Multiple Regression with SPSS – Problem 6.1: Using the Simultaneous Method to Compute Multiple Regression

To reiterate, the purpose of multiple regression is to predict an interval (or scale) dependent variable from a combination of several interval/scale and/or dichotomous independent/predictor variables. In the following assignment, we will see if math achievement can be predicted better from a combination of several of our other variables, such as the motivation scale,

Multiple Regression with SPSS – Problem 6.2: Simultaneous Regression Correcting Multicollinearity

In Problem 6.2, we will use the combined/average of the two variables, mother’s education and father’s education, and then recompute the multiple regression after omitting competence and pleasure. We combined father’s education and mother’s education because it makes conceptual sense and because these two variables are quite highly related (r = .65). We know

Multiple Regression with SPSS – Problem 6.3: Hierarchical Multiple Linear Regression

In Problem 6.3, we will use the hierarchical approach, which is used when you want to enter the variables in a series of blocks or groups. This enables the researcher to see if each new group of variables adds anything to the prediction produced by the previous blocks of variables. This approach is an

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Multiple Regression with SPSS – Problem 6.4: Forward Multiple Linear Regression

In Problem 6.4, we will use the forward approach, which is when SPSS adds variables one at a time by assessing which variable has the smallest probability of F (i.e., p value) continuing until all variables are added that have a p value equal to or less than .05. This enables the researcher to

Multiple Regression with SPSS – Problem 6.5: Backward Elimination Multiple Linear Regression

In Problem 6.5, we will use the backward approach, which is when all the variables are added into the model, then are eliminated one by one, with the variable that has the largest probability of F (i.e., p value) removed until all variables have a p value equal to or less than .10. The

Mediation, Moderation, and Canonical Correlation

1. Mediation Mediation occurs when the observed relationship between two variables is due, at least in part, to a third variable. Statistically, mediation occurs when a variable (i.e., the mediating variable) reduces the magnitude of the relationship between two other variables. Mediation can be pictured as: Conditions of Mediation There are a few important

Running the Mediation analysis in SPSS using PROCESS

The purpose of testing statistical mediation is to understand the effects of a presumed mediating variable on the relationship between a scale or dichotomous predictor variable and a scale outcome variable. If mediation is occurring, the mediating variable will reduce the size of the relationship between the predictor and the outcome variable. Perfect mediation

Running the Moderation analysis in SPSS using PROCESS

In the previous post, we will conduct a moderation analysis. Recall that the purpose of moderation is to investigate how the interaction between two or more independent variables affects the relationship of these variables to the outcome variable. When conducting a moderation analysis with interval or ratio (scale) predictors, centering the predictors makes effects

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Problem 7.3: Moderation: Graphing the Slopes with SPSS

In Problem 7.3 we will graph the slopes using the data from the output from Problem 7.2. What do the slopes look like when previous experience (months) is moderating the relationship between beginning salary and current salary? Follow these steps: Open a new and blank SPSS data file by going to File → Data

Problem 7.4: Canonical Correlation with SPSS

In Problem 7.4 we will assess the relationship between multiple scale independent variables and multiple scale dependent variables. This analysis is an extension of multiple regression. Recall with multiple regression there are multiple scale (or dichotomous) independent variables and one scale dependent variable. Yet, researchers are at times interested in more than one dependent

Problem 8.1: Logistic Regression with SPSS

Is there a combination of gender, parents’ education, mosaic, and visualization test that predicts whether students will take algebra 2? Let’s try a logistic regression to predict a dichotomous (two category) dependent variable when the independent variables (called covariates in SPSS) are either dichotomous or normal/scale. First, we should check for multicollinearity. Because tolerance

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Problem 8.2: Hierarchical Logistic Regression with SPSS

We will rerun Problem 8.1, but this time we will enter the background variables gender and parents’ education first and then, on the second step or block, enter mosaic and visualization test. If we control for gender and parents’ education, will mosaic and/or visualization test add to the prediction of whether students will take

Problem 8.3: Discriminant Analysis with SPSS

Discriminant analysis is appropriate when you want to predict which group participants will be in (in this example, who took algebra 2). The procedure produces a discriminant function (or for more than two groups, a set of discriminant functions) based on linear combinations of the predictor variables that provide the best overall discrimination among

Problem 9.1: Factorial (Two-Way) ANOVA with SPSS

We would use a t test or one-way ANOVA to examine differences between two or more groups (comprising the levels of one independent variable or factor) on a continuous dependent variable. These designs, in which there is only one independent variable and it is a discrete or categorical variable, are called single­factor designs. In

Problem 9.2: Post Hoc Analyses of a Significant Interaction with SPSS

We have described, in the interpretation of Output 9.1, how to visually inspect and interpret the Profile Plots when there is a statistically significant interaction. In Problems 9.2b and 9.2c we will illustrate two ways to test the simple effects statistically. In the interpretation of Output 9.1, we indicated that you should examine the

Problem 9.3: Analyses of Covariance (ANCOVA) with SPSS

ANCOVA is an extension of ANOVA that typically provides a way of statistically controlling for the effects of continuous or scale variables that you are concerned about but that are not the focal point or independent variable(s) in the study. These continuous variables are called covariates (or sometimes, control variables). Covariates usually are variables

Repeated-Measures and Mixed ANOVAs with SPSS: The Product Data Set

Open the SPSS for Windows program. Open the Product data set. Do not retrieve the hsbdataNew for this assignment. In this study, each of the 12 participants (or subjects) has evaluated four products that vary in cost (e.g., four brands of DVD players) on 1-7 Likert scales. Product A is the most expensive (i.e.,

Problem 10.1: Repeated-Measures ANOVA with SPSS

The GLM repeated-measures procedure provides a variety of analysis of variance procedures to use when the same measurement is made several times on each subject or the same measurement is made on several related subjects. The single­factor repeated-measures ANOVA, which we will use for Problem 10.1, is appropriate when you have one independent variable

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Problem 10.2: The Friedman Nonparametric Test for Several Related Samples with SPSS

What could you do if the product ratings are ordinal data or the repeated- measures ANOVA assumptions are markedly violated? One answer is to use a nonparametric statistic. As you can tell from Table 5.1, an appropriate nonparametric test for when you have more than two levels of one repeated- measures or related samples

Problem 10.3: Mixed ANOVA with SPSS

You can test null hypotheses about the effects of both between-groups factors and within-subjects factors with a Mixed ANOVA using the General Linear Model procedure. You can investigate interactions between factors as well as the effects of individual factors on a dependent variable. Repeat Problem 10.1 except add gender to see if there are