Selection Of A Statistical Analysis Approach For Data

Selection Of A Statistical Analysis Approachthough Data

Discussion 1: 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. · Post 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. References (use 3 or more) 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”) 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) Trochim, W. M. K. (2006). Internal validity. Retrieved from Document: Week 4: A Short Course in Statistics Handout (PDF) Document: Week 4: Handout: Chi-Square findings (PDF)

Paper For Above instruction

The process of selecting an appropriate statistical analysis approach is a foundational component of rigorous social work research. It begins with a clear understanding of the research question and the nature of the data collected. The researcher must identify which statistical methods are capable of providing valid, reliable insights about the hypothesized relationships or effects while considering the characteristics of the sample, the type of data (nominal, ordinal, interval, ratio), and the study design. For example, parametric tests such as t-tests and ANOVA require assumptions like normality and homogeneity of variance, which are not applicable to non-parametric alternatives like the Mann-Whitney U or Kruskal-Wallis tests (Trochim, 2006). Moreover, the researcher must understand the purpose and limitations of specific tests, such as Chi-square tests, which are suitable for examining relationships between categorical variables, but do not imply causality (Trochim, 2006).

In social work research, the validity of findings is critically dependent on the research design used. As Dudley (2014) emphasizes, the design determines whether relationships observed can be confidently attributed to the intervention or variables under investigation. For example, a cross-sectional study may reveal correlations but cannot establish causal relationships, whereas longitudinal randomized controlled trials are better suited for causal inferences. Internal validity, which refers to the extent to which causal conclusions are warranted, can be threatened by confounding variables, bias, and measurement issues (Trochim, 2006).

The case study on social work research involving Chi-square tests offers an illustrative example. The interpretation stating that “the vocational rehabilitation intervention program may be effective at promoting full-time employment” suggests a relationship between participation in the program and employment status. However, this conclusion must be tempered by the study’s internal validity threats, such as confounding factors like socioeconomic status, prior employment history, motivation levels, or access to resources. These variables may influence employment independently of the intervention, thus confounding the results (Dudley, 2014).

Several factors limit internal validity in this case. First, selection bias could have influenced group comparability if participants self-selected into the program based on motivation or other characteristics. Second, external factors such as economic conditions or training opportunities could also affect employment outcomes, independent of the intervention. Third, measurement bias might occur if the evaluation of employment status was inconsistent or subjective. These threats undermine the ability to confidently attribute improvements in employment to the vocational program. Without randomization or control for confounding variables, the observed association remains correlational rather than causal. Furthermore, the cross-sectional nature of the assessment limits causal inferences, reinforcing the need for cautious interpretation (Dudley, 2014; Trochim, 2006).

Ensuring internal validity requires meticulous design considerations, such as employing randomized controlled trials, matching participants, or statistically controlling for confounders. Recognizing and addressing these threats enhances the strength of the evidence, making it more compelling to support policy or practice changes. As social workers, understanding these methodological nuances is essential in critiquing research findings and applying evidence-based interventions confidently.

References

• Dudley, J. R. (2014). Social work evaluation: Enhancing what we do. Lyceum Books.
• Trochim, W. M. K. (2006). Internal validity. In W. M. K. Trochim, Research Methods. Retrieved from https://socialresearchmethods.net/kb/internal-validity/
• Stocks, J. T. (2010). Statistics for social workers. In B. Thyer (Ed.), The handbook of social work research methods (2nd ed., pp. 75–118). Sage Publishers.
• Krueger, R. A., & Casey, M. A. (2014). Focus groups: A practical guide for applied research. Sage publications.
• Hammersley, M., & Traianou, A. (2012). Ethics in qualitative research. Sage.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-experimental Designs. Houghton Mifflin.