This column provides a brief synopsis and reference information for select recent journal publications. Note that this is only a short sampling of publications and is not intended as a comprehensive listing.
Coding Practices and Iterativity: Beyond Templates for Analyzing Qualitative Data
This analysis provides new insight into what it means to iterate in the service of driving analysis. Iteration is examined through the lens of the analytic process of coding in specific research projects. Using a relational definition of coding, the reported coding actions of several studies are identified with rich descriptions of their analytical processes. It is useful to understand these coding actions in the context of coding moments that relate to how researchers use the coding actions as their project develops. The moments identified are making codes, organizing to code, and putting patterns together. To show iteration, the reported coding practices are traced in exemplar articles. These tracings indicate that the reported process is not a fixed or consistent sequence. Rather, iterativity is organized by what is the next needed analytic input required to progress a given situated study.
Locke, K., Feldman, M., and Golden-Biddle, K. (2020). Coding Practices and Iterativity: Beyond Templates for Analyzing Qualitative Data. Organizational Research Methods. Online First. https://journals.sagepub.com/doi/full/10.1177/1094428120948600opens in new window
A scoping review of the relationship between students' ICT and performance in mathematics and science in the PISA
This scoping literature review examines the relationship between ICT and performance scores in mathematics and science for students around the world included in the PISA assessment. The different types of ICT revealed different relationships with performance, depending on the subject and country of the students being examined. It seems that moderate use, rather than high or no use of ICT, can positively predict academic scores. Autonomy, interest, and use of ICT as a topic in conversations seem to positively predict both mathematics and science scores in 15-year-old students. Implications, limitations, and recommendations are discussed.
Odell, B., Cutumisu, M., and Gierl, M. (2020). A scoping review of the relationship between students' ICT and performance in mathematics and science in the PISA. Social Psychology of Education, 23, 1449-1481. https://doi.org/10.1007/s11218-020-09591-xopens in new window
Theory Construction Methodology: A practical framework for theory formation in psychology
This paper presents a practical methodology for constructing explanatory theories: Theory Construction Methodology, which involves a sequence of five steps. First, the theorist identifies empirical phenomena to become the target of explanation. Second, the theorist constructs a proto-theory: a set of theoretical principles that potentially explain these phenomena. Third, the proto-theory is used to construct a formal model: a set of model equations or simulation models that encode the explanatory principles. Fourth, the theorist investigates this model’s explanatory adequacy. This is done by formalizing the empirical phenomena in terms of the model, and assessing whether the model indeed reproduces them. Fifth, the theorist studies the overall adequacy of the theory by evaluating whether phenomena are indeed reproduced faithfully, whether explanatory principles are parsimonious and substantively plausible, and whether the theory implies new predictions to promote further research. This methodology is discussed in detail with practical examples.
Borsboom, D., van der Maas, H., Dalege, J., Kievit, R., and Haig, B. (2020, February 29). Theory Construction Methodology: A practical framework for theory formation in psychology. https://doi.org/10.31234/osf.io/w5tp8opens in new window
Multiple-Group Invariance with Categorical Outcomes Using Updated Guidelines: An Illustration Using Mplus and the lavaan/semTools Packages
Meaningful comparisons of means or relationships between latent constructs across groups require evidence that measurement is equivalent across the studied groups—a property known as measurement equivalence or invariance (ME/I). Methods typically involve an evaluation of increasingly stringent models via confirmatory factor analysis, a typical assumption of which is continuous observed variables. Alternative methods that directly model the categorical data exist. Although well established, categorical ME/I models pose a number of complexities and various recommendations for their evaluation. The current state of categorical ME/I is described, and an up-to-date method is presented for model identification and invariance testing.
Svetina, D., Rutkowski, L., and Rutkowski, D. (2020). Multiple-Group Invariance with Categorical Outcomes Using Updated Guidelines: An Illustration Using Mplus and the lavaan/semTools Packages. Structural Equation Modeling, 27(1), 111–130. https://doi.org/10.1080/10705511.2019.1602776opens in new window
Guidelines for the “publication corner” column
Have you seen a recent publication that you think would be of great interest to Div. 5 membership? Please feel free to share suggestions for publications to include in this column. Please send your ideas to The Score editor, Gill Sitarenios. Please be sure to include full reference details and a short synopsis (two to three sentences) of the publication. Use “Publication corner” in the subject line of the email.
