Quantitative Methodology

Quantitative Methodology

1. Introduction

Given this range of uses of quantitative methods and the fact that many areas of sociological teaching are now also expected to provide training in them, it is essential to have an explicit presentation of the logic of the method. This is what the essay attempts to do.

Quantitative methods are also widely used for social research that is not by any means ‘pure’ in the sense of being exclusively focused on questions that only interest social scientists. For instance, policy research for government bodies is often about practical needs and is not strongly theoretical. Quantitative methods are also used for evaluation research designed to establish causal effects of interventions on the state of society. Program evaluation is a very practical piece of research, and there is no reason why quantitative methods should be confined to research that is abstract and theory-driven.

Quantitative methods have been growing in importance for the past 40 years to the point where they are considered the paradigm for social science research. While statistics are a key tool for quantitative methods, they are not the only tool, and a range of associated methodologies, with a qualitative flavor, are also used. This is not the place to launch into a detailed discussion of the quantitative-qualitative distinction. Suffice it to say that a study can be purely quantitative or weighted at the quantitative/qualitative borderline. At the pure end, it might involve no contact with people at all if data on them are available from archival sources. Data collection can also involve direct observation of social phenomena.

The present essay provides an introduction to the quantitative methodology that can be used by sociologists. The scope and limitation of the methodology are covered in this essay and how it can be used in the context of education. The strengths and weaknesses of the methodology are also discussed, and a comparison is made with other research methodologies wherever necessary.

2. Data Collection

Designing the questionnaire is a delicate matter and requires experience and skill since a poorly designed questionnaire will, at best, result in a waste of resources and, at worst, produce data that is misleading and/or biased. A clear understanding of the topic is required, as well as the ability to transform this understanding into an effective series of questions. The questions should be relevant, clearly and unambiguously stated, and free from any form of bias. This is something of an ideal and fraught with difficulties but should be aimed towards in every aspect. During the course of this, I constantly referred back to the objectives and research question to ensure a link was maintained between every question and the topic at hand.

At the first stage of collecting quantitative data, a questionnaire is deemed most appropriate because of its flexible nature, and data can be collected from a large number of people who are spread over a wide area. A questionnaire is the single most powerful method of collecting primary data on a large scale. It may be administered either personally, by mail, or in a group context.

This stage covers the second requirement of the research, i.e. to select an appropriate method for the data collection. The method of data collection is imperative for the scale of derived results. Data can be taken through either quantitative or qualitative methods. Both of these have different importance according to the requirement of the researcher. A combined method is also another alternative, but it becomes time-consuming because of collecting data through both methods. Owing to the complexity of the subject matter and time limitation, I chose the quantitative method for the data collection.

3. Data Analysis

The decision to go on to quantify the qualitative results into an explanatory framework was based on a realist philosophy, which held the belief that social reality is made up of structures that influence the behavior and thinking of individuals. This belief would hold that the results of this thesis are important to those involved in training and development and will help create an improvement in trainer credibility. It would also move the research findings from theory to action, building the knowledge base in an area that would benefit those within an organizational setting.

The research reported in this thesis took the form of an “analytic survey” (Punch, 1998), utilizing primary data in the form of both qualitative and quantitative results gleaned from a questionnaire administered to company Personal Managers. Qualitative data presented the researcher with the initial need to look for patterns and meaning to understand what was going on in the area under research. However, there was also a need to present some initial quantification of the issue of trainer credibility, which could act to facilitate further research in this area from both the author and other researchers, and to better communicate the findings to those involved in training and development.

In social science research, research strategy and data sources are various. A strategy is an integrated plan of research intended to achieve persistent goals data analysis (failure to plan or plan to fail). The different strategies of dealing with the data available to the researcher have been outlined by Punch (1998) and, on the whole, are designed to move the researcher from data to theory through a process of fragmentation, display, and then reconnection of the social phenomena under scrutiny into an explanatory framework.

4. Results and Findings

This is where you can really explain your findings and go into depth and detail. Always keep the focus on your research, the way it was conducted, and the original aims. Never drift off the topic and start discussing other theories or unrelated topics. Remember the limit is the page space provided, so don’t waffle and make the reader lose interest. Just continue to elaborate upon your results and explain their connections with your original aims.

Elaborate upon your findings.

This is an excellent way to display your findings as it is a simple and clear way to present your results. The reader can just scan read through your results by reading the sub-headings, and this can keep them engaged throughout your results. This may also help the reader understand your results as they may see a sub-heading and then remember some relevant work. This should also display all the results that were found in the analysis, including both positive and negative findings along with any irrelevant results.

Provide relevant findings as sub-headings.

This is where you present how you have conducted your investigation and analyzed the data from the methodology. You should present your research in a logical and clear manner and infer the reader in reading the methodology. This should make the connection between what you were investigating and the ways you gathered and analyzed your findings.

Explain how evidence was gathered and analyzed.

5. Conclusion and Recommendations

We conclude by recapping the experience of pilot testing the questionnaire, both for internal reliability and the scanning process. Thanks to a preliminary assessment and ironing out of difficulties with colleagues, we were able to identify the potential weaknesses and annoyances in the questions. This led to a significant improvement in the internal reliability, as well as utility and hence quality of the data compared with the practice scan data. Many issues would not have been resolved without the pilot testing. Thus, we highly recommend all would-be cognitive experimenters employing imaging to carry out a preliminary assessment with a mock scanning session using the actual scanning hardware. This should be done before final revisions of any custom-written scripts and certainly before acquiring any experimental data. Due to the environmental constraints and the unfamiliarity of non-experimenters with MRI procedures and output data, it is essential to familiarize participants with the scanning process and experimental environment, such that the subject experience is as close as possible to the real thing. This is another reason why pilot testing, in this case a mock session, is essential. Although a rough indicator, we logged the incidence of participant questions and confusions during the mock and real scans and found the decrease markedly in the latter. Finally, the experimental data for the practice trials were analyzed with CDT, in each case producing useful diagnostic information about question and subject accuracy. Due to favorable impressions of the CDT method, this should therefore be a permanent feature in our future experiments.

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