DBAX 9103 Week 4 Data Preparation Note Example

What Happened to the Data Before Anyone Analyzed It: A Data Preparation Note for a Caregiver Retention Study, From 612 Hires to 531 Analyzed Cases, With Every Exclusion, Recode and Duplicate Accounted For

Student Name

Walden University

DBAX 9103: Business Research Project Stage 3

Instructor Name

Month Day, Year

What Happened to the Data Before Anyone Analyzed It: A Data Preparation Note for a Caregiver Retention Study, From 612 Hires to 531 Analyzed Cases, With Every Exclusion, Recode and Duplicate Accounted For

Why Preparation Must Be Written Down

Most of what happens to data before analysis is invisible in a finished chapter unless someone writes it down. Exclusions, recodes, merges and the handling of duplicates can each change a result, and a reader who cannot see them cannot judge it. Steegen et al. (2016) showed that reasonable alternative preparation choices can produce a range of results from the same raw data, and recommended making those choices visible. This note records every preparation step in the order performed for the agency's caregiver retention project, following the advice of Appelbaum et al. (2018) that reports show how many cases entered and left at each stage.

From Hires to Analyzed Cases

Table 1

Case Flow

The 576 cases are used for a sensitivity analysis with the original 30-day window; the 531 cases are used for all main analyses.

Check-In Log Preparation

The raw check-in export contained 2,946 entries for the 576 caregivers. Three steps changed it. First, 37 exact duplicates, created when supervisors saved a note twice, were removed. Second, 211 entries were reclassified as scheduling-only contacts and excluded from the check-in count, following the coding rule described in the procedure account. Third, entries dated before the hire date, 6 in all, were traced to data entry errors in the year field and corrected after checking with the scheduling office. After these steps, 2,698 check-ins remained for the 576 caregivers, of which 870 fell within the first 14 days for the 531 caregivers in the main analysis.

Onboarding Coding

Onboarding completion was coded 1 if a caregiver completed all six modules within 14 days of hire and 0 otherwise. Of the 531 caregivers, 384 met this standard and 147 did not. The three caregivers whose dates came from paper logs were coded from those dates.

Outcome Coding

A caregiver was coded as retained if no separation date fell within 90 days of hire. Of 531 caregivers, 369 were retained, a retention rate of 69.5 percent.

Control Variables

Hourly pay was taken as the starting rate. Prior caregiving experience was coded from a yes or no item on the application. Average travel miles between visits during the first 14 days was log-transformed after inspection showed a long right tail, with a handful of caregivers serving rural clients.

Descriptive Summary After Preparation

For the 531 caregivers in the main analysis, check-ins during the first 14 days ranged from 0 to 6, with a mean of 1.64 and a median of 1. Eighty-seven caregivers received no check-in in their first two weeks. Starting pay ranged from $14.50 to $17.25 an hour. Just under half, 251, reported prior caregiving experience. These figures are reported in the first table of the results chapter so readers can see the distribution of each variable before any model is shown.

Files and Versions

Each preparation step was carried out in a script rather than by hand, and the script was run from the raw exports each time, so any step can be repeated or changed and the analyzed file rebuilt. The raw exports, the script and the final file are stored with the date of each version, and the chair has a copy.

Checks Performed

After preparation I compared the analyzed data with the agency's own reports. The agency's human resources dashboard showed 90-day retention of 68 percent for the same period, close to the study's 69.5 percent once early leavers and rehires are accounted for. Newman (2014) recommends checking prepared data against independent sources where possible; this comparison suggested no large coding error.

What Remains Uncertain

The coding of check-ins depends on note text, which some supervisors wrote in detail and others in two words. Short notes were harder to classify, and a different coder might have classified some differently. The agreement check on 200 entries, 93 percent before discussion, suggests the rule was applied consistently, but it cannot remove the dependence on supervisors' writing habits. A sensitivity run that dropped the 41 caregivers whose supervisors wrote the shortest notes left the direction of the check-in result unchanged, which is some reassurance, though not proof.

References

Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report. American Psychologist, 73(1), 3-25. https://doi.org/10.1037/amp0000191

Newman, D. A. (2014). Missing data: Five practical guidelines. Organizational Research Methods, 17(4), 372-411. https://doi.org/10.1177/1094428114548590

Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702-712. https://doi.org/10.1177/1745691616658637