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This chapter describes methodological issues that are relevant for research on recovery. We aim to provide an overview of methodological approaches that have been or can be used in recovery research, and to provide methodological guidelines that researchers may use in assessing the process of recovery. We argue that studies on recovery must be explicit about recovery settings, recovery processes (i.e., activities and experiences) and recovery outcomes. We describe typical operationalizations of these three perspectives and focus in more detail on potential measures of recovery outcomes. We give an overview of research designs including experiments and quasi-experiments, diary studies, and longitudinal field studies. We conclude by pointing to remaining challenges for researchers in the area of recovery.

The introductory chapter includes how to design-in good practices in theory, data collection procedures, analysis, and interpretations to avoid these bad practices. Given that bad practices in research are ingrained in the career training of scholars in sub-disciplines of business/management (e.g., through reading articles exhibiting bad practices usually without discussions of the severe weaknesses in these studies and by research courses stressing the use of regression analysis and structural equation modeling), this editorial is likely to have little impact. However, scholars and executives supporting good practices should not lose hope. The relevant literature includes a few brilliant contributions that can serve as beacons for eliminating the current pervasive bad practices and for performing highly competent research.

In this paper, we develop a simple model of the signaling value of the General Educational Development certificate (GED) credential. The model illustrates necessary assumptions for a difference-in-differences estimator that uses a change in the GED passing standard to yield unbiased estimates of the signaling value of the GED for marginal passers. We apply the model to the national 1997 passing standard increase, which affected GED test takers in Texas. We utilize unique data from the Texas Schools Micro Data Panel (TSMP) that contain demographic and GED test score information from the Texas Education Agency linked to pre- and post-test-taking Unemployment Insurance quarterly wage records from the Texas Workforce Commission. Comparing Texas dropouts who acquired a GED before the passing standard was raised in 1997 to dropouts with the same test scores who failed the GED exams after the passing standard hike, we find no evidence of a positive GED signaling effect on earnings. However, we find some evidence suggesting that our finding may be due to the low GED passing threshold that existed in Texas for an extended period.

Tests of assumed mediation models are common in research in many disciplines, including managerial psychology, industrial and organizational psychology, organizational behavior, and organizational theory. Thus, the purpose of this paper is to detail experimental design options for conducting such tests in a manner that has the potential to yield results that have high levels of internal and construct validity.

The paper presents a logical analysis of strategies for testing mediation models so as to insure valid inferences about causal relations between variables.

The most appropriate strategy for testing assumed mediation models is research that uses randomized experimental designs.

Managers should base their actions on valid evidence about phenomena. More specifically, managerial actions should be predicated on research results that have high levels of internal, construct, and statistical conclusion validity. Thus, this paper encourages managers to base decisions about organizational policies and practices on well‐designed experimental research.

This paper addresses a number of points about issues involving internal and construct validity in tests of assumed causal models that have not been covered in previous work.

To argue that enhanced professional development and writing for publication are related activities and are mutually beneficial.

An opinion piece.

That by aiming for a more rigorous form of professional writing, greater insight into one's professional practice is possible.

The examples of research scenarios sketched out in this editorial are pure suggestions which have not been carried out as real experiments. However, this would be possible and even desirable in order to prove the hypothesis outlined in the paper.

If these conclusions are valid, then writing for publication has a clear practical benefit for practitioners.

The simple examples of the workplace experiment and case study that this paper briefly sketches out could help practitioners improve the type of “practitioner research” that they undertake.

This paper aims to advocate and facilitate undertaking research focused on the effects of human behaviour, judgment and decision making in logistics and supply chain management (SCM).

In addition to providing an overview of the potential benefits of behavioural research, this paper presents two modified frameworks for identifying and addressing behavioural issues in logistics and SCM.

Behavioural research can significantly advance both theory and practice in logistics and SCM. Little behavioural research appears in top logistics journals. As researchers begin to conduct more such projects, knowledge pertaining to issues of importance to logistics and SCM will be created.

This paper highlights an important research area and a methodology, (controlled behavioural experiments), that are currently underutilized in logistics and SCM. It further presents potential research questions and suggestions for ways in which interested researchers could begin to address such issues.

A relatively recent advance in analyzing longitudinal data, structural equation modeling with structured means, for examining the impact of organizational change and development interventions, is presented. Some of the limitations of current approaches to analyzing data collected from “experimental” and “control” groups are discussed, along with why structural modeling is particularly useful for real‐world experiments and quasi‐experiments. An illustration is then given, applying this approach to data collected from a team‐building intervention which involved 2,331 employees in 16 plants of a large garment manufacturer. Implications of the research are briefly considered.

Among the gold standards in human resource development (HRD) research are studies that test theoretically developed hypotheses and use experimental designs. A somewhat typical experimental design would involve collecting pretest and posttest data on individuals assigned to a control or experimental group. Data from such a design that considered if training made a difference in knowledge, skills or attitudes, for example, could help advance practice. Using simulated datasets, situated in the example of a scenario-planning intervention, this paper aims to show that choosing a data analysis path that does not consider the associated assumptions can misrepresent findings and resulting conclusions. A review of HRD articles in a select set of journals indicated that some researchers reporting on pretest-posttest designs with two groups were not reporting associated statistical assumptions and reported results from repeated-measures analysis of variance that are considered of minimal utility.

Using heuristic datasets, situated in the example of a scenario-planning intervention, this paper will show that choosing a data analysis path that does not consider the associated assumptions can misrepresent findings and resulting conclusions. Journals in the HRD field that conducted pretest-posttest control group designs were coded.

The authors' illustrations provide evidence for the importance of testing assumptions and the need for researchers to consider alternate analyses when assumptions fail, particularly the homogeneity of regression slopes assumption.

This paper provides guidance to researchers faced with analyzing data from a pretest-posttest control group experimental design, so that they may select the most parsimonious solution that honors the ecological validity of the data.