Quantitative research is a field of inquiry that emphasizes numbers, is often deductive in nature, concentrates on hypotheses testing, and aims to uncover causal connections between different phenomena as well as to make generalizations.
Quantitative research is often associated with a positivist paradigm. Positivists tend to see the social world as existing objectively and independently from the researcher. According to this paradigm, research should be conducted in a detached, impersonal way. Quantitative methods usually focus on testing theories as opposed to the qualitative approach, which is often more concerned with theory generation.
Main methods of data collection in quantitative research include experiments, surveys, structured interviews, structured observation, and various sources of secondary data. Experiments are characterized by an artificially created setting and an opportunity to control the experiment conditions. Two groups are normally used in experiments—a test group and a control group. Cases are assigned to the groups randomly. Although very useful and most similar to natural sciences in methodology, experiments have only limited applicability in business research and social sciences in general because of various ethical and practical issues. There also exist so-called quasi-experiments in which the groups emerge in a natural way rather than artificially, and the researcher has no power in assigning participants or cases to the groups.
Surveys are among the most popular methods of data collection in business research. They are conducted by mail or via the telephone; lately, Web-based and email surveys are also becoming increasingly popular. A survey contains a predefined list of answers, so it is in effect a multiple-choice questionnaire. The questions embrace several interconnected topics, and the aim is usually to test multiple hypotheses. The answers are then coded, entered into statistical software, and subsequently treated as quantitative data.
Structured interviews are very similar to surveys. Just like surveys, they contain a predefined set of answers. What is different is that a researcher asks the questions rather than gives out questionnaires for respondents to fill in, and has an opportunity to clarify any issues if necessary.
Structured observation (also sometimes referred to as systematic observation) is a technique that emphasizes documenting observed behavior under explicitly formulated rules. This approach differs significantly from unstructured observation, which is common for qualitative research. Rules in structured observation help the researcher to pay attention to and record exactly those aspects of participants’ behavior that are relevant for answering the research questions. The resulting data can then be coded in a way similar to that of the survey and analyzed statistically.
Content analysis is an approach to the document analysis that aims to quantify content in terms of predefined categories. Words or phrases can be chosen as the units of analysis. This approach is often used to determine the amount of attention paid to a particular topic in news or other media.
Secondary data are data that have been collected by someone other than a researcher conducting the study—often they come from government or statistical bodies, the media, or other researchers. Sometimes secondary data are used in combination with primary data within the same research project.
Main methods of quantitative data analysis include univariate, bivariate, and multivariate statistics. In univariate methods, a single variable at a time is analyzed. Frequency tables display the number of observations and the percentage belonging to each category. Bar charts and pie charts are the most popular methods for visualizing frequency tables. Common methods in univariate data analysis include measures of the central tendency (arithmetic mean, median, and mode) that show a typical value for a given distribution; measures of dispersion (such as range, variance, and standard deviation) characterize variation in a distribution of values.
Bivariate analysis explores two variables at a time to establish if they are related. Contingency tables are similar to frequency tables, but they permit analysis of two variables simultaneously. Pearson’s correlation coefficient is an important method of bivariate analysis. It indicates the strength and the direction of a relationship between two variables. The coefficient lies between 0 and 1, and the closer it is to 1, the stronger the relationship is. Correlation coefficient does not say anything about causality.
Multivariate analysis refers to statistical techniques that simultaneously analyze more than two variables. Some multivariate techniques are just extensions of univariate (such as distributions) and bivariate methods (such as correlation, analysis of variance, regression). Other techniques, such as factor analysis, are uniquely designed to deal with multivariate data.
- Bryman and E. Bell, Business Research Methods (Oxford University Press, 2007);
- John W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (Sage, 2009);
- Hair et al., Multivariate Data Analysis (Pearson Prentice Hall, 2006);
- Tharenou et al., Management Research Methods (Cambridge University Press, 2007);
- Donald J. Treiman, Deirdre D. Johnston, and Thomas J. Grites, Quantitative Data Analysis: Doing Social Research to Test Ideas (Jossey-Bass, 2008).
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