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STATISTICAL MEASURES FOR DATA ANALYSIS

Statistical measures are procedures or techniques used in analyzing data collected in any research work. These procedures are basically methods of handling quantitative information in such a way as to make the information meaningful.

A knowledge of basic statistical measures is important especially for a researcher as it enables you to do two basic things:

  • To analyze, interpret and communicate your findings to others
  • To familiarize yourself with statistical procedures in order to understand and evaluate research studies conducted by others and up-to-date information

You can use statistical procedures as a researcher in two major ways:

  • To describe and summarized your observations
  • To determine how reliably we can infer that observations made from a limited group(sample) will also occur in the unobserved larger group(Population)

The statistical procedures are therefore categorized into descriptive and inferential statistics.

Descriptive statistics refers to the statistical methods that are used to describe and summarize data. They are not used to test hypothesis.

Examples of descriptive statistical methods include measures of central tendency such as mean, median, mode and Standard deviation, correlation analysis e.t.c.

Inferential Statistics refers to those statistical tools that are used in  cases where a sample was drawn from the population and the findings from the sample is generated to the population. In other words, the population parameter can be inferred from the sample. These statistics can be used to test hypothesis.

Examples of inferential statistics commonly used in educational research include; Chi Square, t-test of independent  data(unpaired data), t-test of dependent data (paired data),Analysis of Variance (ANOVA), Analysis of Covariance(ANCOVA),correlation, linear & multiple regression, Mann Whitney U test, Kruskal Wallis Test, Wilkinson. e.t.c.

Some key concepts

Before we look at types of analysis and tools in detail, we need to be familiar with a few concepts first:

•           Population – the whole units of analysis that might be investigated, this could be students, cats, house prices etc

•           Sample – the actual set of units selected for investigation and who participate in the research

•           Variable – characteristics of the units/participants

•           Value – the score/label/value of a variable, not the frequency of occurrence. For example, if age is a characteristic of a participant then the value label would be the actual age, eg. 21, 22, 25, 30, 18, not how many participants are 21, 22, 25, 30, 18.

•           Case/subject – the individual unit/participant of the study/research.

Sampling

Sampling is complex and can be done in many ways dependent on 1) what you want to achieve from your research, 2) practical considerations of who is available to participate!

The type of statistical analysis you do will depend on the sample type you have. Most importantly, you cannot generalize your findings to the population as a whole if you do not have a random sample. You can still undertake some inferential statistical analysis but you should report these as results of your sample, not as applicable to the population at large.

Common sampling approaches include; random sampling, stratified sampling, cluster sampling, convenience sampling and accidental sampling.

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