In quantitative research it’s important to know what type of instruments and sampling methods are available. It’s also beneficial to understand the descriptive statistical method as well as the inferential method when dealing with business problems. This paper will attempt to summarize each of the selected data collection instruments, sampling methods, and the statistical methods as well as express their weaknesses and strengths and identify specific situations they could be utilized within. An instrument is a tool used to collect data within a research project. There are many quantitative data collection instruments available but we will focus on three: interview, survey/questionnaire and focus group. The interview can be conducted either individually or in a group setting. Interviewers are trained to deviate only minimally from the question wording to ensure uniformity of interview.

Specific circumstances for which interviews are particularly appropriate include situations involving complex subject matter, detailed information, high-status respondents, and highly sensitive subject matter. This could include answering questions such as “What are participants’ and stakeholders’ expectations?” (nsf.gov, 2014). The survey or questionnaire is another valued instrument in collecting data. Surveys are a very popular form of data collection for large groups, where standardization is important. Surveys consist of written questions and responses. Responses may take the form of a scale rating, give categories from which to choose, or ask to estimate numbers or percentages of time. Surveys are specifically selected when information is to be collected from a large number of people or when answers are needed to a clearly defined set of questions.

This could include trying to improve customer service of a business (nsf.gov, 2014). The final instrument of data collection is that of the focus group. Focus groups are a gathering of 8 to 12 people who share some characteristics relevant to the evaluation and combine elements of both interviewing and participant observation. The assurance of using a focus groups is the explicit use of the group interaction to generate data and insights that would be unlikely to emerge otherwise. The groups can be useful at both the formative and summative stages of an evaluation. A focus group should be used specifically for quantitative data collection when continuity of information a single subject area is being examined in depth and strings of behaviors are less relevant, such as identifying project strengths and weaknesses (nsf.gov, 2014).

Sampling is selecting some of the elements in a population, and drawing conclusions about the entire population. There are several compelling reasons for sampling, including lower cost, greater accuracy of results, greater speed of data collection, and availability of population elements. This paper will focus on three types of sampling methods: cluster, stratified, and systematic. When the population can be divided into groups of elements with some groups randomly selected for study, one has cluster sampling. To use cluster sampling two conditions must be present: (1) the need for more economic efficiency than can be provided by simple random sampling and (2) the frequent unavailability of a practical sampling frame for individual elements (Cooper & Schindler, 2014).

Cluster sampling has the strengths and weaknesses associated with most probability sampling procedures, some of these strengths include: •If the clusters are geographically defined, cluster sampling requires less time, money, and labor than simple random sampling. It is the most cost-effective probability sampling procedure. •For the same level of costs, cluster sampling with a higher sample size may yield less sampling error than that resulting from simple random sampling with a smaller sample size. •Cluster sampling is much easier to implement than simple random sampling. The weaknesses one would encounter include:

•The sampled clusters may not be as representative of the population as a simple random sample of the same sample size. •Cluster sampling introduces more complexity in analyzing data. •The more stages there are in a cluster sample design, the greater overall sampling error (sagepub.com, 2014). The probability sampling procedure in which the target population is first separated into mutually exclusive, equal segments, then a simple random sample is selected from each segment, and combined into a single sample is a process called stratified random sampling. Results can be weighted and combined into appropriate population estimates.

Three reasons to choose this include: (1) to increase a sample’s statistical efficiency, (2) to provide adequate data for analyzing the various subpopulations or strata, and (3) to enable different research methods and procedures to be used in different strata (Cooper & Schindler, 2014). Stratified sampling has some strengths which include: •Stratified samples yield smaller random sampling errors than those obtained with a simple random sample of the same sample size. •Stratified samples tend to be more representative of a population because they ensure that elements from each stratum in the population are represented in the sample. •Utilizing stratified sampling permits the researcher to use different sampling procedures within the different strata. The weaknesses one would encounter include:

•Stratified sampling has a greater requirement for prior auxiliary information. •Selection of stratification variables may be difficult if a study involves a large number of variables. •Misclassification of elements into strata may increase variability (sagepub.com, 2014). Systematic sampling is a probability sampling procedure in which a random selection is made of the first element (kth) for the sample, and then subsequent elements are selected using a fixed or systematic interval (the range of 1 to k) until the desired sample size is reached. The kth element, or skip interval, is determined by dividing the sample size into the population size to obtain the skip pattern applied to the sampling frame (Cooper & Schindler, 2014). There are several strengths to this method which include: •Simplicity and flexibility.

•Systematic sampling ensures that the sample is more spread across the population. •Systematic sampling eliminates the possibility of autocorrelation. The weaknesses include:

•Periodicity in the sampling frame is a constant concern in systematic sampling. •Estimating variances is more complex than that for simple random sampling. •Monotonic trend in the population elements (sagepub.com, 2014). The descriptive statistical method is the analysis of data that helps describe, show or summarize data in a meaningful way such that patterns might emerge. This is done by using measures of tendencies and measures of spread, such as average or standard deviation (laerd.com, 2014). By taking the article “A Descriptive Study on the Motivation of Bosnian Workers” one can make conclusions beyond the data analyzed or reach conclusions regarding any hypotheses that might have been made. The article states, “This research aims to descriptively identify the degree of impacts of antecedents and consequences of employee motivation in the selected company.”

The study employs conducting a questionnaire on all available employees of the company. The questionnaire consisted of inquiries about personal information, work satisfaction, the degree of satisfaction, and factors of motivation. The results of the survey indicate that loyalty level are low and there is a recommendation of improving manager- employee relations (Ozlen & Hasanspahic, 2013). Using the descriptive statistical method has only one identified strength, that of simplifying large volumes of data. Its main weakness is limiting one to using the data to make summations about the people or objects being measured (laerd.com, 2014). On the other hand inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came. Inferential statistics use data one has observed in a sample to make hypotheses or predictions about data that have not been observed directly in the larger population (laerd.com, 2014).

By using the article “Benefits of Inferential Statistical Methods in Radiation Exposure Studies: Another Look at Percutaneous Spinal Cord Stimulation Mapping [Trialing] Procedures” one can see two studies conducted using large sample sets, to compare fluoroscopy times between novice and expert physician implanters performing SCS trialing procedures, and to the benchmarked reference level, using inferential statistical methods. The results were no statistical difference was found in mean fluoroscopy times for SCS trialing procedures between the novice- and expert-implanter. This means no differences were shown in fluoroscopy times for such based on physician experience (Wininger, 2012). This result can show part of the strength of inferential statistics, the ability to generalize one’s findings to a broader population group.

One of the main weaknesses of this is inferential statistics are based on the concept of using the values measured in a sample to estimate/infer the values that would be measured in a population; there will always be a degree of uncertainty in doing this (laerd.com, 2014). This methods can be combined to take a generalization made by inferential and use the descriptive method to expand and see if this generalization can be applied to a population. This can also work in reverse, if you want to take a population sample and generalize it to a specific area. By using applications from both you can combine and get the research study you wish to fulfill. In my functional area the inferential statistical method would be most beneficial. As a police officer, I would want to target a general area not a broad population. By using this method I could understand better the research done in my area as that would benefit producing effective results in my community.

References

Cooper, D., & Schindler, P. (2014). Business research Methods (12th ed.). New York, NY: McGraw-Hill. Ozlen, M. K., & Hasanspahic, F. (2013, Jul). A Descriptive Study on the Motivation of Bosnian Workers. International Journal of Academic Research in Business and Social Sciences, 3(7), 184-201. Wininger, K. (2012). Benefits of inferential statistical methods in radiation exposure studies:another look at percutaneous spinal cord stimulation mapping [trialing] procedures. Pain Physician, 15(2), 161-170. laerd.com. (2014). Descriptive inferential statistics. Retrieved from http://statistics.laerd.com/statistical-guides/descriptive-infrential-statistical-faqs.php nsf.gov. (2014). An Overview of Quantitative and Qualitative Data Collection Methods. Retrieved from http://www.nsf.gov/pubs/2002/nsf02057/nsf02057_4.pdf sagepub.com. (2014). Choosing the type of probability sampling. Retrieved from http://www.sagepub.com/upm-data/40803_5.pdf