BASIE is a framework for interpreting impact estimates from evaluations. It is an alternative to null hypothesis significance testing. This guide walks researchers through the key steps of applying BASIE, including selecting prior evidence, reporting impact estimates, interpreting impact estimates, and conducting sensitivity analyses. The guide…

Engaging students in active learning can enhance their understanding and appreciation of a subject such as statistics. Classroom activities and projects help to engage students and further promote the learning process. In this paper, an activity investigating the influence of population size and wealth on the medal counts from the 2012 London…

The use of confidence intervals (CIs) as an addition or as an alternative to null hypothesis significance testing (NHST) has been promoted as a means to make researchers more aware of the uncertainty that is inherent in statistical inference. Little is known, however, about whether presenting results via CIs affects how readers judge the…

In this paper the use of the Rasch model is explored as a transparent, systematic and theoretically underpinned response to quality issues that are widely recognised as problematic in the refinement of Likert scale questionnaires. Key issues are the choice of length of scale, the pursuit of a favourable estimate of Cronbach's alpha at the possible…

Confidence intervals (CIs) and effect sizes are essential to encourage meta-analytic thinking and to accumulate research findings. CIs provide a range of plausible values for population parameters with a degree of confidence that the parameter is in that particular interval. CIs also give information about how precise the estimates are. Comparison…

Experimental data are analysed statistically to allow researchers to draw conclusions from a limited set of measurements. The hard fact is that researchers can never be certain that measurements from a sample will exactly reflect the properties of the entire group of possible candidates available to be studied (although using a sample is often the…

Null hypothesis significance testing has dominated quantitative research in education and psychology. However, the statistical significance of a test as indicated by a p-value does not speak to the practical significance of the study. Thus, reporting effect size to supplement p-value is highly recommended by scholars, journal editors, and academic…

Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…

Evaluates arguments concerning the generality and statistical analysis of single-subject data. Considers the role of philosophy of science in evaluating research methodology. Single-subject data have special relevance for psychologists and educators who focus on the individual but statistical techniques for analyzing data have numerous…

Discusses three topics related to interpretation of discriminant analyses (DA's): (1) partial F ratios and partial Wilks's lambdas for predictor variables in standard, step-down, and stepwise DA's; (2) relation of goals of classification to definition/evaluation of classification rules; and (3) significance tests for total hit rates in internal…

This handbook is intended to help government, university, private sector, and other users become better acquainted with the concepts and data underlying the Department of Agriculture's statistical series. The introduction reviews what various pieces of federal legislation say about the computation of agricultural production costs by the U.S.…