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Related Experiment Videos

Graphing within-subjects confidence intervals using SPSS and S-Plus.

Daniel B Wright1

  • 1Psychology Department, University of Sussex, Brighton, England. danw@sussex.ac.uk

Behavior Research Methods
|June 8, 2007
PubMed
Summary
This summary is machine-generated.

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This study presents methods for calculating within-subjects confidence intervals in SPSS and S-Plus. These procedures enhance statistical analysis by offering bias-corrected and bootstrap confidence intervals for researchers.

Area of Science:

  • Psychology
  • Statistics
  • Data Analysis

Background:

  • Within-subjects confidence intervals are crucial for accurate data interpretation in psychological research.
  • Existing methods for calculating these intervals are becoming more prevalent.
  • Previous work by Loftus and Masson (1994) laid the groundwork for these calculations.

Purpose of the Study:

  • To provide practical procedures for calculating within-subjects confidence intervals using SPSS and S-Plus statistical software.
  • To offer users the ability to compute standard, bias-corrected, and bootstrap confidence intervals.
  • To facilitate the adoption and application of within-subjects confidence intervals in research.

Main Methods:

  • Development of code for calculating within-subjects confidence intervals in SPSS.

Related Experiment Videos

  • Implementation of procedures in S-Plus, including options for bootstrap and bias-corrected intervals.
  • Availability of an R version for broader accessibility.

    • Main Results:

    • Functional code for SPSS and S-Plus is presented, enabling the calculation of within-subjects confidence intervals.
    • The S-Plus procedure offers advanced options like bias-corrected and bootstrap intervals.
    • The provided code is adaptable to specific user requirements.

    Conclusions:

    • The presented procedures simplify the calculation of within-subjects confidence intervals for researchers.
    • The availability of these tools in common statistical software (SPSS, S-Plus, R) promotes their wider use.
    • Researchers can now more readily employ robust methods for confidence interval estimation in within-subjects designs.