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2 results

  • 10 - Data Processing

  • from Part III - Expanding the Multiverse
  • Cristobal Young, Cornell University, New York, Erin Cumberworth, Cornell University, New York
  • Book:
    Multiverse Analysis
    Published online:
    28 February 2025
    Print publication:
    06 March 2025, pp 154-176

  • Summary

    Raw data require a great deal of cleaning, coding, and categorizing of observations. Vague standards for this data work can make it troublingly ad hoc, with much opportunity and temptation to influence the final results. Preprocessing rules and assumptions are not often seen as part of the model, but they can influence the result just as much as control variables or functional form assumptions. In this chapter, we discuss the main data processing decisions that analysts often face and how they can affect the results: coding and classifying of variables, processing anomalous and outlier observations, and the use of sample weights.

  • How to use replicate weights in health survey analysis using the National Nutrition and Physical Activity Survey as an example

  • Carole L Birrell, David G Steel, Marijka J Batterham, Ankur Arya
  • Journal:
    Public Health Nutrition / Volume 22 / Issue 18 / December 2019
    Published online by Cambridge University Press:
    19 August 2019, pp. 3315-3326
    • Article
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  • Objective:

    To conduct nutrition-related analyses on large-scale health surveys, two aspects of the survey must be incorporated into the analysis: the sampling weights and the sample design; a practice which is not always observed. The present paper compares three analyses: (1) unweighted; (2) weighted but not accounting for the complex sample design; and (3) weighted and accounting for the complex design using replicate weights.

    Design:

    Descriptive statistics are computed and a logistic regression investigation of being overweight/obese is conducted using Stata.

    Setting:

    Cross-sectional health survey with complex sample design where replicate weights are supplied rather than the variables containing sample design information.

    Participants:

    Responding adults from the National Nutrition and Physical Activity Survey (NNPAS) part of the Australian Health Survey (2011–2013).

    Results:

    Unweighted analysis produces biased estimates and incorrect estimates of se. Adjusting for the sampling weights gives unbiased estimates but incorrect se estimates. Incorporating both the sampling weights and the sample design results in unbiased estimates and the correct se estimates. This can affect interpretation; for example, the incorrect estimate of the OR for being a current smoker in the unweighted analysis was 1·20 (95 % CI 1·06, 1·37), t= 2·89, P = 0·004, suggesting a statistically significant relationship with being overweight/obese. When the sampling weights and complex sample design are correctly incorporated, the results are no longer statistically significant: OR = 1·06 (95 % CI 0·89, 1·27), t = 0·71, P = 0·480.

    Conclusions:

    Correct incorporation of the sampling weights and sample design is crucial for valid inference from survey data.