Research skills 4

Question 1

what is correlational (observational) design?

Answer 1

• Quantitative description of trends, attitudes, or opinions of a population
• Testing association of X and Y

Question 2

what is an experimental design?

Answer 2

• Systematic manipulation of one or more variables (X) to evaluate an outcome (Y)
• Holds other variables constant to isolate effects
• allows to test causability

Question 3

correlation coefficients all boil down to a ratio of…

Answer 3

How much two variables vary together: How much two variables vary on their own

Question 4

which correlation coefficient is used for continous (numerical) data?

Answer 4

Pearson’s r

Question 5

By squaring the value of r we get the….

Answer 5

proportion of variance in one variable shared by the other (the overlap)

For example a coefficient of r = 0.6 indicates that 36% of the variance of X and Y is shared -> 0.6 * 0.6 = 0.36

Question 6

which correlation coefficients are used for ‘ranked’ data

Answer 6

• Spearman’s rho for few tied ranks
• Kendall’s tau for when there are tied ranks, and better for small samples

Question 7

what is the phi correlation and when should it be used?

Answer 7

The phi coefficient can be used when you have a 2x2 contingency table (two binary variables), and it quantifies the strength of association between these two variables. It is a measure of the degree of association or dependency between the two binary variables

Question 8

what is the point-biserial correlation and when should it be used

Answer 8

a statistical measure used to assess the strength and direction of the relationship between two variables when one of the variables is dichotomous (having two categories, often represented as 0 and 1) and the other is continuous. It is essentially a special case of the Pearson correlation coefficient (r) that is adapted for situations with one dichotomous variable.

Question 9

what is a partial correlation

Answer 9

Measures the relationship between two variables, controlling for the effect that a third variable has on them both

Question 10

what is semi-partial correlation

Answer 10

Measures the relationship between two variables controlling for the effect that a third variable has on only one of the variables in the correlation.

Question 11

what is a random variable?

Answer 11

they are What we measure in psychological research, are probabilistic quantities (not deterministic).

measured using the mean and standard deviations

Question 12

go through the simple regression pipeline

Answer 12

0. 2 variables (DV, IV)
1. Overall fit (R^2)
2. Test of overall fit (F)
   - only if the F statistic is significant )p<.05)
3. Coefficients (b0, bx)

Question 13

what does the F statistics tell us?

Answer 13

The F-statistic is a measure of overall significance or goodness-of-fit of the regression model.
It assesses whether the regression model explains a significant amount of variability in the dependent variable compared to a model with no independent variables (i.e., a null model).

Question 14

what does the R² statistic tell us?

Answer 14

The R-squared statistic measures the proportion of variance in the dependent variable that is explained by the independent variable(s) in the regression model.

Question 14

what do coeffiecients b₀ and b₁ tell us?

Answer 14

• The coefficient b₀ (also known as the intercept) represents the predicted value of the dependent variable when the independent variable is zero.
• The coefficient b₁ (also known as the slope) represents the change in the dependent variable for a one-unit change in the independent variable.

Question 14

what is multicollinearity?

Answer 14

The more two IVs are correlated with each other, the less sense it makes to keep both

Question 14

go through the multiple regression pipeline

Answer 14

0. N (at least 3) variables (1 DV, N IV)

1.Entry Method

2.Overall fit (R2)

3.Test of overall fit (F)
- if p(F) < (alpha)

4.
a.Coefficients (b0, bX1, … , bXN)
b.Zero-ord & Partial correlations

Question 14

R² is essentially the combination of..

Answer 14

• each IV’s unique contribution to the DV (unique variance)
  &
• shared variance

Question 15

what is ‘forced entry’

Answer 15

when all the predictors are entered at once

Question 16

what is hierarchical regression?

Answer 16

Hierarchical regression involves entering blocks of variables into the model in a predetermined order based on theoretical or conceptual considerations.

Question 17

what are stepwise, forward and backward regressions?

Answer 17

• Stepwise regression is a combination of forward selection and backward elimination.
  It iteratively adds and removes variables from the model based on predetermined criteria (e.g., significance level, change in R-squared).
• Forward selection starts with an empty model and iteratively adds one independent variable at a time.
  At each step, the variable that contributes the most to the model’s explanatory power (e.g., based on significance level, change in R-squared) is added to the model.
• Backward elimination starts with a model that includes all independent variables, and iteratively removes one variable at a time.
  At each step, the variable with the least contribution to the model’s explanatory power (e.g., based on significance level, change in R-squared) is removed from the model.

Question 18

what are the 7 assumptions to check for a multiple regression?

Answer 18

1. Independence
2. Variable Type
3. Sample Size
4. Linearity
5. Outliers and Influential Cases
6. Normality
7. Multicollinearity (Tolerance, VIF)

Question 19

explain the independence assumption

Answer 19

All values of the outcome should come from a different person:

• Each observation (raw in the dataset) comes from a unique individual
• Each individual is in one group and one group only
  Each group is made of different people

Question 20

what is the ‘variable type’ assumption?

Answer 20

Dependent Variable or DV: outcome must be continuous
Independent Variable(s) or IV(s): predictors can be continuous or categorical

Question 21

what is the ‘sample size’ assumption? and what are the 2 approaches?

Answer 21

The sample size assumption for multiple regression analysis is typically related to the number of observations (cases or data points) relative to the number of predictor variables included in the model.

1. Liberal (Stevens, 1996): 15 participants per predictor
   You need at least 10 for every variable you enter, and some would argue for as many as 50.
2. Conservative (Green, 1991):
   a) N = 50 + 8m (where m is the number of IVs) for testing the multiple correlation
   b) N = 104 +m for testing individual predictors (partial correlation)

Question 22

what is the ‘linearity’ assumption?

Answer 22

The linearity assumption in multiple regression refers to the assumption that there is a linear relationship between the independent variables (predictors) and the dependent variable (outcome). This assumption means that changes in the dependent variable are assumed to be proportional to changes in the independent variables, with a constant rate of change across all levels of the independent variables.

Question 23

what are the 2 kinds of outliers in the ‘outliers and influential cases’ assumption?

Answer 23

There are two kinds of outliers

Univariate: only present in one variable
Eg: one participant has a very different score from the rest of the sample

Multivariate: they result from the combination of two or more variables together
Eg: a participant’s scores are in the same range as the rest of sample in each variables (ie, not univariate outliers), but the overall pattern of their scores is off from the group (eg, one participant has the same exact score in all variables)

Question 24

what is the ‘normality’ assumption?

Answer 24

The normality assumption in multiple regression pertains to the distribution of the residuals (the differences between observed and predicted values) and not directly to the distributions of the independent or dependent variables themselves. The assumption states that the residuals should be normally distributed.

Question 25

what is the ‘Multicollinearity (Tolerance, VIF)’ assumption?

Answer 25

Multicollinearity exists if predictors are highly correlated. This assumption can be checked in SPSS, you access this option through the Statistics button when you set up a linear regression. Select Collinearity diagnostics

Question 26

what are the 3 tests for multivariate outliers?

Answer 26

a. Residual Statistics
Standardized Residuals (ΖΡΕ_1)
- , if ZRE_1 is greater than +3 or smaller than -3, is likely to be an outlier and of concern.

b. Mahalanobis distance
(MAH_1)

c. Influential cases
Cook’s (COO_1) distance
- Cook’s values greater than 1 (or closer to it) are a cause for concern.

Question 27

define z-score

Answer 27

A z-score, also known as a standard score, is a statistical measurement that describes a data point’s position relative to the mean of a group of data points. It is expressed in terms of standard deviations from the mean.

Question 28

Define Inferential statistics

Answer 28

Inferential statistics is a branch of statistics that focuses on making predictions or inferences about a population based on a sample of data drawn from that population

Question 29

define ‘central limit theorem’

Answer 29

The Central Limit Theorem (CLT) is a fundamental principle in statistics that states that the distribution of the sample mean (or sum) of a sufficiently large number of independent, identically distributed (i.i.d.) random variables approaches a normal (Gaussian) distribution, regardless of the original distribution of the variables

Question 30

define ‘standard error of the mean’

Answer 30

The standard error of the mean (SEM) is a statistical measure that quantifies the amount of variability or dispersion in the sample mean estimates of a population mean

Question 31

define t-score

Answer 31

A t-score is a type of standardized score used in statistics to compare the difference between an observed sample mean and the population mean when the population standard deviation is unknown and the sample size is relatively small

Question 32

define ‘confidence interval’

Answer 32

A confidence interval (CI) is a range of values, derived from sample data, that is likely to contain the true population parameter (such as the mean or proportion) with a specified level of confidence

Question 33

open questions:

ADVANTAGES:DISADVANTAGES

Answer 33

Writing the question response in any form the respondent feels is useful: (e.g., ‘What reasons are there for recycling?’)

Advantages
- Gets all the information

• Does not lead the respondent
• Is more naturalistic

Disadvantages
- Can be difficult to complete (requires listing)

• Difficult to code and analyse
• Poor when a numeric result is required

Question 34

closed questions:

ADVANTAGES:DISADVANTAGES

Answer 34

Require researcher to have a idea of the likely
response options (e.g., Which of the following do you recycle?:
Glass/paper/clothing/none of the above)

Advantages
- Easy to code and analyse

• Good when a numerical result is required
• Quick for respondents to answer

Disadvantages
- Can encourage bias

• Can miss possible answers
• Can create opinions where none exist

Question 35

define Split-half reliability

Answer 35

Split-half reliability measures the consistency of a test by dividing it into two equal halves and correlating the scores on each half. It assesses whether both halves produce similar results.

Question 36

define Internal reliability

Answer 36

Internal reliability, also known as internal consistency, refers to the extent to which all items or components of a test, survey, or measurement instrument measure the same underlying construct consistently

Question 37

define Cronbach’s Alpha

Answer 37

Cronbach’s Alpha is a measure of internal consistency, indicating how well a set of items in a test or survey measure a single unidimensional latent construct.

Question 38

what is Factor analysis

Answer 38

Factor analysis is a statistical method used to identify underlying relationships between variables by grouping them into factors. It reduces data dimensionality by detecting patterns of correlations.

Question 39

what are the two types of factor analysis?

Answer 39

Exploratory Factor Analysis (EFA): Used when the underlying structure is not known. It explores the potential factors without predetermined ideas.
Confirmatory Factor Analysis (CFA): Used to test hypotheses or theories about the structure of factors, confirming whether the data fits a pre-specified factor model.

Question 40

what are the 2 types of rotation

Answer 40

1. orthogonal – this type of rotation assumes that each factor is unique and has no shared associations. This tends to be used when testing a theoretical model that specifies independent factors (e.g. varimax).
2. oblique – this is more often used and determines the relationship of factors to one another rather than assuming independence (e.g. oblimin).

Question 41

what is ‘test-retest reliability’?

Answer 41

Correlation between the scores at two different times of testing. There are a number of factors that can affect this: the internal reliability of the test, external factors in the test sample (mood, fatigue etc.), carry over effects from the first testing period

Question 42

define validity

Answer 42

The extent to which a test measures what it is intended to measure

Question 43

define face validity

Answer 43

Face validity is the extent to which a test, measurement, or instrument appears to measure what it is intended to measure, based on subjective judgment. It refers to the degree to which the items on a test look like they are assessing the intended construct, at face value.