Foundations of Descriptive and Inferential Statistics

TL;DR

This paper provides an accessible, comprehensive introduction to the core concepts of descriptive and inferential statistics, emphasizing practical application and interdisciplinary perspectives for students and researchers.

Contribution

It offers a technically solid, hyperlinked set of lecture notes covering key statistical topics and practical commands across multiple software packages, promoting active data analysis practice.

Findings

Emphasizes effect sizes for inference

Covers Likert scale operationalization

Provides practical commands for R, SPSS, Excel, OpenOffice

Abstract

These lecture notes were written with the aim to provide an accessible though technically solid introduction to the logic of systematical analyses of statistical data to both undergraduate and postgraduate students, in particular in the Social Sciences, Economics, and the Financial Services. They may also serve as a general reference for the application of quantitative--empirical research methods. In an attempt to encourage the adoption of an interdisciplinary perspective on quantitative problems arising in practice, the notes cover the four broad topics (i) descriptive statistical processing of raw data, (ii) elementary probability theory, (iii) the operationalisation of one-dimensional latent statistical variables according to Likert's widely used scaling approach, and (iv) null hypothesis significance testing within the frequentist approach to probability theory concerning (a) distributional differences of variables between subgroups of a target population, and (b) statistical associations between two variables. The relevance of effect sizes for making inferences is emphasised. These lecture notes are fully hyperlinked, thus providing a direct route to original scientific papers as well as to interesting biographical information. They also list many commands for running statistical functions and data analysis routines in the software packages R, SPSS, EXCEL and OpenOffice. The immediate involvement in actual data analysis practices is strongly recommended.