Can Outcomes of a Chat-Based Suicide Prevention Helpline Be Improved by Training Counselors in Motivational Interviewing? A Non-randomized Controlled Trial

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.

Evaluating the intercoder reliability (ICR) of a coding frame is frequently recommended as good practice in qualitative analysis. ICR is a somewhat controversial topic in the qualitative research community, with some arguing that it is an inappropriate or unnecessary step within the goals of qualitative analysis. Yet ICR assessment can yield numerous benefits for qualitative studies, which include improving the systematicity, communicability, and transparency of the coding process; promoting reflexivity and dialogue within research teams; and helping convince diverse audiences of the trustworthiness of the analysis. Few guidelines exist to help researchers negotiate the assessment of ICR in qualitative analysis. The current article explains what ICR is, reviews common arguments for and against its incorporation in qualitative analysis and offers guidance on the practical elements of performing an ICR assessment.

Many research designs require the assessment of inter-rater reliability (IRR) to demonstrate consistency among observational ratings provided by multiple coders. However, many studies use incorrect statistical procedures, fail to fully report the information necessary to interpret their results, or do not address how IRR affects the power of their subsequent analyses for hypothesis testing. This paper provides an overview of methodological issues related to the assessment of IRR with a focus on study design, selection of appropriate statistics, and the computation, interpretation, and reporting of some commonly-used IRR statistics. Computational examples include SPSS and R syntax for computing Cohen's kappa and intra-class correlations to assess IRR.