Methodology tutorial - descriptive statistics and scales: Difference between revisions
m (New page: {{Incomplete}} {{under construction}} <pageby nominor="false" comments="false"/> This is part of the methodology tutorial (see its table of contents). == Introduction == This tutori...) |
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This tutorial is a short introduction to simple descriptive statistics for beginners. | This tutorial is a short introduction to simple descriptive statistics for beginners. | ||
<div class="tut_goals"> | |||
; Learning goals | |||
* Be able to distinguish between data types | |||
* Understand simple measures of centrality and dispersion | |||
; Prerequisites | |||
* None | |||
; Moving on | |||
* [[Methodology tutorial - quantitative data analysis]] | |||
* [[Methodology tutorial - exploratory data analysis]] | |||
; Level and target population | |||
* | |||
; Quality | |||
* under construction !! | |||
</div> | |||
== 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. | |||
{| border="1" | |||
! rowspan="1" colspan="1" | | |||
Types of measures | |||
! rowspan="1" colspan="1" | | |||
Description | |||
! rowspan="1" colspan="1" | | |||
Examples | |||
|- | |||
| rowspan="1" colspan="1" | | |||
nominal or category | |||
| rowspan="1" colspan="1" | | |||
enumeration of categories | |||
| rowspan="1" colspan="1" | | |||
male, female | |||
district A, district B, | |||
software widget A, widget B | |||
|- | |||
| rowspan="1" colspan="1" | | |||
ordinal | |||
| rowspan="1" colspan="1" | | |||
ordered scales | |||
| rowspan="1" colspan="1" | | |||
1st, 2nd, 3rd | |||
|- | |||
| rowspan="1" colspan="1" | | |||
interval or quantitative or "scale" (in SPSS) | |||
| rowspan="1" colspan="1" | | |||
measure with an interval | |||
| rowspan="1" colspan="1" | | |||
1, 10, 5, 6 (on a scale from 1-10) | |||
180cm, 160cm, 170cm | |||
|} | |||
=== Descriptive statistics === | |||
* Descriptive statistics are not very interesting in most cases (unless they are used to compare different cases in comparative systems designs) | |||
* Therefore, do not fill up pages of your thesis with tons of Excel diagrams !! | |||
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 | |||
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: tutorials]] |
Revision as of 20:35, 5 March 2009
This article or section is currently under construction
In principle, someone is working on it and there should be a better version in a not so distant future.
If you want to modify this page, please discuss it with the person working on it (see the "history")
<pageby nominor="false" comments="false"/>
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 |
Descriptive statistics
- Descriptive statistics are not very interesting in most cases (unless they are used to compare different cases in comparative systems designs)
- Therefore, do not fill up pages of your thesis with tons of Excel diagrams !!
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
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...