ANCOVA in SPSS

ANCOVA in SPSS

Statistical Package for the Social Sciences (SPSS) is a program for analyzing data collected by researchers in the social sciences. An ANCOVA (Analysis of Covariance) is used to analyze data in which there is one or more independent variables and a dependent variable when the researcher wants to remove the influence of one or more predictor variables on the dependent variable.

Data requirements. In all GLM models, the dependent(s) is/are continuous. The independents may be categorical factors (including both numeric and string types) or quantitative covariates. Data are assumed to come from a random sample for purposes of significance testing. The variance(s) of the dependent variable(s) is/are assumed to be the same for each cell formed by categories of the factor(s) (this is the homogeneity of variances assumption).

Regression in GLM is simply a matter of entering the independent variables as covariates and, if there are sets of dummy variables (ex., Region, which would be translated into dummy variables in OLS regression, for ex., South = 1 or 0), the set variable (ex., Region) is entered as a fixed factor with no need for the researcher to create dummy variables manually. The b coefficients will be identical whether the regression model is run under ordinary regression (in SPSS, under Analyze, Regression, Linear) or under GLM (in SPSS, under Analyze, General Linear Model, Univariate). Where b coefficients are default output for regression in SPSS, in GLM the researcher must ask for "Parameter estimates" under the Options button. The R-square from the Regression procedure will equal the partial Eta squared from the GLM regression model.

The advantages of doing regression via the GLM procedure are that dummy variables are coded automatically, it is easy to add interaction terms, and it computes eta-squared (identical to R-squared when relationships are linear, but greater if nonlinear relationships are present). However, the SPSS regression procedure would still be preferred if the reseacher wishes output of standardized regression (beta) coefficients, wishes to do multicollinearity diagnostics, or wishes to do stepwise regression or to enter independent variables hierarchically, in blocks. PROC GLM in SAS has a greater range of options and outputs (SAS also has PROC ANOVA, but it handles only balanced designs/equal group sizes).

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When to Use ANCOVA in spss

One will see it used in two primary ways, one good, the other invalid by most accounts.

Good: Experimental design

Manipulation of IV

Random Selection of Subjects

Random Assignment to Groups

IV does not affect the Covariate!!1

There are variables that might relate to the DV but one wants to control for them, i.e. partition out their variance from the residual variance

Leads to more statistical power, though the raw effect size should not change

Adjusted mean difference is the same as the adjustment is equal for the groups involved, and if one follows Kline and others' suggestion, the standardized effect dwould not change.

You may also see ANCOVA used as a followup procedure in MANOVA (again assuming experimental design

Statistical Package for the Social Sciences (SPSS) is a program for analyzing data collected by researchers in the social sciences. An ANCOVA (Analysis of Covariance) is used to analyze data in which there is one or more independent variables and a dependent variable when the researcher wants to remove the influence of one or more predictor variables on the dependent variable.

Data requirements. In all GLM models, the dependent(s) is/are continuous. The independents may be categorical factors (including both numeric and string types) or quantitative covariates. Data are assumed to come from a random sample for purposes of significance testing. The variance(s) of the dependent variable(s) is/are assumed to be the same for each cell formed by categories of the factor(s) (this is the homogeneity of variances assumption).

Regression in GLM is simply a matter of entering the independent variables as covariates and, if there are sets of dummy variables (ex., Region, which would be translated into dummy variables in OLS regression, for ex., South = 1 or 0), the set variable (ex., Region) is entered as a fixed factor with no need for the researcher to create dummy variables manually. The b coefficients will be identical whether the regression model is run under ordinary regression (in SPSS, under Analyze, Regression, Linear) or under GLM (in SPSS, under Analyze, General Linear Model, Univariate). Where b coefficients are default output for regression in SPSS, in GLM the researcher must ask for "Parameter estimates" under the Options button. The R-square from the Regression procedure will equal the partial Eta squared from the GLM regression model.

The advantages of doing regression via the GLM procedure are that dummy variables are coded automatically, it is easy to add interaction terms, and it computes eta-squared (identical to R-squared when relationships are linear, but greater if nonlinear relationships are present). However, the SPSS regression procedure would still be preferred if the reseacher wishes output of standardized regression (beta) coefficients, wishes to do multicollinearity diagnostics, or wishes to do stepwise regression or to enter independent variables hierarchically, in blocks. PROC GLM in SAS has a greater range of options and outputs (SAS also has PROC ANOVA, but it handles only balanced designs/equal group sizes).

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Discriminant Analysis with SPSS

Discriminant Analysis with SPSS

Rather than working with pre-existing classifications of subjects, as the other tests in 
Chapter 9 do, a discriminant analysis attempts to create classifications. To conduct a 
discriminant analysis in SPSS, therefore, you cannot use the "General Linear Model" 
function. The following process allows you to use continuous values to predict subjects' 
group placements.
1. Choose the "Classify" option in SPSS Analyze pull-down menu. 
2. Identify your desired type of classification as "Discriminant." Choose "Discriminant" 
from the prompts given. A window entitled a window entitled Discriminant Analysis
should appear. 
FIGURE 9.9 –SPSS DISCRIMINANT ANALYSIS WINDOW
The user identifies the variables involved in a one-way discriminant analysis by selecting their names from 
those listed on the left side of the Discriminant Analysis window. SPSS performs the test using variables with 
names placed into the "Independents" and variables with names placed into the "Grouping Variables" box.The user identifies the variables involved in a one-way discriminant analysis by selecting their names from 
those listed on the left side of the Discriminant Analysis window. SPSS performs the test using variables with 
names placed into the "Independents" and variables with names placed into the "Grouping Variables" box.
3. In this window, you can define the variables involved in the analysis as follows
a. Move the name of the categorical dependent variable from the box on the left to the 
"Grouping Variable" box. You must also click on the "Define Range" button below 
this box and type the values for the lowest and highest dummy-variable values used 
to identify groups. 
b. Identify the continuous measure(s) used to predict subjects' categories by moving 
the names of the predictor(s) to the "Independents" box. 
4. Click OK.
The Discriminant Analysis' "Independents Variable" box allows you to identify more than 
one predictor of subjects' categories. Inputting more than one independent variable leads 
to a multiple discriminant analysis. The analysis presented in Chapter 9's examples, though, 
use a single independent variable.

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