After having looked at quantitative data collection, Grinnell, Williams and Unrau move into a critical and important step in social research which, in this case, involves analyzing quantitative data covered in chapter 15. Quantitative data analysis is the process of making sense out of numbers in order to allow meaningful interpretation.
This process involves four key steps which include data organization, doing calculations, interpreting information and explaining limitations. The authors break down each step of analyzing quantitative data, through explaining what quantitative data entails, definition of terminologies in quantitative data analysis and expounding on the steps involved in quantitative research. The authors also show how the analysis of quantitative data is significant by giving various examples of data sets to illustrate the process of data analysis.
The authors begin by stating that quantitative data is the type of data that measure a quantity or an amount. The process of measuring quantities or amounts involves using specific rules to assign labels or numerals to the attributes of observations, events or objects. There are various levels of measurement in the analysis of quantitative data.
These levels, according to the authors, determine degree to which characteristics of a data set can be modeled mathematically. Grinnell et al list four measurement levels as nominal, ordinal, interval and ratio. The nominal classifies variables by assigning names as compared to the ordinal which classifies variables by rank. The interval has an arbitrarily chosen zero point while the ratio has a non-arbitrary, fixed zero point.
After defining the levels of measurement with illustrations, the authors examine the three main measures of central tendency which are the mean, the median and the mode. The mean is the average score in a data set. The median is the middle value of a specific data set arranged in order of magnitude. The mode is the most frequent score in a given data set. In addition the authors illustrate how these measures of central tendency are useful in the analysis of quantitative data.
Quantitative data analysis also involves the use of variables in measuring data. A variable, according to the authors, is a concept with attributes that can take on different values. Variables are classified according to their attributes and the type of data set to be analyzed. The relationship between the variables is then determined using the measures of variability. There are two main types of measures of variability that are common in quantitative data analysis.
These methods are variance and standard deviation. The authors demonstrate how to derive these measures of variability and their role in the analysis of quantitative data. The analysis of quantitative data includes data coding and analysis through the use of t-tests and computer applications such as the Statistical Package for Social Sciences (SPSS) and Analysis of Variance (ANOVA). The t-tests involve assessing whether the means of two groups of data sets are statistically different from one another.
Finally, the process of analyzing quantitative data ends by interpreting and discussing of information. During interpretation meaning is attached to data by judging the results fairly and carefully. This process helps in identifying the emerging lessons which are important in discussing the limitations of the data analyzed. In conclusion, the process of analyzing quantitative data helps a social researcher to carefully organize data, do calculations, interpret information and discuss the results.
References
Grinnell, R.M., Williams, M., & Unrau, Y. A. (2012). Research Methods for Social Workers. An Introduction (9th ed.) Oak Highlands, Pair Bond Publications