Hypothesis testing

Data analysis

Experimental design

All of the above

To confirm a hypothesis

To reject a hypothesis

To determine the probability of a hypothesis being true

To generate new hypotheses

Descriptive statistics summarize data, while inferential statistics make predictions about a population.

Descriptive statistics are used to describe a sample, while inferential statistics are used to generalize to a population.

Descriptive statistics are based on observed data, while inferential statistics are based on theoretical models.

All of the above

Mean

Median

Mode

All of the above

t-test

ANOVA

Regression analysis

All of the above

To control for confounding variables

To ensure that the results of a study are valid and reliable

To increase the likelihood of finding a statistically significant result

All of the above

A variable that is related to both the independent and dependent variables

A variable that is not related to either the independent or dependent variables

A variable that is related to the independent variable but not the dependent variable

A variable that is related to the dependent variable but not the independent variable

A correlation is a relationship between two variables, while a causation is a relationship in which one variable causes the other.

A correlation is a relationship between two variables that are both affected by a third variable, while a causation is a relationship in which one variable directly causes the other.

A correlation is a relationship between two variables that are both random, while a causation is a relationship in which one variable is determined by the other.

A correlation is a relationship between two variables that are both measured at the same time, while a causation is a relationship in which one variable is measured before the other.

To predict the value of a dependent variable based on the values of one or more independent variables

To determine the relationship between two or more variables

To control for confounding variables

All of the above

A simple regression has one independent variable, while a multiple regression has two or more independent variables.

A simple regression is used to predict the value of a continuous dependent variable, while a multiple regression is used to predict the value of a categorical dependent variable.

A simple regression is used to determine the relationship between two variables, while a multiple regression is used to control for confounding variables.

A simple regression is used to make predictions about a population, while a multiple regression is used to make predictions about a sample.

To reduce the number of variables in a dataset

To identify the underlying structure of a dataset

To create a new set of variables that are uncorrelated with each other

All of the above

Exploratory factor analysis is used to generate hypotheses about the structure of a dataset, while confirmatory factor analysis is used to test hypotheses about the structure of a dataset.

Exploratory factor analysis is used to identify the underlying factors in a dataset, while confirmatory factor analysis is used to determine the number of factors in a dataset.

Exploratory factor analysis is used to create a new set of variables that are uncorrelated with each other, while confirmatory factor analysis is used to reduce the number of variables in a dataset.

Exploratory factor analysis is used to make predictions about a population, while confirmatory factor analysis is used to make predictions about a sample.

To test hypotheses about the relationships between variables

To create a model of the relationships between variables

To estimate the parameters of a model

All of the above

A path analysis is a type of structural equation model that is used to test hypotheses about the relationships between variables.

A path analysis is a type of structural equation model that is used to create a model of the relationships between variables.

A path analysis is a type of structural equation model that is used to estimate the parameters of a model.

A path analysis is a type of structural equation model that is used to do all of the above.

To combine the results of multiple studies

To increase the sample size of a study

To determine the overall effect of a treatment

All of the above