Methodology

Groot, Adrianus Dingeman de; Spiekerman, J A A

doi:10.1515/9783112313121

Cited by 123 publications

(16 citation statements)

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“…The debate between supporters of the two approaches has a long history; for an example the reader is referred to De Leeuw (1988) and Molenaar (1988). From the theory‐driven perspective, the theory precedes the data (see, for example, De Groot (1969)). Theoretical notions are used to construct a model and the model is fitted to empirical data.…”

Section: Examples Of Research Methodology Topicsmentioning

confidence: 99%

Conceptual issues of research methodology for the behavioural, life and social sciences

Mellenbergh¹,

Adèr²,

Baird³

et al. 2003

Journal of the Royal Statistical Society: Series D (The Statistician)

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Research methodology (RM) must be clearly separated from substantive fields, such as medicine, psychology, education, sociology and economics, and, on the other side, from the philosophy of science and statistics. RM starts from substantive research problems and uses statistical knowledge, but it goes its own way in developing and applying new methods and concepts, all of which are relevant to RM consultants. Examples of topics are given that are typical for RM research and consultancy. Finally, it is argued that RM needs its own research programme and curriculum for RM consultants.

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Section: Examples Of Research Methodology Topicsmentioning

confidence: 99%

Conceptual issues of research methodology for the behavioural, life and social sciences

Mellenbergh¹,

Adèr²,

Baird³

et al. 2003

Journal of the Royal Statistical Society: Series D (The Statistician)

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“…power, NHST), openness/transparency in research, publication bias, etc. goes back decades (Greenwald, 1975;Rosenthal, 1979;Tversky & Kahneman, 1971), if not centuries (see Babbage, 1830 and the seminal text Methodologie by de Groot, 1960de Groot, , 1969 press/ahead of print articles, or are not assigned to a specific volume/issue, 4) are a part of a special issue or are designated to a "special section" within an issue, 5) are not written in the English language, 6) are any one of the following types of articles: a) editorial, b) commentary or opinion-piece, c) systematic review, d) meta-analysis, e) qualitative study, f) psychometric/validation study, g) book review, h) letter/letter-to-the-editor, 7) exclusively employ Bayesian, likelihood, or model-fit (e.g. AIC-based) statistical approaches to statistical inference, or 8) if the full-text of the article is not available.…”

Section: Inclusion and Exclusion Criteria For Articles 40mentioning

confidence: 99%

“…Researchers have put forth numerous hypotheses about the causes of the reproducibility crisis (see Ioannidis, 2005Ioannidis, , 2008Ioannidis, , 2012; none of which completely explain the problem. These include: 1) low statistical power (Button et al, 2013;Cohen, 1992;Sham & Purcell, 2014), 2) impoverished theory (Mischel, 2008;Muthukrishna & Henrich, 2019), 3) reliance on exploratory, rather than confirmatory analyses of data (de Groot, 1969(de Groot, , 1969(de Groot, /2014Wagenmakers, Wetzels, Borsboom, Kievit, & van der Maas, 2011;Wagenmakers et al, 2012), 4) the idiosyncratic nature and flexibility of data analysis (Gelman & Loken, 2013;Silberzahn et al, 2018;Simmons et al, 2011), 5) reliance and misuse of frequentist statistics (Colling & Szűcs, 2018;Dienes, 2016;Meehl, 1976Meehl, , 1978, 6) engagement in Questionable Research Practices (QRPs) 1 (e.g. Fraser, Parker, Nakagawa, Barnett, & Fidler, 2018;Kerr, 1998;Murphy & Aguinis, 2019;Wansink, 2016), 7) publication bias (Ioannidis, Munafò, Fusar-Poli, Nosek, David, 2015;Rosenthal, 1979), and 8) research fraud (e.g.…”

Section: Introduction the Problem: Science In Crisismentioning

confidence: 99%

Appraising Contemporary Social Work Research: Meta-Research on Statistical Reporting, Statistical Power, and Evidential Value

Dunleavy¹

2020

Preprint

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Background. In recent years, the veracity of scientific findings has come under intense scrutinyin what has been called the “replication crisis” (sometimes called the “reproducibility crisis” or“crisis of confidence”). This crisis is marked by the propagation of scientific claims which weresubsequently contested, found to be exaggerated, or deemed false. The causes of this crisis aremany, but include poor research design, inappropriate statistical analysis, and the manipulationof study results. Though it is uncertain if social work is in the midst of a similar crisis, it is notunlikely, given parallels between the field and adjacent disciplines in crisis.Objective. This dissertation aims to articulate these problems, as well as foundational issues instatistical theory, in order to scrutinize statistical practice in social work research. In doing so, itparallels recent work in psychology, neuroscience, medicine, ecology, and other scientificdisciplines, while introducing a new program of meta-research to the social work profession.Method. Five leading social work journals were analyzed across a five-year period (2014-2018).In all 1,906 articles were reviewed, with 310 meeting inclusion criteria. The study was dividedinto three complementary parts. Statistical reporting practices were coded and analyzed in Part 1of the study (n = 310). Using reported sample sizes from these articles, a power survey wasperformed, in Part 2, for small, medium, and large effect sizes (n = 207). A novel statistical tool,the p-curve, was used in Part 3 to evaluate the evidential value of results from one journal(Research on Social Work Practice) and to assess for bias. Results from 39 of the 78 eligiblearticles were included in the analysis. Data and materials are available at: https://osf.io/45z3h/Results. Part 1: Notably, 86.1% of articles reviewed did not report an explicit alpha level. Apower analysis was performed in only 7.4% of articles. Use of p-values was common, beingreported in 96.8% of articles, but only 29% of articles reported them in exact form. Only 36.5%of articles reported confidence intervals; with the 95% coverage rate being the most common(reported in 31.3% of all studies). Effect sizes were explicitly reported in the results section ortables in a little more than half of articles (55.2%). Part 2: The mean statistical power for articleswas 57% for small effects, 88% for medium effects, and 95% for large effects. 61% of studiesdid not have adequate power (.80) to detect a small effect, 19% did not have adequate power todetect a medium effect, and 7% a large effect. A robustness test yielded similar but moreconservative estimates for these findings. Part 3: Both the primary p-curve and robustness testyielded right-skewed curves, indicating evidential value for the included set of results, and noevidence of bias.Conclusion. Overall, these findings provide a snapshot of the status of contemporary social workresearch. The results are preliminary but indicate areas where statistical design and reporting canbe improved in published research. The results of the power survey suggest that the field hasincreased mean statistical power compared to prior decades; though these findings are tentativeand have numerous limitations. The results of the p-curve demonstrate its potential as a tool forinvestigating bias within published research; while suggesting that the results included fromResearch on Social Work Practice have evidential value. In all this study provides a first steptowards a broader and more comprehensive assessment of the field.

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“…Adrianus Dingeman de Groot (1914–2006) was a Dutch methodologist whose ideas about scientific inquiry were inspired in part by Popper. Although his major work “Methodology” (de Groot, 1961/1969) fails to mention Peirce, the ideas of de Groot and Peirce were closely aligned. De Groot’s “empirical cycle,” shown here in Figure 6, provides a systematic overview of the scientific growth of knowledge.…”

Section: Historical Perspectivementioning

confidence: 99%

“…A prominent example of the new appreciation for rigor is the widespread adoption of preregistration , where researchers detail their substantive hypotheses and statistical analysis plan in advance of data collection or data inspection (e.g., Nosek, Ebersole, DeHaven, & Mellor, in press; Nosek & Lindsay, 2018; van ‘t Veer & Giner-Sorolla, 2016). The goal of such preregistration is to allow a sharp distinction between what was preplanned and what is post hoc (e.g., Chambers, 2017; de Groot, 1956/2014, 1969; Peirce, 1883).…”

mentioning

confidence: 99%

The Creativity-Verification Cycle in Psychological Science: New Methods to Combat Old Idols

Wagenmakers

Dutilh

Sarafoglou

2018

Perspect Psychol Sci

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Over the years, researchers in psychological science have documented and investigated a host of powerful cognitive fallacies, including hindsight bias and confirmation bias. Researchers themselves may not be immune to these fallacies and may unwittingly adjust their statistical analysis to produce an outcome that is more pleasant or better in line with prior expectations. To shield researchers from the impact of cognitive fallacies, several methodologists are now advocating preregistration—that is, the creation of a detailed analysis plan before data collection or data analysis. One may argue, however, that preregistration is out of touch with academic reality, hampering creativity and impeding scientific progress. We provide a historical overview to show that the interplay between creativity and verification has shaped theories of scientific inquiry throughout the centuries; in the currently dominant theory, creativity and verification operate in succession and enhance one another’s effectiveness. From this perspective, the use of preregistration to safeguard the verification stage will help rather than hinder the generation of fruitful new ideas.

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Methodology

Cited by 123 publications

References 0 publications

Conceptual issues of research methodology for the behavioural, life and social sciences

Conceptual issues of research methodology for the behavioural, life and social sciences

Appraising Contemporary Social Work Research: Meta-Research on Statistical Reporting, Statistical Power, and Evidential Value

The Creativity-Verification Cycle in Psychological Science: New Methods to Combat Old Idols

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