Methodology tutorial - descriptive statistics and scales: Difference between revisions

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=== Descriptive statistics ===
In quantitative research designs, it is not very interesting to present descriptive statistics. But they play an important role in early stages of data analysis, e.g. you can check data distributions and make more informed decisions about data analysis techniques. Simple data distributions are most often uninteresting, you should aim to '''explain''' these...


* Descriptive statistics are not very interesting in most cases (unless they are used to compare different cases in comparative systems designs)
On the other hand, descriptive statistics are often used to compare different cases in comparative systems designs or they are used to summarize qualitative data in more qualitative studies.
* Therefore, do not fill up pages of your thesis with tons of Excel diagrams !!
 
In any case, avoid filling up pages of your thesis with tons of Excel diagrams. !!
 
== Descriptive statistics ===


Some popular summary statistics for interval variables
Some popular summary statistics for interval variables
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* High and Low value: extremes a both end
* High and Low value: extremes a both end
* Quartiles: same thing as median for 1/4 intervals
* Quartiles: same thing as median for 1/4 intervals
In most cases, you simply should not bother trying to include descriptive statistics in a thesis work or a conference paper. In particular avoid cakes. Simple data distributions are most often uninteresting, you should aim to '''explain''' these...




[[Category: research methodologies]]
[[Category: research methodologies]]
[[Category: tutorials]]
[[Category: tutorials]]

Revision as of 20:48, 5 March 2009

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In principle, someone is working on it and there should be a better version in a not so distant future.
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This is part of the methodology tutorial (see its table of contents).

Introduction

This tutorial is a short introduction to simple descriptive statistics for beginners.

Learning goals
  • Be able to distinguish between data types
  • Understand simple measures of centrality and dispersion
Prerequisites
  • None
Moving on
Level and target population
Quality
  • under construction !!

Scales and "data assumptions"

Types of quantitative measures (scales)

Quantitative data come in different types or forms . Depending on the data type you can or cannot do certain kinds of analysis. There exist three basic data types and the literature uses various names for these. E.g.

Types of measures

Description

Examples

nominal or category

enumeration of categories

male, female

district A, district B,

software widget A, widget B

ordinal

ordered scales

1st, 2nd, 3rd

interval or quantitative or "scale" (in SPSS)

measure with an interval

1, 10, 5, 6 (on a scale from 1-10)

180cm, 160cm, 170cm

In quantitative research designs, it is not very interesting to present descriptive statistics. But they play an important role in early stages of data analysis, e.g. you can check data distributions and make more informed decisions about data analysis techniques. Simple data distributions are most often uninteresting, you should aim to explain these...

On the other hand, descriptive statistics are often used to compare different cases in comparative systems designs or they are used to summarize qualitative data in more qualitative studies.

In any case, avoid filling up pages of your thesis with tons of Excel diagrams. !!

Descriptive statistics =

Some popular summary statistics for interval variables

  • Mean
  • Median: the data point that is in the middle of "low" and "high" values
  • Standard deviation: the mean deviation from the mean, i.e. how far a typical data point

is away from the mean.

  • High and Low value: extremes a both end
  • Quartiles: same thing as median for 1/4 intervals