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Researching Entrepreneurship pp 247–284Cite as

The Power of Replication

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Part of the book series: International Studies in Entrepreneurship ((ISEN,volume 33))

Abstract

How can we develop more solid knowledge about entrepreneurship? Like in other fields of research, the truth is that we never know the truth, but that we can arrive at increasingly accurate approximation of it. In this collective quest of knowledge development, statistical significance testing is a sadly overused tool, while replication of prior research is a better but sadly underused tool. After reiterating the limitations and frequent misuse of significance testing, this chapter illustrates how we can make progress by replicating others (traditional replication studies), each other (harmonized research collaboration), and ourselves (using multiple samples or sub-samples; robustness testing). The chapter ends on a high note with observation of several signs that our research culture may finally be about to start embracing the importance of replication and reproducibility.

“All studies have limits. It is only in their combination that evidence reveals itself”

(Rousseau, Manning, & Denyer, 2008 , p. 50)

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Notes

  1. 1.

    It’s ‘Strailian, mate! And for you Aussies: yes, with both an “e” and an “h.” Stop whingeing about my spelling…

  2. 2.

    I assume here that you apply a one-tailed test, i.e., that you halve the “associated probability” (p-value) typically reported by your statistics package. Reporting one-tailed tests for directional hypotheses is not a “dubious practice” as some geniuses out there would have it—it is a logical and linguistic necessity if you want “5 % level of risk” to mean “5 % risk of reporting a false positive result.”

  3. 3.

    It almost brings me to tears that otherwise smart and knowledgeable PhD students never find the right answer to this problem when I pose it to them. As the problem is formulated, the only insight you really need is that if you repeatedly draw random samples from a population with effect size x, half of the samples are going to show an effect larger than x, whereas the other half will yield estimates smaller than x. Sampling variation in the variance of variables may mean that this example’s assumed 1:1 relationship between effect size and significance does not hold to the full, but these deviations would cancel out for a fifty-fifty end result.

  4. 4.

    In the next paragraph (also p. 198), the authors offer the following, peculiar analysis “As indirect indicators of substantive and external validity, we coded the number of independent-to-dependent variables in each study that were statistically related (…) the ratios of statistically related to unrelated variables for the periods were 0.87 and 1.05, a significant increase over time (p < 0.01).” Again, the increase over time is a fact about the studied population of articles or the result of some measurement error about which the statistical test is silent. More importantly, I am mystified as to how this trend is supposed to reflect improvements in “substantive and external validity.” Assuming that by “statistically related” they mean that the relationships “achieve statistical significance,” the effect probably reflects an increase in average sample size, which likely indicates an improvement in research quality. However, the effect probably also reflects that authors and/or editors are becoming less prone to submit and accept for publication, respectively, papers with (many) nonsupported hypotheses. If so, this indicates increased confirmation bias. While such bias is a pervasive human trait, it is certainly not an indicator of research quality (Davidsson & Wahlund, 1992; Fanelli, 2010).

  5. 5.

    Some colleagues would refer to “a hypothetical population” and/or “safeguarding against the influence of some unknown stochastic process” (as the culprit behind the observed difference/effect) to justify statistical testing. I have occasionally done so myself, but I think we are just kidding ourselves when we try such defenses. For lack of better alternatives, we continue to fantasize that significance testing is that strong, truth-telling tool that we need, but which simply does not exist.

  6. 6.

    Note that absence of a meaningfully strong effect can also be theoretically and practically important, but then of course it is the small magnitude of the effect that should be highlighted in the reporting of results. You might argue that for some type of variables (operationalizations) it is very hard to say what size an effect needs to be in order to be meaningful. This is true, because we cannot measure everything in easily interpretable units like numbers of dollars or people. But shouldn’t you then apply the same logic to the cutoff for what is to be regarded “significant”? In a regression context, the magnitude of unique contribution to R 2 can always be used. Further, you can find ways to give more meaning to results referring to an arbitrary scale. For example, in Davidsson (1995c) I explain: “For example, for ‘Need for Autonomy’ the difference is 0.38 [p = 0.02] on a scale with possible values from 4 to 16. All that is needed to obtain such a difference is for 20 of the respondents in Region A [i.e., about 10 % of them] to choose a response alternative one step further towards the ‘entrepreneurial’ end of the scale on each of the four items in the index than do a set of 20 respondents from Region B, while the average for ‘all others’ in the two regions is identical.” This portrayal of a “significant” difference is quite far removed from conveying the image that “in Region A people in general hold more entrepreneurial attitudes than do people in Region B.”

  7. 7.

    Of course, there is a previous round of considering how “our study” relates to “the world…” at the design stage, in setting up our experiment, selecting our cases or interviewees, defining a sampling frame and drawing a sample from it, and in operationalizing theoretical constructs.

  8. 8.

    If the research is experimental, statistical significance has a role here: is there considerable risk that the results within our sample are due to an unfortunate distribution of participants to experimental conditions so that the supportive results may be spurious? I find the use of statistical testing in an experimental context relatively unproblematic. It is fairly clear what you are going to test before you analyze the data; there is typically no large pool of correlations to potentially over exploit, and there is (I sincerely hope) no fantasizing that “significant” means “true” for an outside population—it merely means the results are unlikely to be wholly attributable to preexisting differences among your experiment participants. But as pointed out by Simmons et al. (2011), there is quite a bit of fishing potential in the experimental pond as well.

  9. 9.

    Don’t even try! When I vented my views on statistical significance on Facebook, a colleague–friend replied “This is one of the best justifications I have known to undertake qualitative research. Thanks Per.” To which I replied, “Sorry NN (…) The problem with statistical significance (as applied) is a within-paradigm problem. You certainly do not gain any credibility for external validity claims by reducing, per se, the number of cases studied (…) Qualitative (small n) research has its roles, but securing external validity is not one of its strong points.”

  10. 10.

    Sic! It should be “taxonomy.”

  11. 11.

    Like I said in a dissertation footnote: “Typologies [sic; see the above note!] arriving at 2–11 groups, on the basis of different approaches and with more or less of systematic empirical backing, may be found in: [nine references]. Conclusion: adding another one would be superfluous” (Davidsson, 1989a, p. 158). However, see Woo, Cooper, and Dunkelberg (1991)—a paper which made the popularity of entrepreneurial taxonomies and typologies plummet—for evidence of instability of Smith’s types.

  12. 12.

    We also stubbornly (valiantly?) refused to report significance tests for analyses of our population data: “It should be noted that the study covers the whole population of establishments and regions. Statistical significance thus is a non-issue and such tests are therefore not reported” (Davidsson et al., 1994, p. 397). This collaborative effort provides a good example of how stupid it would be to use statistical significance to assess the observed effects. The US study (Reynolds, 1994) included 15 times as many regions (380-ish) as did the Irish study (23-ish), meaning that if exactly the same coefficients were obtained for the two countries, they would likely be judged “significant” for the US but not for Ireland. Time to turn the brain back on; the comparison is between empirical facts for two different countries, not statistically uncertain estimates.

  13. 13.

    Honig and Samuelsson (2014) is an extended reanalysis rather than a replication on new data.

  14. 14.

    For ease of communication, the numbering of experiments and hypotheses as well as the exact wording of the hypotheses have been adapted for this example but stay true to the essence of the underlying research.

  15. 15.

    One examiner suggested the analyses should be broken down by the sex of the participants. Seems a reasonable suggestion….

  16. 16.

    Actually, adding a separate second sample is even better, because then neither the data source nor the process of developing the model are subject to stochastic idiosyncrasies that are capitalized on in model fitting. In addition, a trusted colleague questions the logic of tryout/holdout altogether on the grounds that if split randomly, the only source of difference in results is stochastic variation. I would counterargue that the strategy mitigates the tendency to step-by-step make adaptation that capitalize on stochastic relationships in the data, but agree that a separate, second sample that did not affect model development is even better.

  17. 17.

    In addition, they do all they can to reduce experimental research’s eternal problem of questionable external validity by using samples of real entrepreneurs as well as real technologies and real market needs in the experimental design. It’s an awesome piece of entrepreneurship research.

  18. 18.

    Special thanks to A. A. Aarts; some name-changing ancestor; Brian Nosek, and the Science editors for giving me the chance to put this important work as the very first entry in this book’s rather long list of references!

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  1. QUT Business School, Queensland University of Technology, Brisbane, QLD, Australia

    Per Davidsson

  2. Jönköping International Business School, Jönköping, Sweden

    Per Davidsson

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  1. Per Davidsson
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Davidsson, P. (2016). The Power of Replication. In: Researching Entrepreneurship. International Studies in Entrepreneurship, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-319-26692-3_9

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