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

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

Visit the following websites for more information about OLS and logistic regression:

Hierarchical linear modeling

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.

Visit the following website for more information about duration models:

Glossary terms related to regression analysis:

Adjusted R-Squared
Alpha Level
Coefficient of Determination
Degrees of Freedom
Dependent Variable
Effect Size
Error Term
Heteroskedastic
Independent Variable
Indirect Effect
Interaction Effect
Intercept
Least Squares
Linear Regression
Logistic Regression
Main Effect
Multicollinearity
Ordinary Least Squares Estimation
Outlier
Parameter
Predictor Variable
Regression Analysis
Regression Coefficient
Regression Equation
R-Squared
Significance Level
Simple Linear Regression
Slope
Standard Error
Standardized Variables
Statistical Significance
T-test
Type I Error
Type II Error
Z-test

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.

Visit the following website for more information about discriminant analysis:

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.

Visit the following websites for more information about cluster analysis:

Glossary terms related to grouping methods:

Cluster Analysis
Discriminant Analysis
Exploratory Study

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

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

Visit the following websites for more information about multiple equation models:

Glossary terms related to multiple equation models:

Causal Analysis
Path Analysis
Regression Analysis