This chapter describes evidence-based psychotherapeutic approaches for adults with depressive disorders. We focus on the most recognized, contemporary evidence-based psychotherapies for depression including: traditional and newer forms of cognitive behavioral therapy (defined broadly), interpersonal psychotherapy, brief dynamic psychotherapy and supportive psychotherapy. We summarize the findings on the extent to which these psychotherapies promote response, remission, relapse prevention, and recovery in adults and that permit these psychotherapies to be termed “evidence based.” Effect sizes are reported, allowing the reader to grasp quickly the efficacy of each psychotherapy used as a solitary treatment. We highlight current trends and future possibilities that may expand understanding of depressive disorders and increase access to effective psychotherapy for adults with depressive disorders.

Chapter 13 provides a brief treatment of effectiveness-cost and benefit-cost analysis as it applies to school inputs and outputs. Cost-effectiveness analysis compares how much each intervention costs in order to produce the estimated increase in output, where increases in output from each of these different interventions is measured by the same output metric. The goal is to identify the inputs that produce the largest increases in output per unit cost. Cost-benefit analysis comes into play when the gains in output are measured on different outputs – for example, in one intervention, it might be measured as mathematics test score gain, and in another, it might be measured as increased growth mindset. Because the outputs are different, they need to be translated into a “common denominator.” This is usually the economic value of each of those educational outcomes as measured by increases in adult earnings.

This chapter interrogates corpus data to analyze the three alternations subject to study in this book one by one using a battery of state-of-the-art analysis techniques in addition to customary descriptive statistics, Conditional Random Forest (CRF) modeling and mixed-effects logistic regression analysis. The goal of the chapter is to uncover qualitative generalizations: for example, we see that while effect directions of constraints on variation are generally stable across varieties of English, effects strengths can be significantly different.

This chapter provides an overview of the processes that are commonly used for analyzing data. Our intention is to explain what these processes achieve and why they are done. Analyzing data goes through four stages. For each stage, we explain the most important concept and then explain the practical steps that are involved. This begins with the data themselves as variables. Next, we move on to describing the data, their variance and covariance, with linear models. Next, we cover interpreting effects and focus on effect sizes. We end with a discussion of inferences about the population and how the presence of uncertainty has to be taken into account in reaching conclusions.

Effect sizes, like relative risks and Cohen’s d, are shown to be important, rather than mere statistical significance.

Data analysis and interpretation allow you to test your predictions and interpret your results. This is an exciting time and can be daunting because it’s a big change from data collection. It’s very unlikely that you will have collected exactly the data you set out to collect, but your analysis plan will keep you on track and avoid the dangers of aimlessly exploring your dataset. You will probably need further statistical advice at this stage. This chapter guides you through data preparation, initial data analysis, hypothesis testing, calculating your effect sizes and confidence intervals, interpreting your results and extrapolating from them.

Statistical evidence is fundamental to science. Understanding statistics helps us to understand the literature and assess it critically, refine our research questions into testable hypotheses and predictions, design studies that are appropriate to test these predictions, evaluate whether our findings support our predictions, and derive appropriate conclusions. The dominant paradigm in primatology and allied disciplines is to test whether patterns we observe in our observations are due to more than random variation in our data. However, the statistical analyses we use to do this are very often misinterpreted. In this chapter I distinguish different kinds of variables, then introduce relationships between variables. I explain how we use statistical analysis to infer something about a theoretical population based on a sample. I introduce null hypothesis significance testing and explain common misunderstandings of this approach. I review the two types of error that arise in NHST and the concept of statistical power. I explain the need to assess and report effect sizes and confidence intervals, briefly introduce alternatives to null hypothesis statistical testing and end with how to interpret statistical results appropriately.