Meta-Analysis and Systematic Literature Review

Time & place: 26.3.-27.3.2020, Hanken, Helsinki


Meta-analysis is a statistical procedure for combining data from multiple studies. The aims of meta-analysis are to provide a synthesized overview of the current state of research; increase statistical power of tests; deal with controversy when individual studies disagree; estimate the size of certain effects; and answer new questions not previously posed in existing studies. Meta-analyses are frequently used to develop new conceptual frameworks which reconcile past research and to gain research insights, identify gaps in the literature, and provide future research directions. This course aims at discussing the basic tenets of meta-analysis and illustrating recent approaches. During this two-day course, Prof. Markus Blut will present the different steps for planning and conducting a meta-analysis. The participants will learn how to conduct a meta-analysis, which meta-analytic choices are available to them and which judgement calls to make, and how to assess existing meta-analyses. They will also learn which mistakes to avoid when conducting meta-analysis. You need to bring your own laptop with you to the course.

Target group:

Meta-analysis is a widely used method which is applied in various disciplines. The course is offered to doctoral students in business administration, economics, social sciences, and other disciplines. The course material uses mainly meta-analyses in business research as examples for applying the method.  

Learning goals:

After completing this course, students will be able to:

  • understand the need for meta-analysis,
  • plan and conduct a meta-analysis,
  • conduct a literature search,
  • choose an appropriate effect size for the meta-analysis,
  • prepare a coding plan for collected studies,
  • convert statistical information into effect sizes,
  • differentiate and choose between fixed- and random-effects meta-analysis,
  • understand the underlying formulas,
  • calculate averaged effect sizes,
  • understand the importance of outlier tests,
  • test homogeneity of effect sizes,
  • understand and conduct tests for publication bias,
  • conduct subgroup-analysis and meta-regression,
  • understand differences across various meta-analytic approaches,
  • gain insights into advanced meta-analytic techniques, and
  • evaluate the quality of a meta-analysis.

Student workload:

The course consists of two days. The first day is dedicated to the conceptual issues in meta-analysis. We discuss what a meta-analysis is and how it should be conducted. The students will also learn how to search for empirical studies, developed a coding plan, and understand which information to extract from collected studies. They learn to differentiate between different effect sizes and calculate effect sizes from reported statistical information. They learn to differentiate between two statistical models (i.e., fixed-effect and random-effects) and how to calculate averaged effect sizes and the underlying formula. The students will be given several exercises.

The first day reviews the basic methods:

  • Problem definition
  • Document Retrieval
  • Coding
  • Effect sizes and computation
  • Analysis of effect sizes
  • Publication Bias

The second day focuses on more advanced analyses and other decisions to be made. The students learn how to compare effect sizes across samples using subgroups-analysis. Also, they learn how to use meta-regression to tests the simultaneous influence of various study characteristics on effect sizes. The second day uses various exercises where students have to analyse small data sets. We also briefly discuss other issues such as conducting meta-SEM and multi-level SEM. We further compare the employed meta-analysis approach with other approaches to understand the differences between them. Finally, we discuss several meta-analyses which serve as examples of good practice.

The second day covers the following issues:

  • Subgroup analysis
  • Meta-regression
  • Complex data sets
  • Judgement calls and method choices
  • Common mistakes in meta-analyses
  • Advanced analyses: meta-SEM, multilevel analysis
  • Comparison of meta-analytic approaches
  • Discussion of good-practice meta-analyses


For both days,

  • read the selection of academic literature on meta-analysis prior to the start of the course (see course literature),
  • mandatory presence and participation in course discussions, and
  • participation in small (group) exercises. 

Course literature:

Pre-readings (Method Papers):

1.     Aguinis, H., Dalton, D. R., Bosco, F. A., Pierce, C. A., & Dalton, C. M. (2011). Meta-analytic choices and judgment calls: Implications for theory building and testing, obtained effect sizes, and scholarly impact. Journal of Management37(1), 5-38.

2.     Bergh, D. D. et al. (2016). Using meta‐analytic structural equation modeling to advance strategic management research: Guidelines and an empirical illustration via the strategic leadership‐performance relationship. Strategic Management Journal37(3), 477-497.

3.     Grewal, D., Puccinelli, N., & Monroe, K. B. (2018). Meta-analysis: integrating accumulated knowledge. Journal of the Academy of Marketing Science46(1), 9-30.

4.     Palmatier, R. W., Houston, M. B., & Hulland, J. (2018). Review articles: purpose, process, and structure, Journal of the Academy of Marketing Science,DOI 10.1007/s11747-017-0563-4.

5.     Shaw, J. D., & Ertug, G. (2017). The suitability of simulations and meta-analyses for submissions to academy of management journal, Academy of Management Journal60(6), 2045-2049.

Pre-readings (Exemplary Meta-Analyses)

1.     Blut, M., & Wang, C. (2019). Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science, DOI 10.1007/s11747-019-00680-8.

2.     Motyka, S. et al. (2014). Regulatory fit: A meta-analytic synthesis. Journal of Consumer Psychology,24(3), 394-410.

3.     Palmatier, R. W., Dant, R. P., Grewal, D., & Evans, K. R. (2006). Factors influencing the effectiveness of relationship marketing: a meta-analysis. Journal of Marketing70(4), 136-153.

4.     Zlatevska, N., Dubelaar, C., & Holden, S. S. (2014). Sizing up the effect of portion size on consumption: a meta-analytic review. Journal of Marketing78(3), 140-154.

Furthermore, readings include the lecture handouts and statistical software guidance provided by Prof. Markus Blut.

 Recommended Timing:

The course is advised to be taken after the students have attended a basic statistics course; students should be familiar with SPSS. While the course will cover various statistical formula, significance tests, and methods (e.g., regression analysis), it focuses on the application of these as part of the meta-analytic process.


A max of 25 students are admitted to the course.

Application deadline: February 16. Accepted participants will be notified: February 18


Professor Markus Blut

Professor of Marketing, Aston University, Birmingham, UK

Profile website:

Coordinator (contact for all administrative questions regarding the course)

Professor Veronica Liljander,

Course credit: 

6 ECTS, divided into:

·      3 ECTS for reading the literature and actively participating in the full two seminar days.

·      3 ECTS of additional credits for passing the following assignment: One exercise assignment to be submitted within 1 month after the course. In order to receive credits, students need to pass the exercise (pass/fail). Instructions for the assignment will be given during the course days.

KATAJA course application form:

Apply by copying this link to your browser and filling in the form.