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.The preface to the first edition of my power handbook (Cohen, 1969) begins:During my first dozen years of teaching and consulting on applied statistics with behavioral scientists, 1 became increasingly impressed with the importance of statistical power analysis, an importance which was increased an order of magnitude by its neglect in our textbooks and curricula. The case for its importance is easily made: What behavioral scientist would view with equanimity the question of the probability that his investigation would lead to statistically significant results, i.e., its power? (p. vii)

The digitalisation of business processes has led to new opportunities for companies. One such opportunity is to investigate process executions by using process mining, but it may require a complex preparation step of building an event log. This task is often perceived as a technical task, although business considerations should be involved due to possible business implications. We argue this is a complex task that requires proper guidance to be performed adequately. In this paper, we examine whether and how a guiding procedure supports the performance of this task. In an experimental study, we follow and compare the lines of thinking of novices that follow procedural guidance with an undirected control group. Our findings provide insights into the parts played by business and technical considerations in this task and suggest that procedural guidance positively impacts the process of building an event log and its outcome.

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. 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 8 standard statistical tests: (1) the difference between independent means, (2) the significance of a product-moment correlation, (3) the difference between independent rs, (4) the sign test, (5) the difference between independent proportions, (6) chi-square tests for goodness of fit and contingency tables, (7) 1-way analysis of variance (ANOVA), and (8) the significance of a multiple or multiple partial correlation.

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A previously described coefficient of agreement for nominal scales, kappa, treats all disagreements equally. A generalization to weighted kappa (K W ) is presented. The KW provides for the incorporation of ratio-scaled degrees of disagreement (or agreement) to each of the cells of the k X k table of joint nominal scale assignments such that disagreements of varying gravity (or agreements of varying degree) are weighted accordingly. Although providing for partial credit, K W is fully chance corrected. Its sampling characteristics and procedures for hypothesis testing and setting confidence limits are given. Under certain conditions, K W equals productmoment r. Although developed originally as a measure of reliability, the use of unequal weights for symmetrical cells makes K W suitable as a measure of validity.