| 1. |
- Schweinsberg, Martin, et al.
(författare)
-
Same data, different conclusions : Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis
- 2021
-
Ingår i: Organizational Behavior and Human Decision Processes. - : Elsevier BV. - 0749-5978 .- 1095-9920. ; 165, s. 228-249
-
Tidskriftsartikel (refereegranskat)abstract
- In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists' gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for orga-nizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed.
|
|
| 3. |
- Stoffel, Martin A., et al.
(författare)
-
inbreedR : an R package for the analysis of inbreeding based on genetic markers
- 2016
-
Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 7:11, s. 1331-1339
-
Tidskriftsartikel (refereegranskat)abstract
- Heterozygosity-fitness correlations (HFCs) have been widely used to explore the impact of inbreeding on individual fitness. Initially, most studies used small panels of microsatellites, but more recently with the advent of next-generation sequencing, large SNP datasets are becoming increasingly available and these provide greater power and precision to quantify the impact of inbreeding on fitness. Despite the popularity of HFC studies, effect sizes tend to be rather small. One reason for this may be low variation in inbreeding levels among individuals. Using genetic markers, it is possible to measure variance in inbreeding through the strength of correlation in heterozygosity across marker loci, termed identity disequilibrium (ID). ID can be quantified using the measure g2, which is also a central parameter in HFC theory that can be used within a wider framework to estimate the direct impact of inbreeding on both marker heterozygosity and fitness. However, no software exists to calculate g2 for large SNP datasets nor to implement this framework. inbreedR is an R package that provides functions to calculate g2 based on microsatellite and SNP markers with associated P-values and confidence intervals. Within the framework of HFC theory, inbreedR also estimates the impact of inbreeding on marker heterozygosity and fitness. Finally, inbreedR implements user-friendly simulations to explore the precision and magnitude of estimates based on different numbers of genetic markers. We hope this package will facilitate good practice in the analysis of HFCs and help to deepen our understanding of inbreeding effects in natural populations.
|
|
