Analytical techniques enable researchers to examine complex relationships between variables. There are three basic types of analytical techniques:
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 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
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Hierarchical linear modeling
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.
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Glossary terms related to regression analysis:
Coefficient of Determination
Degrees of Freedom
Ordinary Least Squares Estimation
Simple Linear Regression
Type I Error
Type II Error
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.
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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.
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Glossary terms related to grouping methods:
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 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:
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.
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Glossary terms related to multiple equation models: