How to Choose the Right Statistical Tests for Hypothesis Testing in Management Research: A Guide for PhD Scholars
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A Guide for PhD Scholars
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Introduction
For most PhD students in management, quantitative analysis is often the toughest part of the research process. The process of choosing the right statistical test is confusing because students try to tie their hypotheses into a statistical analysis test when they have a little understanding of the statistical tests they are used to reading in the literature, the statistical assumptions, and are relying on SPSS or SmartPLS, all while being told they should be focussed on a p-value rather than the effect size or theoretical significance. In fact, as Lindsay (2025) describes, our traditional emphasis on “significance testing” ideology provides theoretical blinders, so it is important for researchers to understand how to run a statistical test while also clearly explaining why they chose that statistical test, and what the analysis tells them about their data.
1. Understanding the Purpose of Hypothesis Testing
Hypothesis testing indicates whether the differences or relationships you observed in the data are statistically significant, or unlikely to have occurred by chance (Kalpande & Toke, 2023). In management-related research, hypothesis testing allows you to confirm theoretical propositions about something, such as the impact of leadership on employees, the adoption of an innovation, or employee engagement, among other things.
Example: Take a PhD student studying the relationship between transformational leadership and employee engagement who may propose:
- H1: Transformational leadership positively influences employee engagement.
In this case, either a Pearson correlation or linear regression would be an appropriate way to determine the strength or direction of that relationship. Kalpande and Toke (2023) further illustrate the process of hypothesis testing within their example of testing critical success factors in total productive maintenance, in particular, that hypothesis testing is how theoretical variables identified in a study connect to performance measures.
2. Aligning Research Design with Statistical Tests
The appropriate statistical test will depend on the hypothesis type, data measurement level, and number of groups or variables involved. Purohit (n.d.) mentions that it is important to “understand the type of variables you are working with and question structure to make appropriate decisions regarding test choice and how to apply it”.
Objective | Example in Management | Appropriate Tests |
Compare two groups | Male vs. female managers’ leadership scores | Independent samples t-test / Mann–Whitney U test |
Compare more than two groups | Job satisfaction across departments | One-way ANOVA / Kruskal–Wallis test |
Test relationships | Motivation vs. performance | Pearson / Spearman correlation |
Predict outcomes | Predicting turnover from job stress | Multiple regression |
Model complex effects | Culture mediating leadership and innovation | SEM / PLS-SEM |
Example:
A one-way ANOVA would be appropriate in a specific situation where a researcher is investigating training effectiveness in HR, Marketing, and Finance. In this situation, the dependent variable is the performance score (count data), which is continuous in nature, and the independent variable (department category) is categorical.
3. Checking Statistical Assumptions
Verifying assumptions is a means of checking that the chosen statistical method is appropriate. Failure to verify the assumptions can lead the researcher to draw incorrect or misleading conclusions (Kotronoulas et al., 2023).
Assumptions:
- Normality: The data occur in a bell-shaped curve (Shapiro–Wilk test).
- Homogeneity of variances: The variance across groups is equal (Levene’s test).
- Linearity: The relationship between the independent and dependent variables is linear (scatterplot).
- Independence: The observations are unrelated.
Example:
If a researcher compares productivity between two different leadership styles and finds that the data were non-normal, then a Mann–Whitney U test (non-parametric) would be the appropriate alternative to a t-test.
4. Moving Beyond the Null Hypothesis Paradigm
Traditional null hypothesis significance testing (NHST) has an outsize influence in management research, but it only reduces inquiry to whether p < 0.05 (Lindsay, 2025; van Witteloostuijn & van Hugten, 2022). This practice bypasses effect size and prohibits theoretical interpretation.
For example,
Instead of saying: “There is a significant difference in satisfaction across departments (p < 0.05),” it could say: “Employees in HR report higher satisfaction than Finance (M = 4.2 versus M = 3.6), with a large effect size (Cohen’s d = 0.85).”
Schwab and Starbuck (n.d.) suggest a continuous model-based approach; we should assess the model compared to realistic managerial baselines, not an arbitrary null hypothesis or significance level.
5. Exploring Advanced Approaches
Newer methods, such as Bayesian and diagnostic testing, present alternatives to the traditional NHST.
Bayesian Hypothesis Testing
Neil and Fenton (2021) argue that Bayesian methods allow for the incorporation of previous knowledge with incoming data – a perfect combination when facing uncertainty or small sample sizes.
Example: A researcher interested in management could estimate the probability that remote work increases employee productivity by leveraging previous survey data combined with the current sample.
Diagnostic Testing.
Schmidt (2024) describes how auditors use hypothesis testing strategies to evaluate “tone at the top”.
Example: A researcher exploring ethical leadership could emulate this approach by testing how well managers self-assess and how this correlates with employee trust ratings.
Computational – Artificial Intelligence-Testing.
MS et al. (2025) propose advanced computational models for healthcare-related research that can be adapted to management-related analytics.
Example: A researcher could simulate how changes in process automations influence operational efficiency using Monte Carlo simulations.
6. Ensuring Measurement Reliability and Validity
Before hypotheses can be tested, measurement instruments must be both valid and reliable (Kalpande & Toke, 2023).
For example, a scale with 10 items to measure “employee engagement” should yield a Cronbach’s alpha coefficient of greater than or equal to 0.7, indicating internal consistency.
Purohit points out that questions that are poorly designed lead to invalid tests. You can validate constructs using exploratory factor analysis and confirmatory factor analysis (CFA) to confirm that the items measure the constructs they were intended to measure.
7. Using a Decision Framework for Statistical Choice
Van Witteloostuijn and van Hugten (2022) maintain in the end, the structured framework is the method that will best enhance the transparency of methodologies and the reliability of experiments.
An ordinary decision route may appear as follows:
Define the hypothesis type: “There exists a difference in stress levels between male and female managers.”
Determine the types of variables: Gender (nominal), Stress (interval).
Perform assumption checks: Data normality and variance homogeneity.
Choose test: t-test for unpaired samples.
Report results: Utilize test statistic, p-value, and effect size.
The usage of such logical reasoning not only increases the analytical rigor but also strengthens the position of your research in the case of thesis defense or publication review (Shestakov, 2021).
Conclusion
Choosing the appropriate statistical test is not merely a procedure but rather a methodological reasoning that links theory with proof. For doctoral students in management, the implication is to not only select tests mechanically but also to grasp well the logic of statistical decisions. If researchers are to perform their work at the intersection of statistics and theory, they will have to validate their assumptions, use reliable instruments, and even apply modern techniques like Bayesian or baseline modelling to ensure that their results are not only backed by statistics but also of theoretical importance. Such is the case with Schwab and Starbucks’ claim that meaningful management insights come not merely from the rejection of null hypotheses but from the construction and comprehension of the realities that determine organizational behaviour.
Are you ready to conduct a statistical test for your PhD dissertation?
At the PhD Assistance Research Lab, we specialize in guiding PhD scholars and researchers through every stage of this process. Our expert statisticians will guide you in the selection of a valid and strong statistical test for your dissertation.
Contact PhD Assistance Research Lab to complete your PhD research successfully.
References
- Kalpande, S. D., & Toke, L. K. (2023). Reliability analysis and hypothesis testing of critical success factors of total productive maintenance. International Journal of Quality & Reliability Management, 40(1), 238–266.
- Kotronoulas, G., Miguel, S., Dowling, M., Fernández-Ortega, P., Colomer-Lahiguera, S., Bağçivan, G., … & Papadopoulou, C. (2023). An overview of the fundamentals of data management, analysis, and interpretation in quantitative research. Seminars in Oncology Nursing, 39(2), 151398.
- Lindsay, R. M. (2025). The null hypothesis statistical testing paradigm undermines knowledge acquisition in management accounting research: It needs to be abandoned. In Advances in Management Accounting (pp. 1–55). Emerald Publishing Limited.
- MS, K., Ganpur, K. S., Ninawe, S. S., & Manjunath, T. C. (2025). Hypothesis testing in health care research—Novel approaches. Grenze International Journal of Engineering & Technology (GIJET), 11.
- Neil, M., & Fenton, N. (2021). Bayesian hypothesis testing and hierarchical modeling of ivermectin effectiveness. American Journal of Therapeutics, 28(5), e576–e579.
- Purohit, H. (n.d.). Importance of questionnaires in hypothesis testing in commerce and management.
- Schmidt, R. N. (2024). An examination of auditor hypothesis testing strategies in ‘tone at the top’ evaluations: Evidence of diagnostic knowledge structures. International Journal of Auditing, 28(3), 562–581.
- Schwab, A., & Starbuck, W. H. (n.d.). Why baseline modeling is better than null-hypothesis testing: Examples from research about international business and management, developing countries, and emerging market economies.
- Shestakov, D. (2021). The hypotheses testing method for evaluation of startup projects. Journal of Economics and Management Sciences, 4(4), 47–47.
- van Witteloostuijn, A., & van Hugten, J. (2022). The state of the art of hypothesis testing in the social sciences. Social Sciences & Humanities Open, 6(1), 100314.