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Conducting and publishing rigorous empirical research based on original data is essential for advancing and sustaining high-quality counseling practice. The purpose of this article is to provide a one-stop-shop for writing a rigorous quantitative Methods section in counseling and related fields. The importance of judiciously planning, implementing, and writing quantitative research methods cannot be understated, as methodological flaws can completely undermine the integrity of the results. This article includes an overview, considerations, guidelines, best practices, and recommendations for conducting and writing quantitative research designs. The author concludes with an exemplar Methods section to provide a sample of one way to apply the guidelines for writing or evaluating quantitative research methods that are detailed in this manuscript.

Meeting the mental health needs of students enrolled in science, technology, engineering, and mathematics (STEM) majors is particularly challenging for professional counselors who work in college settings, as STEM students are a subgroup of college students that face unique risks for developing mental health issues. The scarcity of literature on STEM student mental health coupled with their reticence to seek counseling is concerning. An important next step in this line of research is understanding why STEM students are reticent to seek counseling. Accordingly, the present investigators validated STEM students’ scores on the Revised Fit, Stigma, and Value (RFSV) Scale, a screening tool for measuring barriers to seeking counseling. Results also established the capacity of STEM students’ RFSV scores to predict peer-to-peer referrals to the counseling center and revealed demographic differences in barriers to counseling. Findings have implications for enhancing professional counselors’ efforts to support STEM students’ mental health.

Assessment literacy is an essential competency area for professional counselors who administer tests and interpret the results of participants’ scores. Using factor analysis to demonstrate internal structure validity of test scores is a key element of assessment literacy. The underuse of psychometrically sound instrumentation in professional counseling is alarming, as a careful review and critique of the internal structure of test scores is vital for ensuring the integrity of clients’ results. A professional counselor’s utilization of instrumentation without evidence of the internal structure validity of scores can have a number of negative consequences for their clients, including misdiagnoses and inappropriate treatment planning. The extant literature includes a series of articles on the major types and extensions of factor analysis, including exploratory factor analysis, confirmatory factor analysis (CFA), higher-order CFA, and multiple-group CFA. However, reading multiple psychometric articles can be overwhelming for professional counselors who are looking for comparative guidelines to evaluate the validity evidence of scores on instruments before administering them to clients. This article provides an overview for the layperson of the major types and extensions of factor analysis and can serve as reference for professional counselors who work in clinical, research, and educational settings.