The purpose of the chapter is to review the role of quantitative methods in corporate research and the methods for analysing quantitative data. The study used a secondary data on quantitative research methods and a survey of published articles on schools, businesses and non- profit organizations. The key findings show that exploratory data can be analyzed using graphs and charts and hypothesis testing can be employed to test statements made. Impacts of one variable on another and the relationships between variables can be explained using correlation and regression analysis. The implications are that the value of a quantitative analysis arises when it is possible to identify features that occur frequently across the many participatory discussions aimed at studying a particular research theme.
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Quantitative data analysis is a powerful research form, emanating in part from the positivist tradition. It is often associated with large scale research, but can also serve smaller scale investigations, with case studies, action research, correlational research and experiments.
In this chapter, the author introduces several common statistics used in social research and explains how they can be used to make sense of the “raw” data gathered in research. Such quantitative data analysis, using numbers to discover and describe patterns in data, is the most elementary use of social statistics. Numerical analysis can be performed using software, for example the Statistical Package for Social Sciences (SPSS), Minitab, or Excel. Software packages apply statistical formulae and carry out computations.
With this in mind, extended outlines of statistical formulae are avoided though we do provide details where considered useful. The primary aim is to explain the concepts that underpin statistical analyses and to do this in as user-friendly a way as possible. Lest the approach should raise purist eyebrows, we provide extended treatments in greater detail, signalled where appropriate by web site references. This chapter begins by identifying some key concepts in numerical analysis (scales of data, parametric and non-parametric data, descriptive and inferential statistics, dependent and independent variables). I then address the concept of statistical significance and finally conclude with a brief outline of some simple statistics.
The use of multiple data collection methods dates back to the earliest social science research. It was, however, Campbell and Fiske’s (1959) study of the validation of psychological traits that brought multiple data collection methods into the spotlight. In their classic study, the multitrait-multimethod matrix was designed to rule out method effects; that is, to allow one to attribute individual variation in scale scores to the personality trait itself rather than to the method used to measure it.
Over time, mixed methods research has gradually gained momentum as a viable alternative research method. Over the past 15 years, at least 10 mixed methods textbooks have been published (Bamberger, 2000; Brewer & Hunter, 1989; Bryman, 1988; Cook & Reichardt, 1979; Creswell, 2002, 2003; Greene & Caracelli, 1989; Newman & Benz, 1998; Reichardt & Rallis, 1994; Tashakkori & Teddlie, 1998). Recently, the Handbook of Mixed Methods in Social and Behavioral Research was published (Tashakkori & Teddlie, 2003). In addition, journals such as Field Methods and Quantity and Quality are devoted to publishing mixed methods research. International online journals (see Forum: Qualitative Social Research at http://www.fiu.edu/~bridges/people.htm) provide easy access, resources, and hands-on experiences for interested researchers. Despite this growth and development, a number of controversial issues and debates have limited the widespread acceptance of mixed methods research.