Statistical Techniques, Sample, and Data Collection - Research Paper Example

STATISTICAL TECHNIQUES, SAMPLE, AND DATA COLLECTION Statistical techniques, sample, and data collection One thing that distinguishes quantitative research works from qualitative research works is the use of statistical data. This is because as noted by Hunter and Leahey (2008), in quantitative research, researchers put a lot of emphasis on the use of statistical and mathematical data. There are several advantages that can be derived from undertaking quantitative research in any typical education setting.

However, Williams & Monge (2000) cautioned that the real benefits and advantages of engaging in quantitative research may not be realized if researchers do not put in a lot of efforts to follow the procedural requirements of this type of research method. In this, Hunter and Leahey (2008) stressed that there are several issues and concepts that come together to make up the procedural requirements of a quantitative research. Three of these can be noted to include the setting up the sample, performing data collection, and using statistical techniques in the analysis of results or data.

Each of these three concepts has a different impact on the study. In a quantitative research, the sample refers to a group of people among the population with who the researcher has direct interaction or encounter with as part of the data collection process (Gall, Gall & Borg, 2006). The population can therefore be said to be a very large set made up of several people but the sample a subset within the larger set from who data is collected. Reading through reading through the work of Gall, Gall and Borg (2008), one gets the understanding that having a sample is important for several reasons.

In the first place, having a sample is very important to ensure that the researcher can have a group of people who can be handled well in relation to the time available for the study. What this means is that when there are so many people to deal with at a time, it may be difficult to perform an in-depth data collection (Eschenbacher, 2012). What is more, sample can be used to ensure that the researcher uses only people with the right form of information that the researcher seeks for the study.

After the sample has been developed, a researcher may go ahead to collect data from the respondents or participants within the sample. Performing data collection can be very difficult for researchers and can even impact on the outcome of the study if the right data collection procedure is not selected. With this, Gall, Gall and Borg (2008) noted that there are several data collection procedures which are often defined under the research strategy that the researcher uses. For most quantitative research, the use of survey is used as the most preferred data collection procedure.

Through survey, a researcher develops a data collection instrument, commonly a questionnaire, and administers it to the sample. Williams & Monge (2000) notes that there are several factors that go into the data collection process. Normally, it is important for the researcher to facilitate the process by ensuring that steps are taken to equip the sample with the right form of mindset to provide the form of data that is most desired for the study. After quantitative data have been collected through such means as the use of a questionnaire, the need to use statistical techniques to analyzing data becomes very important.

In this, various statistical techniques have been recommended in literature. For very typical empirical investigations or research, the use of percentages, diagrams, distributions, correlation coefficient, test of significance of mean, analysis of variance, and tests of goodness of fit may be employed as statistical techniques (Gerber, Batalo and De Arment, 2014). Gall, Gall and Borg (2008) created the understanding that statistical techniques help the researcher to have a comprehensive and empirical analysis of the data.

The analysis is said to be comprehensive because by the use of statistical software such as GRETL, SPSS or STATA, all the variables of the quantitative outcomes are each subjected to thorough statistical calculations. The analysis is also said to be empirical because the outcome of statistical techniques always have the same universal interpretations no matter where they are applied (Burton and Mazerolle, 2011). To conclude, it will be reiterated that even though research works have several benefits and advantages, it takes researchers who are committed to following the exact procedures and principles that guide the conduct of such research to attain them.

Some of the ways by which a researcher may attain this is by the correct application of statistical techniques after the right sample has been set and the most suitable data collection procedure has been used. In each of the instances, it is important for the researcher’s role as a facilitator for success to be clearly identified. This is because from the discussions above, it can be noted that the researcher is single handedly tasked to be at the center of all these processes and also ensuring that the right forms of interventions are put in place to make each process a success.

References Burton, L. J. and Mazerolle, S. M. (2011). Survey Instrument Validity Part I: Principles of Survey Instrument Development and Validation in Athletic Training Education Research. Athletic Training Education Journal, 6(1 ), 27-35 Eschenbacher, H. (2012). The Research Process in a Multi-Level Mixed-Methods Case Study: International Organization Headquarters and Field Employee Perspectives of a Program in Southern Sudan. Research in Comparative and International Education, 7(2), 176-191 Gall M. D., Gall J. P. & Borg W. R. (2006).

Educational Research An Introduction (8th ed.). Boston, MA: Pearson Gerber, P. J., Batalo, C. G and De Arment, S. T. (2014). An Analysis of State Data Collection Protocols for Measuring Postschool Outcomes for Students with Disabilities. Career Development and Transition for Exceptional Individuals, 37(2), 97-105 Hunter, L. and Leahey, E. (2008). "Collaborative Research in Sociology: Trends and Contributing Factors". The American Sociologist 39 (4): 290. Williams F. & Monge P. R. (2000). Reasoning With Statistics: How To Read Quantitative Research. (5th ed.).

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