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
Visit the following Web sites for more information about OLS and logistic regression:
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.
Visit the following Web sites for more information about duration models:
- Box-Steffensmeier, J. and Zorn, C.J.W (2002), "Duration models for repeated events," The Journal of Politics 64(4).
- Duration Models (MS Powerpoint)
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 Web sites 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 Web sites for more information about cluster analysis:
Glossary terms related to grouping methods:
Cluster Analysis
Discriminant Analysis
Exploratory Study
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.
Visit the following Web sites for more information about multiple equation models:
- Electronic Textbook: StatSoft
- Structural Equation Modeling (David A. Kenny)
Glossary terms related to multiple equation models:
|
||||
 |
 |
 |
 |
 |