224 The Professional Counselor | Volume 12, Issue 3 Operationalizing Variables and Corresponding Statistical Analyses Clearly outlining each variable is an important first step in selecting the most appropriate statistical analysis for answering each research question (Creswell & Creswell, 2018). Researchers should specify the independent variable(s) and corresponding levels as well as the dependent variable(s); for example, “The first independent variable, time, was composed of the three following levels: pre, middle, and post. The dependent variables were participants’ scores on the burnout and compassion satisfaction subscales of the ProQOL 5.” After articulating the variables, counseling researchers are tasked with identifying each variable’s scale of measurement (Creswell & Creswell, 2018; Field, 2018; Flinn & Kalkbrenner, 2021). Researchers can select the most appropriate statistical test(s) for answering their research question(s) based on the scale of measurement for each variable and referring to Table 8.3 on page 159 in Creswell and Creswell (2018), Figure 1 in Flinn and Kalkbrenner (2021), or the chart on page 1072 in Field (2018). Assumption Checking Statistical analyses used in quantitative research are derived based on a set of underlying assumptions (Field, 2018; Giordano et al., 2021). Accordingly, it is essential that quantitative researchers outline their protocol for testing their sample data for the appropriate statistical assumptions. Assumptions of common statistical tests in counseling research include normality, absence of outliers (multivariate and/or univariate), homogeneity of covariance, homogeneity of regression slopes, homoscedasticity, independence, linearity, and absence of multicollinearity (Flinn & Kalkbrenner, 2021; Giordano et al., 2021). Readers can refer to Figure 2 in Flinn and Kalkbrenner (2021) for an overview of statistical assumptions for the major statistical analyses in counseling research. Exemplar Quantitative Methods Section The following section includes an exemplar quantitative methods section based on a hypothetical example and a practice data set. Producers and consumers of quantitative research can refer to the following section as an example for writing their own Methods section or for evaluating the rigor of an existing Methods section. As stated previously, a well-written literature review and research question(s) are essential for grounding the study and Methods section (Flinn & Kalkbrenner, 2021). The final piece of a literature review section is typically the research question(s). Accordingly, the following research question guided the following exemplar Methods section: To what extent are there differences in anxiety severity between college students who participate in deep breathing exercises with progressive muscle relaxation, group exercise program, or both group exercise and deep breathing with progressive muscle relaxation? Methods A quantitative group comparison research design was employed based on a post-positivist philosophy of science (Creswell & Creswell, 2018). Specifically, I implemented a quasi-experimental, control group pretest/posttest design to answer the research question (Leedy & Ormrod, 2019). Consistent with a postpositivist philosophy of science, I reflected on pursuing a probabilistic objective answer that is situated within the context of imperfect and fallible evidence. The rationale for the present study was grounded in Dr. David Servan-Schreiber’s (2009) theory of lifestyle practices for integrated mental and physical health. According to Servan-Schreiber, simultaneously focusing on improving one’s mental and physical health is more effective than focusing on either physical health or mental wellness in isolation. Consistent with Servan-Schreiber’s theory, the aim of the present study was to compare the utility of three different Exemplar
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