Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

3 results

  • ANOVA and ANCOVA of Pre- and Post-Test, Ordinal Data

  • Mark L. Davison, Anu R. Sharma
  • Journal:
    Psychometrika / Volume 59 / Issue 4 / December 1994
    Published online by Cambridge University Press:
    01 January 2025, pp. 593-600

  • With random assignment to treatments and standard assumptions, either a one-way ANOVA of post-test scores or a two-way, repeated measures ANOVA of pre- and post-test scores provides a legitimate test of the equal treatment effect null hypothesis for latent variable Θ. In an ANCOVA for pre- and post-test variables X and Y which are ordinal measures of η and Θ, respectively, random assignment and standard assumptions ensure the legitimacy of inferences about the equality of treatment effects on latent variable Θ. Sample estimates of adjusted Y treatment means are ordinal estimators of adjusted post-test means on latent variable Θ.

Statistics for the Social Sciences
  • A General Linear Model Approach
  • 2nd edition
  • Russell T. Warne
  • Published online:
    24 December 2020
    Print publication:
    17 December 2020
  • The second edition of Statistics for the Social Sciences prepares students from a wide range of disciplines to interpret and learn the statistical methods critical to their field of study. By using the General Linear Model (GLM), the author builds a foundation that enables students to see how statistical methods are interrelated enabling them to build on the basic skills. The author makes statistics relevant to students' varying majors by using fascinating real-life examples from the social sciences. Students who use this edition will benefit from clear explanations, warnings against common erroneous beliefs about statistics, and the latest developments in the philosophy, reporting, and practice of statistics in the social sciences. The textbook is packed with helpful pedagogical features including learning goals, guided practice, and reflection questions.

  • 7 - Is This Normal?

  • from Part II - How to Use Statistics
  • Paul Cairns, University of York
  • Book:
    Doing Better Statistics in Human-Computer Interaction
    Published online:
    26 January 2019
    Print publication:
    07 February 2019, pp 86-94

  • Summary

    Many statistical tests that are commonly used rely on the assumption that data are normally distributed. This chapter discusses why normality commonly occurs in statistics but also how, in many practical situations in HCI, it is not safe to assume normality. It also shows how tests for normality are not meaningful or useful. Instead, where normality is in doubt, analysis should be more careful and use suitable alternative tests.