Multiple Regression Analysis Data Set

¶ … Multiple Regression Analysis

Data Set Entered into SPSS

The variables entered into SPSS were as follows:

Race

Age

Gender

Recruitment Location

Locus of Control

Appraisal

Mentor

Family Mentor

School Experience

Religious Faith

Childhood SES

Childhood Maltreatment

Resilience Composite Score

Each of these variables represents the item totals for individual questionnaires in the survey, or responses to individual questions. For example, Race was answered by selecting one of 3 options. Age was answered by entering one's age. Gender was answered by selecting one's gender. For measures such as Locus of Control and Appraisal, response to multiple items were averaged to provide a scale score.

These variables can then be used to conduct a multiple regression.

The dependent variable in this case is "Resilience" because one is trying to determine what factors account for the greatest amount of variance with respect to resilience.

A hierarchical multiple regression is conducted by entering different variables at different steps. On the first step, if replicating the study provided, one would enter the demographic variables (Race, age, gender, recruitment location). On the second step, one would enter locus of control, mentor, family mentor, school experience, religious faith and appraisal. In this study, these variables are considered protective factors. In the third step, childhood SES would be entered. On the fourth step, childhood maltreatment would be entered. The results of the analysis would determine to what extent each of these additional steps adds to the ability to explain the variance in resilience scores. While all of these variables may ultimately be correlated with resilience, either positively or negatively, not all of these variables will continue to "add" something to our ability to predict resilience after the contribution of other variables have been allowed for first.