Analyzing qualitative data is an inductive process, involving the reduction of information that has been collected by organizing it into important themes and patterns.
The reduction of qualitative data is typically accomplished through the development of a coding scheme, which is used to group data that provide similar types of information.
The process of coding narrative data often necessitates rereading your data numerous times.
Once all narrative data have been coded, the main features of each of the categories must be described.
The final step of analyzing qualitative data involves the interpretation of the data that have been coded into categories.
Reflection throughout the process of inductive analysis is an essential component to remaining objective and open-minded while gaining a better understanding of your data.
Numerous computer software programs can aid the researcher with the organization and categorization of narrative data.
Analysis of quantitative data is a deductive process, using descriptive or inferential statistics.
Descriptive statistics are relatively simple mathematical procedures used to simplify, summarize, and organize large amounts of numerical data.
Three categories of descriptive statistics include measures of central tendency, dispersion, and relationship.
Three measures of central tendency, which describe what is typical about a group, are the mean, the median, and the mode.
Two measures of dispersion, which indicate how much spread or diversity exists within a group of scores, are the range and the standard deviation.
A correlation coefficient is used to measure the degree of relationship that exists between two variables.
Data can also be “described” visually through the use of frequency distribution tables and such graphs as histograms, bar charts, and pie charts.
Inferential statistics are used to determine how likely a given statistical result is for an entire population, based on data collected from a smaller sample from that population.
The most common types of inferential statistical tests are the independent-measures t test, the repeated-measures t test, analysis of variance, and the chi-square test.
An independent-measures t test is appropriate for designs where two groups are compared on a common dependent variable.
A repeated-measures t test is appropriate for designs involving two measures (such as a pretest and a posttest) on the same group.
Analysis of variance (or ANOVA) is appropriate for designs where more than two groups are being compared on a common dependent variable.
Chi-square analysis is used when data exist as frequency counts within categories.
Inferential statistics help the researcher determine statistical significance, which indicates a true difference between groups being compared, as opposed to differences due only to chance.
Statistical significance is determined by comparing the obtained p-value to the preestablished α-level, usually 0.05 in educational research studies.
When the p-value is less than the α-level, the results are said to be statistically significant.
There are numerous computer software programs available to assist in the analysis of numerical data.
