ANOVA Concepts and Applications Quiz

A variable that explains the mechanism by which an independent variable affects a dependent variable.

A variable that determines when or under what conditions an independent variable has an effect on the dependent variable.

A variable that unintentionally affects both the independent and dependent variable, potentially distorting the true relationship between them. Ideally, it should be controlled for in the study design.

A variable that is measured on a continuous scale and controlled for in the analysis to reduce variability and improve the accuracy of results.

A variable that is not the primary focus of the study but can influence the dependent variable. Its effects are not analyzed specifically.

A variable that influences the independent variable but is not included in the analysis. It can impact the independence of the independent variable.

A statistical test used to analyze the difference between means of two or more groups, where each group represents a different level of a factor.

A generalization of the One-Way ANOVA, where you have two or more factors with at least two levels each. The levels of each factor are independent of each other.

A statistical test combining categorical and metric independent variables to analyze differences in means between groups. Categorical variables are treated as factors (grouping variables), while metric variables are called covariates.

A statistical test used to analyze the difference between means of repeated measurements on the same subjects. This is when you measure the same variable multiple times on the same individuals.

A metric independent variable used in ANCOVA. It measures a continuous variable that influences the dependent variable.

The difference between the predicted value and the actual observed value in a statistical model.

A statistical technique used to identify underlying dimensions or factors that explain the relationships between a set of observed variables.

The factors or underlying dimensions identified through factor analysis.

A statistical model that explores the relationship between a set of observed variables and a set of unobserved factors, representing the underlying dimensions that explain the observed data.

This refers to the systematic biases or inconsistencies in the way variables are measured.

A type of scaling used in questionnaires that presents a range of responses with numbered categories, allowing respondents to choose their level of agreement or opinion.

A statistical method used in factor analysis to identify the common variance shared by a set of variables. It aims to represent the observed variables as linear combinations of underlying factors.

The proportion of variance in a variable that is explained by the common factors. It represents the shared variability between variables.

The total variance in a variable, including both common and unique variance. It represents the overall variation in the variable.

A statistical method that focuses on identifying the principal components, which are linear combinations of variables that capture the maximum amount of variance in the data. It aims to represent the data in a lower-dimensional space while preserving most of the information.

A statistical method that focuses on modeling the relationships between observed variables and underlying common factors. It assumes that each variable is influenced by a combination of common factors and unique factors.

The variance in a variable that is not shared with other variables. It represents the unique, specific variation within each variable.

The amount of variance in the observed variables that is accounted for by the common factors. It indicates how well the factors explain the relationships between variables.

The difference in the approach to explaining variance between PCA and CFA lies in their treatment of unique variance. PCA aims to maximize the total variance explained by the principal components, including the common and unique variance. CFA, on the other hand, focuses on modeling the common variance shared by the variables, excluding the unique variance. This leads to differences in the interpretation of factors and the amount of variance explained in the data.

A statistical technique used to compare the means of two or more groups when there are multiple dependent variables. It's like ANOVA but with more than one outcome variable.

A technique used to control for the effects of a metric independent variable (covariate) on the relationship between a non-metric independent variable (grouping variable) and one or more dependent variables.

A statistical method used to predict group membership based on multiple independent variables. It's like a classification technique that finds the best boundaries between groups.

A technique used to estimate the probability of an individual receiving a specific treatment, based on their characteristics. It's helpful for creating comparable groups in observational studies.

Information about what would have happened if a different treatment or intervention was applied. It's the missing or unobserved data that we can only imagine.

A type of bias that occurs when individuals self-select into a study or treatment group, creating systematic differences between the groups.

A technique used to match individuals with similar characteristics from different treatment groups, making the groups more comparable.

A statistical technique used to compare the means of two or more groups when there is only one dependent variable.

A variable that is manipulated or changed by the researcher to observe its effects on the dependent variable.