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  • Quantitative Analysis with JASP open-source software: Foundational Methods by Christopher P. Halter book cover
  • Quantitative Analysis with JASP open-source software: Foundational Methods by Christopher P. Halter book cover
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Quantitative Analysis with JASP open-source software: Foundational Methods

by Christopher P. Halter

A practical introduction to quantitative analysis in JASP

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AlbakiReads Editorial

A practical introduction to quantitative analysis in JASP

Organized around common methods used in social science and education research, this guide connects three tasks: choosing a statistical test for a dataset, applying it in JASP, and interpreting the resulting table. Its coverage includes chi-square tests of independence using contingency tables, independent, paired, and one-sample t-tests, one-way ANOVA, correlation, and simple linear regression. The description also notes that the guide discusses limitations of these tests, making interpretation part of the subject alongside software use. The emphasis is on foundational statistical output and methods rather than an exhaustive survey of quantitative analysis.

Good fit for readers who enjoy

  • Novice social science researchers learning to work with quantitative data
  • Education researchers seeking guidance on selecting and interpreting common statistical tests
  • JASP users who want an introduction to interpreting statistical output

Themes

Quantitative analysisStatistical test selection and interpretationJASP softwareFoundational research methods

Based on publisher information and book metadata.

Book overview

The JASP Guide's purpose is to assist the novice social science and education researcher in interpreting statistical output data using the JASP Statistical Analysis application. Or, I could say that this guide helps you understand quantitative analysis without the tears. Through the examples and guidance, you will be able to select the statistical test that is appropriate for your data, apply the inferential test to your data, and interpret a statistical test's results table.

The Guide goes into the uses of some of the most common statistical tests and discusses some of the limitations of those tests, i.e. Chi-square using Contingency Tables (Test of Independence), t-Test (Independent, Paired, and One Sample), One-Way ANOVA, Correlation, and Simple Linear Regression.
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