Analysis of research data
In the "wheel" of the research process (see link), analyzing the research data comes as stage number four following the data collection stage. Depending upon the type of data collected, an appropriate technique of analysis is used. The first two lecture (numbers 9 and 10) focus on quantitative analysis of the data, while the third lecture (number 11) focuses on qualitative analysis of data.
Descriptive Statistics
Descriptive statistics is used to summarize data and make sense out of the raw data collected during the research. Since the data usually represents a sample, then the descriptive statistics is a quantitative description of the sample.
The level of measurement of the data affects the type of descriptive statistics. Nominal and ordinal type data (often termed together as categorical type data) will differ in the analysis from interval and ratio type data (often termed together as continuous type data).
Descriptive statistics for categorical data
Contingency tables (or frequency tables) are used to tabulate categorical data. A contingency table shows a matrix or table between independent variables at the top row versus a dependent variable on the left column, with the cells indicating the frequency of occurrence of possible combination of levels. (check SPSS for examples)
Descriptive statistics for continuous data
The central tendency and variability of the data are the two aspects of descriptive statistics used for continuous type data.
Measures of central tendency "refers to a number (statistic) that best characterizes the group as a whole" (Sommer & Sommer, 1997, pp.250). It is generally refered to as the average. The three types of averages are:
The MEAN (M): is the arithmetic average (sum of all score divided by the number of cases)
The MEDIAN (Mdn): is the midpoint of a distribution of data. Half the scores fall above and half below the median.
The MODE: is the single score that occurs most often in a distribution of data.
Measures of variability "refers to the spread or dispersion among a set of scores" (Sommer et. al. 1997, pp. 251). The different statistics used are the following:
The RANGE: is the difference between the highest and lowest score.
The STANDARD DEVIATION (sd or s): It is related to the variability of the data and the way it is clustered around the mean (median and mode are not used here). The larger the standard deviation the wider the data is spread from the mean. The smaller the standard deviation the closer the data are grouped around the mean.
The VARIANCE: is the square of the standard deviation.
Graphical representation of data
Several graphical techniques exist for summarizing the data. These graphs can work alone or in conjunction with the statistics described above. Some of the well known types of graphs are the bar graphs, the line graphs, and the pie graphs.
SPSS
You will need to practice the statistical software package SPSS and experiment with the descriptive statistics.
The following items are topics for discussion during this lecture. Students should prepare their thoughts and ideas around these topics.
Why do descriptive statistics?
What are the descriptive statistics for categorical measures?
What are the descriptive statistics for continuous measures?
Graphical representation of descriptive statistics
Introduction to SPSS
Sommer, B. & Sommer, R. (2002). A Practical Guide to Behavioral Research: Tools and Techniques, 5th ed. Oxford: Oxford University Press. (Chapter 18, pp.245-260, Descriptive Statistics).
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This page last revised: 09/05/03
