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Data Analysis
Introduction
Research Process
Qualitative Analysis
Quantitative Analysis
What It Is
Issues of Analysis
Types of Data
Statistical Hypothesis Testing
The p-value
Statistical Power
Validity and Reliability
Descriptive or Summary Statistics
Measures of Central Tendency
Measures of Dispersion
Inferential Statistics
Chi-Squared (Difference)
Mann-Whitney (Difference)
Wilcoxon (Difference)
Independent T-Test (Difference)
Paired T-Test (Difference)
Chi-Squared (Association)
Spearman (Association)
Pearson (Association)
Further Reading
Critical Appraisal
Further Resources
Glossary
  Quantitative Analysis - Issues of Analysis

Types of data

Think about any collected data that you have experience of; for example, weight, sex, ethnicity, job grade, and consider their different attributes. These variables can be described as categorical or quantitative.

 

Statistical hypothesis testing

Null and alternative hypotheses
The formal statistical procedure for performing a hypothesis test is to state two hypotheses and to use an appropriate statistical test to reject one of the hypotheses and therefore accept (or fail to reject) the other.

One- and two-tailed tests
Also known as one- and two-sided tests.

 

The p-value (level of significance)

All statistical tests produce a p-value and this is equal to the probability of obtaining the observed difference, or one more extreme, if the null hypothesis is true. To put it another way - if the null hypothesis is true, the p-value is the probability of obtaining a difference at least as large as that observed due to sampling variation.

 

Statistical power

The use of a significance level of 5% controls the probability of erroneously rejecting the null hypothesis when it is, in fact, true. Rejecting the null hypothesis when it is true is called a Type I error. However, there is another error that can be made - failing to reject the null hypothesis when it is, in fact, not true. This is called a Type II error.

 

Validity and Reliability

Validity and Reliability are the key characteristics in quantitative research that reflect quality and rigour in design. A well written research paper will indicate how validity and reliability have been assessed.
There are similar issues within qualitative research, as per Lincoln & Guba (1985)

Validity
refers to the accuracy and truth of the data and findings that are produced. It refers to the concepts that are being investigated; the people or objects that are being studied; the methods by which data are collected; and the findings that are produced. There are several different types of validity which all contribute to the overall credibility of the research.

Reliability
is concerned with the consistency and dependability of a measuring instrument, i.e. it is an indication of the degree to which it gives the same answers over time, across similar groups and irrespective of who administers it. A reliable measuring instrument will always give the same result on different occasions assuming that what is being measured has not changed during the intervening period