships between two or more variables? A. Data analysis B. Descriptive statistics C. Regression coefficient D. Frequency distribution

A

Why is it important to assess the magnitude of the result whenever conducting a statistical test? A. Because some relationships between variables are statistically significant, but the magnitude of the relationship is actually quite small B. Because some correlations are spurious C. Because the difference of means test cannot be used on two variables with nominal measurement D. Because a higher coefficient always means that it will be statistically significant

A

_______________________ does not imply a _______________________ relationship —> it's a statistical technique that tells us how strongly the pair of variables are linearly related and change together. It does not tell us why and how behind the relationship but it just says the relationship exists. A. correlation --> causal B. causal --> correlation

A

a measure of linear relationship between variables —> specifies nature of the association between two continuous variables A. correlation B. Pearson Correlation Coefficient C. Coefficient of Determination

A

r = +/- 0.1 A. small relationship B. medium relationship C. large relationship

A

r=0 —> A. no relation; knowing X tells us nothing about Y B. perfect linear relationship; scores on X completely predictable from Y (and vice versa)

A

A design with m factors (m>1) is called an ___________________. A. 2 way ANOVA B. m-way factorial design

B

A p-value greater than 0.05 would support: A. the research hypothesis B. the null hypothesis C. a difference of means D. an inverse correlation

B

General question: What factors influence consumer purchase decision for energy drinks? RQ: How does carbonation, calories, and price impact consumer purchase decisions? What is IV2? A. Carbonation (2 levels: yes, no) B. Calories (3 levels: regular, low, zero) C. Price (2 levels: below $2.50, above $2.50) D. Purchase decision E. 2x3x2 (carbonation x calories x price)

B

If a study has _______ factors (gender, year of study), it will be called as a a two-way factorial design. A. 1 B. 2 C. 3 D. 4

B

In a ___________________,High values of one variable tend to go with low values of the other (or vice versa). A. positive relationship B. negative relationship C. no relationship

B

In factorial ANOVA there is a variable with _______ or more levels. A. 1 B. 2 C. 3 D. 4

B

Most studies in the social sciences set the significance level of a hypothesis test at: A. 1% B. 5% C. 10% D. 50%

B

Scenario: A researcher is studying how gender (1=male, 2=female) and age (1=below 25, 2=above 25) impact usage of Instagram. DV is measured based a 1 (least frequently) to 7 (most frequently) scale. What is the IV1? A. How does gender and age impact IG usage? B. Gender (2 levels: male, female) C. Age (2 levels: below 25, above 25) D. IG usage

B

Test statistics: r for correlation A. Step 1 B. Step 2 C. Step 3 D. Step 4

B

The older consumers are, the less they purchase soda products at the grocery store A. positive relationship B. negative relationship C. no relationship

B

The p-value can be defined as the probability of observing a result when the: A. null hypothesis is false B. null hypothesis is true C. research hypothesis is false D. research hypothesis is true

B

Which bivariate analysis is most appropriate for the following two variables: age and yearly income? A. Difference of means B. Correlation coefficient C. Descriptive statistics D. Data analysis

B

individual conditions/groups/values that make up a factor A. factor B. level C. factorial design D. 2 way ANOVA

B

r = +/- 0.3 A. small relationship B. medium relationship C. large relationship

B

r=-1 or +1 —> A. no relation; knowing X tells us nothing about Y B. perfect linear relationship; scores on X completely predictable from Y (and vice versa)

B

the strength and direction of a linear relationship between two continuous variables A. correlation B. Pearson Correlation Coefficient C. Coefficient of Determination

B

A correlation coefficient of 0 implies: A. an inverse relationship B. a positive relationship C. no correlation D. a significant difference of means between two groups

C

A statistical interaction occurs when the effect of one of the IVs on the DV changes depending on the level of another one of the IVs —> it's the crossover effect of the variable and other variables on the DV A. variance B. factors C. interaction effect D. main effect

C

Cross tabulation assesses the relationship between: A. four variables with nominal measurement B. four variables with ordinal measurement C. two variables with nominal measurement D. two variables with ordinal measurement

C

General question: What factors influence consumer purchase decision for energy drinks? RQ: How does carbonation, calories, and price impact consumer purchase decisions? What is IV3? A. Carbonation (2 levels: yes, no) B. Calories (3 levels: regular, low, zero) C. Price (2 levels: below $2.50, above $2.50) D. Purchase decision E. 2x3x2 (carbonation x calories x price)

C

In SPSS, Sig (2-tailed) is the: A. correlation coefficient B. mean difference between two groups C. p-value for a test D. confidence interval

C

Scenario: A researcher is studying how gender (1=male, 2=female) and age (1=below 25, 2=above 25) impact usage of Instagram. DV is measured based a 1 (least frequently) to 7 (most frequently) scale. What is the IV2? A. How does gender and age impact IG usage? B. Gender (2 levels: male, female) C. Age (2 levels: below 25, above 25) D. IG usage

C

The correlation coefficient falls between which two numbers? A. -1 and 0 B. 0 and 1 C. -1 and 1 D. 1 and 2

C

The effect of factor A on the DV varies depending on factor B A. variance B. factors C. interaction effect D. main effect

C

The mean differences between individuals treatment conditions, or cells, are different from what would be predicted from the overall main effects of the factors. A. variance B. factors C. interaction effect D. main effect

C

Which of the following evaluates whether there is a difference in the averages between the two groupings of the dependent variable, based on variation in the independent variable in the sample you are using: A. correlation coefficient B. regression analysis C. difference of means D. summary statistics

C

Which of the following is an example of bivariate analysis? A. Descriptive statistics B. Multiple regression analysis C. Correlation coefficient D. Summary statistics

C

a study that combines two or more factors A. factor B. level C. factorial design D. 2 way ANOVA

C

p-value for decision: 0.05 A. Step 1 B. Step 2 C. Step 3 D. Step 4

C

proportion of variance of one variable that is predictable from the other variable A. correlation B. Pearson Correlation Coefficient C. Coefficient of Determination

C

r = +/- 0.5 A. small relationship B. medium relationship C. large relationship

C

Correlations are sometimes spurious, meaning that: A. there is a positive correlation B. that the relationship between the variables is a causal one, but they are not correlated C. that the variables are correlated and caused one another D. that the variables are correlated, but in truth the relationship between them is not a causal one

D

General question: What factors influence consumer purchase decision for energy drinks? RQ: How does carbonation, calories, and price impact consumer purchase decisions? What is the DV? A. Carbonation (2 levels: yes, no) B. Calories (3 levels: regular, low, zero) C. Price (2 levels: below $2.50, above $2.50) D. Purchase decision E. 2x3x2 (carbonation x calories x price)

D

Make a decision: Reject the null, accept alternative: have correlation Accept the null, can't accept alternative: no significant correlation. A. Step 1 B. Step 2 C. Step 3 D. Step 4

D

Scenario: A researcher is studying how gender (1=male, 2=female) and age (1=below 25, 2=above 25) impact usage of Instagram. DV is measured based a 1 (least frequently) to 7 (most frequently) scale. What is the DV? A. How does gender and age impact IG usage? B. Gender (2 levels: male, female) C. Age (2 levels: below 25, above 25) D. IG usage

D

The null hypothesis for the difference of means test: A. posits that there will be a significant difference in the averages for the dependent variable for two groups of the independent variable B. posits that one group will have a higher average than another on the dependent variable C. is always significant D. posits that the difference in the averages for the dependent variable for two groups of the independent variable is 0

D

the effect of one of the IVs on the DV, ignoring the effects of all other IVs —> the effect of a variable by itself A. variance B. factors C. interaction effect D. main effect

D

General question: What factors influence consumer purchase decision for energy drinks? RQ: How does carbonation, calories, and price impact consumer purchase decisions? What is the design? A. Carbonation (2 levels: yes, no) B. Calories (3 levels: regular, low, zero) C. Price (2 levels: below $2.50, above $2.50) D. Purchase decision E. 2x3x2 (carbonation x calories x price)

E