# Analytical Techniques

Analytical techniques enable researchers to examine complex relationships between variables. There are three basic types of analytical techniques:

## Regression Analysis

Regression analysis assumes that the dependent, or outcome, variable is directly affected by one or more independent variables. There are four important types of regression analyses:

Ordinary least squares (OLS) regression

• Used to determine the relationship between a dependent variable and one or more independent variables
• Used when the dependent variable is continuous. For example, if the dependent variable was family child care expenses, measured in dollars, OLS regression would be used

Logistic regression
Used when the dependent variable is dichotomous, or has only two potential outcomes. For example, logistic regression would be used to examine whether a family uses child care subsidies

Hierarchical linear modeling

• Used when data are nested. Nested data occur when several individuals belong to the same group under study. For example, in child care research, many children are cared for by the same child care provider and many child care providers work within the same state. The children are nested in the child care provider and the child care provider is nested in the state
• Allows researchers to determine the effects of characteristics for each level of nested data, child care providers and states, on the outcome variables

Duration models
Used to estimate the length of a status or process. For example, in child care policy research, duration models have been used to estimate the length of time that families receive child care subsidies.

Glossary terms related to regression analysis:

## Grouping Methods

Grouping methods are techniques for classifying observations into meaningful categories. One grouping method, discriminant analysis, identifies characteristics that distinguish between groups. For example, a researcher could use discriminant analysis to determine which characteristics identify families that seek child care subsidies and which identify families that do not.

The second grouping method, cluster analysis, is used to classify similar individuals together. For example, cluster analysis would be used to group together families who hold similar views of child care.

Glossary terms related to grouping methods:

## Interagency Meeting on Subgroup Analysis

This meeting centered on innovative methods for conducting subgroup analysis and discussions of guidelines for interpretation and reporting of subgroup analyses in prevention and intervention research.

## Multiple Equation Models

Multiple equation modeling, which is an extension of regression, is used to examine the causal pathways from independent variables to the dependent variable. There are two main types of multiple equation models:

• Path analysis
• Structural equation modeling

Path analysis
Allows researchers to examine multiple direct and indirect causes of a dependent, or outcome, variable.

• A path diagram is created that identifies the routes between the independent and dependent variables
• The paths can run directly from an independent variable to a dependent variable, or they can run indirectly from an independent variable, through an intermediary variable, to the dependent variable
• The entire model is tested to determine the relative importance of each causal pathway

Structural equation modeling
Expands path analysis by allowing for multiple indicators of unobserved (or latent) variables in the model.