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Update Longitudinal.md #72

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Expand Up @@ -26,6 +26,7 @@ For cross-sectional analysis, consider the **Exploratory Data Science Standard**
- [ ] uses at least two data collection waves
- [ ] subjects (humans or artifacts) are identifiable between waves
- [ ] justifies the data analysis strategy<sup>[3](#myfootnote3)</sup>
- [ ] reports any contextual changes for participants (units) as the study progressed
- [ ] the data analysis strategy is appropriate for the interdependent nature of the data<sup>[2](#myfootnote2)</sup>
- [ ] discusses the critical alpha levels or justifies Bayesian priors<sup>[4](#myfootnote4)</sup>
- [ ] justifies sample size (e.g. using power analysis)<sup>[5](#myfootnote5)</sup>
Expand All @@ -51,9 +52,12 @@ For cross-sectional analysis, consider the **Exploratory Data Science Standard**
- [ ] either builds new theory or tests existing theory
- [ ] investigates causality using the longitudinal nature of the data to establish precedence and statistically controlling for third-variable explanations
- [ ] discusses potential confounding factors (for inferential analyses) that cannot be statistically controlled
- [ ] reports the years in which each wave of the data collection occurred
- [ ] discusses data (in)consistency across waves (e.g. test-retest reliability)
- [ ] specifies independent variables and dependent variables at each wave of data collection.
- [ ] examines differences in distributions between waves (and uses an appropriate data analysis strategy)
- [ ] describes the cost of gathering data and any incentives used
- [ ] provides information on where any portions of the data have been previously published and the degree of overlap with the current report
- [ ] addresses survivorship bias<sup>[8](#myfootnote8)</sup>

</checklist>
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