Socw6311-Wk4-Discussion: Selection Of A Statistical Analysis Approach

though data analysis occurs after the study has completed a data collection stage, the researcher needs to have in mind what type of analysis will allow the researcher to obtain an answer to a research question. The researcher must understand the purpose of each method of analysis, the characteristics that must be present in the study for the design to be appropriate and any weaknesses of the design that might limit the usefulness of the study results. Only then can the researcher select the appropriate design. Choosing the appropriate design enables the researcher to claim the data that is potential evidence that provides information about the relationship being studied. Notice that it is not the statistical test which tells us that research is valid, rather, it is the research design. Social workers must be aware of and adjust any limitations of their chosen design that may impact the validity of the study.

To prepare for this Discussion, review the handout, A Short Course in Statistics and pages 210–220 in your course text Social Work Evaluation: Enhancing What We Do. If necessary, locate and review online resources concerning internal validity and threats to internal validity. Then, review the “Social Work Research: Chi Square” case study located in this week’s resources. Consider the confounding variables, that is, factors that might explain the difference between those in the program and those waiting to enter the program.

Post2 to 3 pages) an interpretation of the case study’s conclusion that “the vocational rehabilitation intervention program may be effective at promoting full-time employment.” Describe the factors limiting the internal validity of this study, and explain why those factors limit the ability to draw conclusions regarding cause and effect relationships.


Required Readings

Dudley, J. R. (2014). Social work evaluation: Enhancing what we do. (2nd ed.) Chicago, IL: Lyceum Books.
Chapter 9, “Is the Intervention Effective?” (pp. 226–236: Read from “Determining a Causal Relationship” to “Outcome Evaluations for Practice”)

Plummer, S.-B., Makris, S., & Brocksen S. (Eds.). (2014b). Social work case studies: Concentration year. Baltimore, MD: Laureate International Universities Publishing. [Vital Source e-reader].
Read the following section:
“Social Work Research: Chi Square” (pp. 63–65)

Document: Stocks, J. T. (2010). Statistics for social workers. In B. Thyer (Ed.), The handbook of social work research methods (2nd ed., pp. 75–118). Thousand Oaks, CA: Sage. (PDF)
Copyright 2010 by Sage Publications, Inc.
Reprinted by permission of Sage Publications, Inc. via the Copyright Clearance Center.

Trochim, W. M. K. (2006). Internal validity. Retrieved from
Be sure to click on all the links in the narrative.

Molly, an administrator with a regional organization that advocates for alternatives to long-term prison sentences for nonviolent offenders, asked a team of researchers to conduct an outcome evaluation of a new vocational rehabilitation program for recently paroled prison inmates. The primary goal of the program is to promote full-time employment among its participants

. To evaluate the program, the evaluators decided to use a quasi-experimental research design. The program enrolled 30 individuals to participate in the new program. Additionally, there was a waiting list of 30 other participants who planned to enroll after the first group completed the program. After the first group of 30 participants completed the vocational program (the “intervention” group), the researchers compared those participants’ levels of employment with the 30 on the waiting list (the “comparison” group).

In order to collect data on employment levels, the probation officers for each of the 60 people in the sample (those in both the intervention and comparison groups) completed a short survey on the status of each client in the sample. The survey contained demographic questions that included an item that inquired about the employment level of the client. This was measured through variables identified as none, part-time, or full-time. A hard copy of the survey was mailed to each probation officer and a stamped, self-addressed envelope was provided for return of the survey to the researchers.

After the surveys were returned, the researchers entered the data into an SPSS program for statistical analysis. Because both the independent variable (participation in the vocational rehabilitation program) and dependent variable (employment outcome) used nominal/categorical measurement, the bivariate statistic selected to compare the outcome of the two groups was the Pearson chi-square.

After all of the information was entered into the SPSS program, the following output charts were generated:


Cases Valid Missing Total N Percent N Percent N Percent Program Participation *Employment 59 98.3% 1 1.7% 60 100.0%


*EMPLOYMENT CROSS TABULATION   Employment Total None Part-Time Full-Time Program Participation Intervention Group Count % within Program Participation 5 16.7%  7 23.3% 18 60.0%  30 100.0%   Comparison Group Count % within Program Participation 16 55.2%  7 24.1%  6 20.7%  29 100.0% Total   Count % within Program Participation 21 35.6% 14 23.7% 24 40.7%  59 100.0%


Value df Asymp. Sig. (2-sided) Pearson Chi-Square 11.748a 2 .003 Likelihood Ratio 12.321 2 .002 Linear-by-Linear Association 11.548 1 .001 N of Valid Cases     59 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 6.88.

The first table, titled Case Processing Summary, provided the sample size (N = 59). Information for one of the 60 participants was not available, while the information was collected for all of the other 59 participants.

The second table, Program Participation Employment Cross Tabulation, provided the frequency table, which showed that among participants in the intervention group, 18 or 60% were found to be employed full time, while 7 or 23% were found to be employed part time, and 5 or 17% were unemployed. The corresponding numbers for the comparison group (parolees who had not yet enrolled in the program but were on the waiting list for admission) showed that only 6 or 21% were employed full-time, while 7 or 24% were employed part time, and 16 or 55% were unemployed.

The third table, which provided the outcome of the Pearson chi-square test, found that the difference between the intervention and comparison groups were highly significant, with a p value of .003, which is significantly beyond the usual alpha-level of .05 that most researchers use to establish significance.

These results indicate that the vocational rehabilitation intervention program may be effective at promoting full-time employment among recently paroled inmates. However, there are multiple limitations to this study, including that 1) no random assignment was used, and 2) it is possible that differences between the groups were due to preexisting differences among the participants (such as selection bias).

Potential future studies could include a matched comparison group or, if possible, a control group. In addition, future studies should assess not only whether or not a recently paroled individual obtains employment but also the degree to which he or she is able to maintain employment, earn a living wage, and satisfy other conditions of probation.

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