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Sampling method and analysis Featured

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In any research, the sampling method and data analysis are critical in making references and conclusion. The main goal of sampling method is to obtain a sample that represent of the target population. The information obtained from the sample is expected to be the same as if a complete census of the target population was executed. There are several strategies for selecting a sample. However, they can be broadly categorized as probabilistic and non-probabilistic methods. Probabilistic samples are selected such that each unit has an equal opportunity of being included in the sample. They include methods such as random sampling (simple random, stratified random & cluster sampling). In “nonprobabilistic” samples, the probability of the unit being selected is unknown. Statistical analysis or hypothesis tests are essential in analyzing results from the sample. Descriptive and inferential statistics are common in most studies. In addition, correlations are common in surveys. The aim of the paper is to analysis how various researchers conducted their sampling, and the statistical test used.  The review will help identify similarities and differences in selecting samples and help in defining common rules in selecting samples and statistical test. The referent papers will be sourced from major databases such Proquest and EBSCOhost. The articles compared in this paper addressed the issue of organization change, and quantitative studies are given priority.

Comparison of articles

Marx and Eduardo (2012) conducted a quantitative survey on semi-autonomous workgroups and team work in firms operating in Brazil.  The aim of the research was to understand and describe how autonomy or self-management has been used in firms in Brazil. The study adopted a probabilistic sample due to limited time and resources. The probabilistic sample limited the chances of extrapolating the study outside the sample.  A sample of 500 companies was selected conveniently among the largest and best performing companies.  The convenience sample favored companies with high revenues. Questionnaires were used to gather quantitative data that was used to test hypotheses generated by the authors.  The aim of was to test the hypothesis of positive correlation between independent variables related to autonomy and dependent variables. The number of variables was reduced through factors analysis. The researchers used cronbach’s a coefficient to evaluate internal validity of variables. The correlation coefficient was calculated using spss V. 15.0.

Boddy & Macbeth (2000) reports on a quantitative survey of 100 companies that explored collaborative relationship between companies.  The aim of the research was to establish whether there is quantitative evidence about effectiveness of prescriptions and change. A pilot survey was conducted before to the main survey. The main survey involved companies that had experience of supply chain partnering.  A convenient sample was selected from a database of companies that had requested information on supply chain partnering.  350 companies were then randomly selected and issued with study questionnaires.  The questionnaires were posted to the participants.   The data were analyzed using chi-square and discriminant function as the main statistical analysis. 

Chrusciel & Field (2006) investigated factors needed for change transformation in an organization. The study used a mixed methodology (qualitative and quantitative) to examine critical success factor in implementing change. The population survey involved 53 Facilities planning and management staffs who have implemented facilities administrative management information system (FAMIS). The authors choose purposeful sampling due to the small number of the target population.  The purposeful sample helped in selecting a random sample of 21 companies.  The main survey instrument was a questionnaire that was sent to respondents through mail. The questionnaire used Likert scale to evaluate questions. The data was analyzed using descriptive statistics (means, standard deviations and skews). 


One of the key similarities in the way authors selected the sampling method was the use of the target population to select that sample population.  According to the size or amount and accessibility of the target population, the researchers selected the sampling method that provided a convenient sample size.  Another similarity is the use of the hypothesis or research questions to guide the analysis of data. The researchers used the study questions to analysis the data. The test analysis selected was directed at answering the research questions. For example, correlation analysis was used to determine the relationship while t-tests were used to test significance relationship. Descriptive analysis is common in all the studies. The researchers started presentation of results by offering a descriptive analysis of the data. 


The sampling process should start with defining the target population. According to Sekeran & Bougie (2010), the target populations must be defined in terms of units, geographical extent, and time. The research objectives and scope of the study are critical in defining the target population. The target population may also be influenced by factors such as access to the population, availability of participants, and time frame.

The data analysis method is determined by the scope and objectives of the study. The data analysis is designed to answer specific research questions and as such must be aligned to meet these objectives.  Using the objectives of the study to select the data analysis methodology ensures the analysis remains objective.

The third rule states that results and decision should be organized in a sequence that matches the research questions. The presentation of the data should be that each of the hypothesis or research question is analyzed separately.   Presenting the data in this chronological order will help the reader to follow the results and make the work align with the other parts of the study.


Boddy, D., & Macbeth, D. (2000). Prescriptions for managing change: a survey of their effects in projects to implement collaborative working between organizations. International Journal Of Project Management, 18(5), 297.

Ho, G. S., Choy, K. L., Lam, C. Y., & Wong, D. C. (2012). Factors influencing implementation of reverse logistics: a survey among Hong Kong businesses. Measuring Business Excellence, 16(3), 29-46. doi:10.1108/13683041211257394

Marx, R., & Simonetti, P. (2013). Study on the implementation of work organization in semi-autonomous groups: a quantitative survey of firms operating in Brazil. International Journal Of Human Resource Management, 24(13), 2473-2489. doi:10.1080/09585192.2012.744333

Sekaran U & Bougie R (2013). Research methods for business: A skill building approach. Wiley publishers, USA.

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