test

Covariance-based SEM, PLS-based SEM, and linear regression models overlap in many ways, including analysis objectives, distribution assumptions, and etiological and correlational linearity assumptions. Nonetheless, there are distinct differences among the three approaches that makes each more or less appropriate for certain types of analysis. Furthermore, even when all three techniques are appropriate, the resulting set of supported hypotheses in the model may be more or less credible because of underlying data distribution assumptions and the analysis methods employed.

Thus, choosing an analysis method based correctly on the research objectives and the limitations imposed by the sample size and distribution assumptions is crucial. The importance of establishing statistical conclusion validity using such tools in positivist research cannot be overemphasized. It is, in essence, the strength of evidence researchers have to report in order to prove that their models are supported by data collected. Indeed, studies lacking strong statistical conclusion validity are highly questionable [Cook and Campbell, 1979]. This paper has presented key criteria for effective practices in the use of new and old tools for this form of validation. These guidelines are summarized in the tables throughout the tutorial.

The meta analysis shown in Tables 13 and 14 indicates that much still must be done in this regard. There is wide disparity among journals on utilization of SEMs. In ISR, for instance, 45% of empirical articles use SEM techniques, whereas in MISQ, this figure is closer to 25%. Assuming that SEM techniques represent state-of-the-art in many research settings, this discrepancy must be heeded. Editors and reviewers may want to encourage authors to use SEM tools, where appropriate. Nonetheless, as noted in this article, there are situations where SEM tools are not called for. In such cases, editors and reviewers will want to ensure that authors are not over-using the techniques, by, perhaps, choosing them for mimetic rather than for solid, technical reasons.
To internalize such statistical knowledge, editors, associate editors, and reviewers will want to immerse themselves in at least the three (or four, including factor analysis) techniques touched on in this article. There are many instances where an editor will be confronted with disagreements among the methodological experts asked to review and where merely adding another knowledgeable reviewer is not going to resolve the issue. The reviewing process should not be a vote. It should be a set of judgments, where more knowledgeable opinions are weighted more heavily than those of less understanding.
Hopefully, this article has resulted in a renewable and upskilling of some faculty in this area. Courses in LISREL are de rigeur for many doctoral graduates since 1990 and in doctoral-granting institutions where it is not, such courses need to be added. The history of our oldest academic journals, such as MIS Quarterly, is testimony to the requirement for post-millennium researchers to be careful methodologists as well as content specialists.
Guidelines as to when to use each SEM and what statistics need to be reported are clearly necessary. In this tutorial, we have summarized some of the most important aspects to be considered when choosing a SEM technique and we have reviewed the most widely used statistics reported together with their established thresholds. As can be seen from Tables 13 and 14, many studies report only a partial set of these statistics, and, even then, many of these statistics fall short of the common thresholds. As in any other statistical method, when the statistics are not within their respective thresholds, the conclusions drawn based on the analysis are potentially flawed. Applying the appropriate analysis technique, given the research objective and the data, reporting the appropriate statistics, and ensuring that their values are within the established thresholds, is crucial in LISREL [Chin, 1998a, Jöreskog and Sörbom, 1989], PLS [Chin, 1998a], and linear regression models [Cohen, 1988, Cook and Campbell, 1979, Hair et al., 1998, Neter et al., 1990, Nunnally and Bernstein, 1994]. Guidelines for such clear reporting are obviously necessary for good positivist science [Chin, 1998a].

We hope this tutorial provides researchers with a helpful and practical tool toward reaching these objectives.