Irfan Ahmed, PhD

Regression is a statistical tool that allows you to predict the value of one continuous variable from one or more other variables. When you perform a regression analysis, you create a regression equation that predicts the values of your DV using the values of your IVs. Each IV is associated with specific coefficients in the equation that summarizes the relationship between that IV and the DV. Once we estimate a set of coefficients in a regression equation, we can use hypothesis tests and confidence intervals to make inferences about the corresponding parameters in the population. You can also use the regression equation to predict the value of the DV given a specified set of values for your IVs. Simple Linear Regression Simple linear regression is used to predict the value of a single continuous DV (which we will call Y) from a single continuous IV (which we will call X). Regression assumes that the relationship between IV and the DV can be represented by the equation. Y i = β 0 ...

Pearson correlation A Pearson correlation measures the strength of the linear relationship between two continuous variables. A linear relationship is one that can be captured by drawing a straight line on a scatterplot between the two variables of interest. The value of the correlation provides information both about the nature and the strength of the relationship. • Correlations range between -1.0 and 1.0. • The sign of the correlation describes the direction of the relationship. A positive sign indicates that as one variable gets larger the other also tends to get larger, while a negative sign indicates that as one variable gets larger the other tends to get smaller. • The magnitude of the correlation describes the strength of the relationship. The further that a correlation is from zero, the stronger the relationship is between the two variables. A zero correlation would indicate that the two variables aren’t related to each other at all. Correlations only measure the strength...

Performing a MANOVA using interactive mode You can use the interactive mode of SPSS to perform a MANOVA if all of your independent variables are manipulated between subjects. To peform a MANOVA using the interactive mode in SPSS • Choose Analyze thenGoto General Linear Model thenGoto Multivariate . • Move the DVs you want to examine to the Dependent Variables box. • Move any categorical IVs to the Fixed Factor(s) box. • Move any continuous IVs to the Covariate(s) box. • By default, SPSS will build a model including all interactions between the categorical independent variables, but no interactions with the continuous independent variables. To analyze a different model you must take the following steps. o Click the Model button. o Click the radio button next to Custom . o Add all of main effects to your model by clicking the IVs in the box labeled Factors and Covariates , setting the pull-down menu to Main effects , and clicking the arrow button. o Add each of the interaction terms t...

MANOVA (multivariate analysis of variance) is a statistical procedure that allows you to determine if a set of categorical predictor variables can explain the variability in a set of continuous response variables. It is also possible to include continuous predictor variables either as covariates or as true independent variables in the design (so that you can test for the effect of interactions). MANOVA is related to within-subject ANOVA in that both of these analyses examine multiple measurements from each case (i.e., participant) in your data set. Whether you should perform a MANOVA or a within-subject ANOVA depends on the relationship between the measurements. If the different measurements reflect observations at different levels of a theoretical factor, then you should perform a within-subject ANOVA. For example, you might look at a person’s heart rate over successive days, such that the different measurements represent different levels of a "time" factor. If the me...

One-way within-subjects ANOVA A one-way within-subjects ANOVA allows you to determine if there is a relationship between a categorical IV and a continuous DV, where each subject is measured at every level of the IV. Within-subject ANOVA should be used whenever want to compare 3 or more groups where the same subjects are in all of the groups. To perform a within-subject ANOVA in SPSS you must have your data set organized so that the subject is the unit of analysis and you have different variables containing the value of the DV at each level of your within-subjects factor. To perform a within-subject ANOVA in SPSS: • Choose Analyze thenGoto General linear model thenGoto Repeated measures. • Type the name of the factor in the Within-Subjects Factor Name box. • Type the number of groups the factor represents in the Number of Levels box. • Click the Add button. • Click the Define button. • Move the variables representing the different levels of the within-subjects factor to the Wit...