Quantitative Methods Essay

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Quantitative methods in sociology pertain to research methods of collecting and analyzing data which will eventually lead to statistical analyses. These analyses, for the most part, use probability statistics to make generalizations applicable to the larger population using data gathered from a representative sample. This idea of generalization is the key concept driving quantitative research methods. Research using quantitative methods broadly includes four major parts: sampling, measurement, design and analysis.

Sampling is the process of selecting units from a population of interest so that by studying the sample we may be able to generalize the findings to the population. For example if your population of interest is female students at your university, then you first need to get a list of all female students at your university. This list is the sampling frame from which you will select the participants for your study. You can select participants for your study using a variety of sampling methods. For example, you can write the names on little pieces of paper and use a lottery method to pick out a sample of 100 participants from the sampling frame for your study. Using a random sample (such as the one in the example), ensures that you can generalize back to the population of interest. Other examples of sampling methods include stratified sampling, cluster sampling, and convenience sampling.

Measurement is the process by which you observe and record information collected as part of the research process. First in this process one has to decide how the information is going to be collected (questionnaire survey or interview) and at what level of measurement. Questionnaires either in the form or surveys or interviews are the most common methods of collecting data for quantitative research methods. The questionnaires can be mailed to individuals (example census) or these days they are more commonly collected web based forms (example: you can create an online survey using www.surveymoneky.com or similar services).

Whatever the method, the questionnaires are highly structured such that the same information is collected from each individual. Within the questionnaire, depending on the type of question different levels of measurement are used. The four levels of measurement are nominal, ordinal, interval and ratio. Nominal level measurement collects the ”name” of the attribute (example: names of people, names of countries or cities). In ordinal level of measurement the attributes can be ordered (example: your rating of iTunes songs on a range of 0 to 5 stars, where more stars indicate a higher rating; but you are not able to say that the distance between 0 to 1 star is the same as the distance between 4 to 5 star meaning that the interval between values cannot be interpreted in an ordinal measure). In an interval level of measurement the distance between two attributes has meaning (example: when you measure temperature (in -Fahrenheit), the distance from 20 to 30 is the same as distance from 50 to 60. The interval between values is interpretable, but the ”zero” value of temperature is not defined and therefore 60F is not twice as hot as 30F). In the ratio level of measurement there is always an absolute zero that is meaningful (example: an income of $50,000 is twice as much as $25,000 and an income of $0 means a person has no income). Using these methods, one collects data about a large number of individual variables from a sample of people. This type of quantitative data can then be tested for reliability (whether the same questionnaire used by a different person would give the same results?), validity (do you have evidence that what you did in the study caused what you observed to happen?), and representativeness (how generalizable is this study?) using statistical techniques.

Design is the process by which all parts of the research are pieced together to answer the specific research question. Designs could be experimental, quasi-experimental or cross-sectional in nature depending on the specific research question or hypotheses.

Regardless of the type of design used, the data collected needs to be analyzed using statistical methods to generate the findings of the study. There are numerous statistics computer programs such as SPSS (or PASW now), SAS, Stata etc, using which one can do simple or complex statistical analyses. The data that has been collected using the questionnaire need to be coded, entered into the program and appropriate statistical analyses conducted. Simple descriptive analyses such as mean, median, mode, variance and standard deviation provide basic information about the sample/ population. Further, depending on the design of the study and the specific hypothesis, inferential analyses such as t-tests (to compare two groups), ANOVA (to compare multiple groups), Pearson’s correlation (to compare the degree of relationship between two variables) etc. can be conducted. If the results of the analysis are statistically significant above a certain threshold, then the results of the research are not due to chance and the hypothesis is proven.


  1. Black, T. R. (1999). Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics. Sage, London.
  2. Field, A. (2009) Discovering Statistics Using SPSS. Sage, London.

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