SOCI 3040 - Finals Review

Question 1

Controlling for Variables

Answer 1

Multiple regression accounts for relationship between independent variables and allows to sort out how much variation in the dependent variable is attributable to each independent variable separately.

Question 2

Interpreting Multiple Regression

Answer 2

1) analyzing the correlation and directionality of the data
2) estimating the model i.e., fitting the line, and
3) evaluating the validity and usefulness of the model.

Question 3

Standardized Coefficients (BETA)

Answer 3

a way of converting the slope of coefficient to standard coefficient.

Question 4

Dummy Variables

Answer 4

Acts as a switch variable.

Question 5

Reference Category/Group

Answer 5

A category that is theoretically important in the researcher’s view also could be converted into the reference group

Question 6

Nested Regression

Answer 6

One way to build a regression model of the given data is to generate a series of regressions where: The dependent variable Y will remain the same. More independent variables will be added at each step to improve the model, keeping all the previous IVs in place.

Question 7

Parsimonious Factors

Answer 7

The simplest and the most efficient plausible explanations.

Question 8

Regression Assumptions

Answer 8

(1) A regression model strives to achieve a high degree of explained variance, encompassed by R2.
(2) A good regression model should avoid the issue of collinearity (correlation) between the independent variables.
(3) Normally Distributed

Question 9

Centring Independent Variables

Answer 9

center around it mean instead of 0.

Question 10

Hypothesis Testing in Regression

Answer 10

used to confirm if our beta coefficients are significant in a linear regression model.

Question 11

R-Squared/Coefficient of Determination

Answer 11

statistics based on variation that summarize how well the regression matches the data. Shows how well the relationship between two variables fits the straight line.

Question 12

Influential Cases

Answer 12

Cases that have a large effect on the model + introduce bias (may have a high residual + be an outlier).
- Have high leverage.

Question 13

Zero-order/Partial Correlation

Answer 13

The correlation within the sample overall is called zero-order correlation, and the correlation for each subgroup in the sample is called partial correlation.

Question 14

Spearman’s Rank-order Correlation

Answer 14

Popular, but requires several assumptions to be valid:
(1) Both variables must be continuous or at least count variables w/ a wide range of values
(2) Must have a linear relationship
(3) Must be normally distributed

Question 15

Reading Correlation Matrices

Answer 15

The diagonal cells always show the correlation of 1. The order of the variables is always the same.

Question 16

Adjusted R-Squared

Answer 16

look at the negative impact of adding variables to our model.

Question 17

Collinearity/Multicollinearity

Answer 17

Occurs when the linear relationship between one independent variable (e.g. age) and the dependent variable Y (e.g. weekly wages) is very similar to the relationship between another variable (e.g. year of birth)

Question 18

Variance Inflation Factor (VIF)

Answer 18

gives the estimation of how much the variance of the slope coefficient is likely to be inflated because the independent variable is correlated with other IVs in the regression

Question 19

Multiple Regression Model

Answer 19

an equation that describes the relationship between a dependent variable and more than one independent variable.

Question 20

Monotonic Relationships

Answer 20

a consistent increase on one variable is associated with a consistent increase (or decrease) on the other variable. E.g., linear relationships

Question 21

Limitations of Chi-square

Answer 21

It is sensitive to small sample sizes. Likely to give false positive.

Question 22

Chi-square hypothesis testing

Answer 22

compares the observed frequencies in a table to the expected frequencies we could receive if the two variables are independent in the population

Question 23

Elaboration Model

Answer 23

Looks at how the relationship between variables X and Y changes after controlling for a third variable Z (replication, specification, distortion, etc).

Question 24

CHI-Square Test of Independence

Answer 24

a statistical test in which both variables are categorical. This test generally tells us if the distribution of participants across categories is different from what would happen if there were no difference between the groups

Question 25

Proportionate Reduction of Error Measure (PRE)

Answer 25

shows how much the error in predicting the attributes of the dependent variable can be reduced if the attributes of the independent variable are known.

Question 26

The Hypothesis Testing

Answer 26

To find the difference in group means.

Question 27

Steps in Hypothesis Testing

Answer 27

(1) Set up the H₀ and Hₐ
(2) F-distribution and decide on the critical value. F𝒸ᵣ will depend on the α (usually, less than 0.05) level of significance and degrees of freedom.
(3) Calculate the F-statistics based on variation between groups, within groups, total variation and the degrees of freedom.
(4) compare p-value to a.
(5) Decide whether to reject the null hypothesis

Question 28

F-Distribution in ANOVA

Answer 28

a theoretical distribution used to compare differences between multiple means. Used to find the likelihood of randomly selecting a sample with the observed ratio of between-group variation to within-group variation

Question 29

Post-Hoc Test

Answer 29

• Practically the LSD test.
• A means of comparing all possible pairs of groups to determine which ones differ significantly from each other

Question 30

Between Group

Answer 30

the difference between the group means

Question 31

Within Group

Answer 31

shows how the cases distribute around the mean in each group

Question 32

Residuals

Answer 32

Not normally distributed indicate that there might be one or more important IVs that were omitted from the regression.

Question 33

Pearson’s Correlation Coefficient (R)

Answer 33

shows the strength and direction of linear relationship between two variables. A coefficient of -1/+1 shows that there is a perfect negative/positive relationship between the variables, and the coefficient of 0 indicates no relationship.