Respond to at least two colleagues by explaining how that colleague might rule out one of the confounding variables that they identified. APA CITATION
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Outline of the Case Study
Objective: To evaluate the effectiveness of a vocational rehabilitation intervention program aimed at promoting full-time employment for individuals facing barriers. (Dudley,2020)
Program Components:
Skills training
Job placement assistance
Ongoing support
Methodology:
Sample selection with demographic data
Pre- and post-intervention employment outcome analysis
Statistical comparisons between intervention and control groups
Findings:
Significant increase in full-time employment rates post-intervention. (Flannelly et.,2018)
Statistical analyses (p-values, confidence intervals) indicate positive outcomes, though further research is warranted.
Statistical Information on Program Effectiveness
To assess the effectiveness of the program, the study likely utilized:
Employment Rate Comparison: Pre- and post-intervention employment rates between intervention and control groups.
P-values: Indicating the statistical significance of observed changes.
Confidence Intervals: To understand the reliability of the estimates regarding employment outcomes.
Limitations to Internal Validity
History: External events occurring simultaneously with the intervention could affect employment outcomes.
Maturation: Participants might find employment due to natural progression rather than the program.
Testing: Repeated measures could influence participant responses.
Instrumentation: Changes in data collection methods may affect outcome measurements.
Statistical Regression: Extreme scores may skew results if they regress toward the mean.
Selection Bias: Non-random assignment may lead to systematic differences between participants and non-participants.
Attrition: Loss of participants can result in biased samples.
Key Factors Limiting Cause-and-Effect Conclusions
Selection Bias and Attrition are the primary factors limiting the ability to draw clear cause-and-effect conclusions:
Selection Bias: If participants are not randomly selected, their characteristics (motivation, prior experience) may influence outcomes, complicating the attribution of employment success solely to the intervention.
Attrition: If participants who drop out differ significantly from those who remain, the final sample may not accurately represent the initial population. This can distort the assessment of the program’s effectiveness and lead to incorrect conclusions about its impact.
These factors underscore the importance of cautious interpretation of results and highlight the need for robust research designs to enhance internal validity.
References
Dudley, J. R. (2020). Social work evaluation: Enhancing what we do (3rd ed.). Oxford University Press.
Flannelly, K. J., Flannelly, L. T., & Jankowski, K. R. B. (2018). Threats to the internal validity of experimental and quasi-experimental research in healthcare. Journal of Health Care Chaplaincy, 24(3), 107–
130. https://doi.org/10.1080/08854726.2017.1421019
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Vocational Rehabilitation Program: Chi-Square Case Study
Case Study Overview
The vocational rehabilitation program was created to assist individuals in securing full-time employment by offering job training, job placement support, and counseling. To evaluate the program’s effectiveness, researchers compared two groups: (1) one that participated in the program and (2) a control group that was on the waiting list. The study gathered and tracked employment outcomes, specifically whether participants attained full-time jobs after completing the program. A Chi-Square test was used to determine if there was a significant relationship amongst program participation and full-time employment (Dudley, 2020; Rubin & Babbie, 2017).
Program Effectiveness
The results of the Chi-Square test helped provide insight into whether the program was able to make a meaningful difference in participants’ job outcomes. An important indicator is the p-value that was generated by the assessment. If the p-value is less than or equal to 0.05, it suggests that the program significantly impacted participants’ ability to find full-time employment. For instance, a p-value of 0.001 would indicate that the program likely played a critical role in helping participants secure full-time jobs (Flannelly, Flannelly, & Jankowski, 2018). On the other hand, a p-value above 0.05 would propose that the program did not make a significant difference in employment outcomes.
Factors Limiting Internal Validity
There are several influences that can limit the study’s internal validity, making it challenging to draw certain conclusions about the program’s effectiveness. One key concern is selection bias, where the individuals in the program may have been more motivated or had stronger job skills than those that were found in the control group. These differences found could explain why program participants were more successful in finding jobs, independent of the program’s effect (Flannelly, Flannelly, & Jankowski, 2018).
Other confusing variables, might be things such as the participants’ education levels or previous work experience, that might also have influenced the results. If these factors were not evenly dispersed between the program and control groups, they could have contributed to the differences in job outcomes (Dudley, 2020; Rubin & Babbie, 2017).
Additional Threats to Validity
Another important threat to the study’s internal validity is attrition. If a significant number of participants were to have dropped out of the study, specifically from just one of the groups, it could skew the results and make the findings less reliable (Rubin & Babbie, 2017). Additionally, external events like changes in the local economy or job market during the study period could have impacted employment outcomes for both of the groups. An example would be, if a major employer shut down, it could affect everyone’s chances of finding a job, regardless of their participation in the program (Flannelly, Flannelly, & Jankowski, 2018).
While the Chi-Square test is able to show a relationship between the vocational rehabilitation program and participants’ success in finding full-time jobs, there could also be other influences that limit the capacity to draw strong cause-and-effect conclusions. Selection bias, external factors, and attrition can all influence the results. In order to improve future studies, researchers could consider using random assignment or controlling for additional variables to better understand the program’s true influence (Dudley, 2020; Rubin & Babbie, 2017).