Doctoral Study Page Counts…

Every hear this?

What is the minimum page count for a doctoral study?

Doctoral student on any given day of the week

I suspect the average student that asks this question has realized that completing a doctoral study will take an investment of time and effort, and they are mentally working through a Gantt chart to show percentage completion. However, any learned committee member understands that it will take as long as it takes to explore a topic and complete the study.

There are some observations I have made –

  • Introduction – This section should be the same regardless of study type (QUAN, QUAL, or MM)
  • Literature Review – A QUAN study will have a more lengthy literature review than a QUAL study because the researcher has to explore and substantiate the inclusion of each variable of interest. Conversely, a QUAL study will have a less lengthy literature review than a QUAN study because the purpose is to obtain a better understanding of a phenomena than what has already explored. If the topic of inquiry has explored in- depth, the reason to perform a QUAL study may not be justified. A MM study will be longer than a QUAN since it includes both QUAN and QUAL components.
  • Methodology – This section should be the about the same for both QUAL or QUAN, but a MM study will be longer since it includes both QUAL and QUAN aspects.
  • Results – A QUAN study will have a less lengthy results sections than a QUAL study because the focus is on the statistical tests. The section be especially less lengthy if tables and figures are placed in an Appendix rather than embedded in the text. A QUAL study will be larger than a QUAN study because it includes support for the thematic development. To do that, a researcher includes anecdotal quotes from interviews and, possibly, documentation obtained from the data collection phase. Finally, connecting themes to prior research in the area and, if not found, performing a mini-literature review will add more length to this section. It could be extremely more lengthy if transcripts are included in this section (rather than an Appendix). A MM study, obviously, will be much larger since it includes both a QUAL and QUAN component.
  • Recommendations – This section should be the same in size regardless of methodology.

Since I’m looking at doctoral studies published by ProQuest in 2019, I thought I would examine page counts. Based on an M = 158.6, SD = 55.18, Mdn = 147.5, a 100-200 page estimate appears right (Figure 1).

Figure 1. Boxplot of Doctoral Study Page Counts for DBA degrees awarded in 2019 (as reported by ProQuest)

Note the 400 and 600 page count studies….ugh!

Next, I wanted to focus on the Top Five schools that create doctors of business administration to see if they differed (Figure 2).

Figure 2. Boxplot of Doctoral Study Page Counts for the Top Five Universities that awarded DBA degrees in 2019 (as reported by ProQuest)

The 100-200 page guidance appears reasonable…

Note: Boxplots were created using R and the ggplot2 package.

When categorical variables and moderation analysis goes wrong…

I stumbled across a dissertation (Bosh, 2020), in which the student performed a moderation analysis using categorical variables. By performing a moderation analysis, a researcher is examining if the causal relationship between an independent variable (X) and the dependent variable (Y) changes upon the introduction of a moderating variable (M). To test for moderation, both X and M must be entered into the regression formula, to examine the main or simple effect, along with the interaction (X*M).

Y = i + aX + bM + cXM + e                      (1)

If the p-value of the moderating variable is statistically significant, then the main effects are ignored and the moderator becomes the focus. I have found moderation analysis can be confusing to students who don’t have a good grasp of statistics.

The student examined categorical variables as moderators. Categorical variables of three or more levels should be dummy-coded, since categorical variables with two levels are naturally dichotomous (0/1). This study had four categorical variables: Age, Gender. Marital Status, and Tenure (p. 110). The student references dummy coding but only in relation to Gender and Marital Status; two variables that are either naturally dichotomous (Gender) or artificially dichotomized in the study (Marital Status). No reference to dummy coding was made for Age and Tenure (p. 117). Student Note #1: When using categorical variables in regression, make sure you understand dummy coding.

In dummy coding, a researcher transforms a nominal or ordinal variable into k-1 variables (k refers to the number of levels). For each variable, a specific category is coded as a 1 and all other units are coded as 0. Age, for example, has three levels: 18-33, 34-49, and 50-65. Age would be dummy coded into two variables (Age1 and Age2), with 34-49 being represented by a 1 in Age1 and 50-65 being represented by a 1 in Age2. The base level, 18-33, would be represented as a 0 in both Age1 and Age2. Thus, Age1 and Age2 would be represented as deviations from the base level. For a great discussion of dummy coding, effect coding, and weighted effect coding, see Grotenhuis et al. (2017).

When the student begins testing hypotheses (p. 134), I know two variables are coded correctly and two that are questionable. However, upon inspection of the output, I note that there is no evidence that moderation was examined. In reviewing Table 22 in the study, the main affect of Job Satisfaction was used as a predictor of Affective Commitment in Model 1. However, the two poorly formed MVs of Age and Tenure were entered as a block in Model 2. Entering additional variables into a regression formula and examining the changes is not moderation analysis; it is simply a measurement of change in a model upon the inclusion of additional variables. The other two dichotomous variables, Gender and Marital Status, are entered as a block in the third model.

What does all this mean? Well, the student didn’t structure the moderation analysis properly. First, ordinal independent variables were not dummy-coded properly. Second, interaction was not examined. Could there be a moderating effect of these categorical variables? Maybe. We’ll never know. Technically, this is an example of a combined Type II and Type III error.

I reached out to Capella University via email to request the student’s email address so I could include his thoughts in this discussion; potentially working to perform a post-publication analysis of data. The University did not reply to my email nor to my follow-up phone call to the University’s FERPA representative. I also reached out to the student’s chairperson for comment. No reply.

Instructions to Students

Ignore the results of this study. However, the framework set by Bosh (2020) is ripe for replication. Simply cite the results of the study and the problems in the analysis as a reason for the need to replicate, and do the analysis correctly.

Reference:

Bosh, G. B. (2020). Explanatory relationships among employees personal characteristics, job satisfaction, and employee organizational commitment (Doctoral dissertation). ProQuest Dissertations Publishing. (27837234)

Grotenhuis, M., Pelzer, B., Eisinga, R., Nieuwenhuis, R., Schmidt-Catran, A., & Konig, R. (2017). When size matters: advantages of weighted effect coding in observational studies. International Journal of Public Health, 62(1), 163–167. https://doi.org/10.1007/s00038-016-0901-1