How to conduct a standalone systematic literature review

How to conduct a standalone systematic literature review: a guide for social science scholars 

Rasch Measurement Theory

Educational and psychological measurement in the first half of the twentieth century was dominated by what I have called the Test Score Tradition (Engelhard, 2013). As its label suggests, the Test Score Tradition is dominated by sum scores with Classical Test Theory as a key example of measurement research in this tradition (Crocker and Algina, 1986). The second half of the 20th century witnessed the emergence of a Scaling Tradition that recognized the duality between items and person scores (Mosier, 1940, 1941).

As pointed out by van der Linden (2016), Rasch was one of the pioneers within the tradition that represented a paradigm shift from earlier measurement research. Rasch (1960), presented a set of ideas and methods described by Loevinger (1965) as a “truly new approach to psychometric problems” (p. 151) that can lead to “nonarbitrary measures” (p. 151). Rasch sought to develop “individual-centered statistical techniques [that] require models in which each individual is characterized separately and from which, given adequate data, the individual parameters can be estimated” (Rasch, 1960, p. xx).

Problems of invariant measurement played a central role in the development of Rasch’s measurement theory. As pointed out by Andrich (1988), Rasch presented “two principles of invariance for making comparisons that in an important sense precede though inevitably lead to measurement” (p. 18). Problems related to invariance played a key role in motivating his measurement theory. Rasch’s concept of specific objectivity and his principles of comparison form his version of the requirements for invariant measurement (Rasch, 1977). In his words,

 The comparison between two stimuli should be independent of which particular individuals were instrumental for the comparison; and it should also be independent of which stimuli within the considered class were or might also have been compared . Symmetrically, a comparison between two individuals should be independent of which particular stimuli within the class considered were instrumental for the comparison; and it should be independent of which other individuals were also compared, on the same or on some other occasion (Rasch, 1961, pp. 331–332).

 It is clear in this quotation that Rasch recognized the importance of both personinvariant item calibration, and item-invariant measurement of persons. In fact, he made them cornerstones in his quest for specific objectivity. In order to address problems related to invariance, Rasch laid the foundation for the development of a family of measurement models that are characterized by the potential to separate item and person parameters (Wright & Masters, 1982).

Andrich (1985) has made a strong and persuasive case for viewing the Rasch model as a probabilistic realization of a Guttman scale. Rasch measurement theory can be used to model the probability of dichotomous item responses as a logistic function of item difficulty and person location on the latent variable.

Figure above shows four item response functions based on the Rasch model. Model-data fit can then be based on the comparison between the observed and expected response patterns that is conceptually equivalent to other methods of evaluating a Guttman scale. Engelhard (2013) provides a description of several modeldata fit indices that can be used with the Rasch model. Rasch measurement theory (Rasch, 1960) provides a framework for meeting these requirements when acceptable model-data fit is obtained. Bond & Fox (2015) provide an accessible introduction to Rasch measurement theory.

Source: Wilson and Fisher (2017)

EXPLORATORY VERSUS CONFIRMATORY FACTOR ANALYSIS

There are two basic types of factor analysis: exploratory and confirmatory. Exploratory factor analysis (EFA) is used when the researcher does not know how many factors are necessary to explain the interrelationships among a set of characteristics, indicators, or items (Gorsuch, 1983; Pedhazur & Schmelkin, 1991; Tabachnick & Fidell, 2001). Therefore, the researcher uses the techniques of factor analysis to explore the underlying dimensions of the construct of interest. This was the approach that Leske (1991) used in her conceptualization of the dimensions of needs of families of the critically ill. EFA is the most commonly used form of factor analysis in health care research. It is what we will use to examine the dimensions of Concerns About Genetic Testing.

In contrast, confirmatory factor analysis (CFA) is used to assess the extent to which the hypothesized organization of a set of identified factors fits the data (Nunnally & Bernstein, 1994; Pedhazur & Schmelkin, 1991). It is used when the researcher has some knowledge about the underlying structure of the construct under investigation. CFA could also be used to test the utility of the underlying dimensions of a construct identified through EFA, to compare factor structures across studies, and to test hypotheses concerning the linear structural relationships among a set of factors associated with a specific theory or model. Pett, Wampold, Turner, and Vaughan-Cole (1999), for example, used CFA to test a hypothesized model predicting the paths of influence of divorce on young children’s psychosocial adjustment.

When undertaking a factor analysis using EFA, it is common practice to use more traditional statistical computer packages (e.g., SPSS, SAS, and BMDP) for the statistical analyses. CFA, on the other hand, requires a comprehensive analysis of covariance structures (Byrne, 1989). This form of measurement model is available in structural equation modeling (SEM). LISREL (Jöreskog & Sörbom, 1989) and EQS (Bentler, 1985) are two statistical computer packages that are used to undertake SEM analyses.

Pet et al. (2003)