Respond to two or more of your colleagues

Issa

Population and Sampling

The constructs of population and sampling are integral to all research undertakings. Populations refer to a complete set of elements, whether persons or objects, that possess some common characteristics defined by the sampling criteria established by the researcher(Han,2019). The number of elements in the populations reflects the size of the population. Populations can fall into either target populations or accessible populations. The targeted population for this research study will consist investment leaders from capital investment companies in the northeast of U.S , who have a least 5 years experiences  in the industry and implanted strategies to increase investment return, and investment performance.  

The population is a collection of individuals from which researchers develop their sample research study(Han, 2019). Researchers must ensure the targeted population to be accessible; selecting an inaccessible population could potentially affect the scholar's ability to collect data(Binde & Romild, 2019). Aligning the population with the research question allows researchers to collect data from strategic investment decision making leaders from capital investment industry in northeast region of the U.S ,who met the criteria from the research study. Hence, providing enough data is a precursor to credible analysis and reporting to determine an adequate sample size that has a direct relation with data saturation. However, the quantity is not the determining factor when it comes to data saturation; the quality of the data must align with the research study(Binde & Romild, 2019). 

Sampling refers to the process of selecting a sample from a population of interest so that a researcher can fairly generalize the results gained from these participants to the population from which the researcher obtained the sample(Wei et al,2019). There are different types of sampling techniques that researchers can utilize during their investigations, which broadly fall into probability and non-probability sampling(Wei et al ,2019). Some of the most notable probability sampling techniques include simple random sampling, stratified sampling, clustered sampling, and systematic sampling (Han, 2019).

Thu, on the contrary, non-probability sampling techniques include convenience sampling, purposive sampling, quota sampling, and snowball sampling. The main difference between a sample and a population has to do with the manner in which the researcher assigns observations to the data set(Binde & Romild, 2019). A population includes all the elements from a data set, whereas a sample consists of one or more observations derived from the population. The constructs of population and sampling can indeed find direct application in my doctoral research study. I will use purposive sample to  compute the correct sample size from the population that can make the findings easy to generalize to the wider population.

In selecting samples during my research endeavors, I will ensure that I take into account different attributes, including the demographic profile of the participants, and experiences to enhance representativeness.

 Moreover, to determine the desired representative sample size ,the  G*Power3.1.2  power analysis  will be used to determine the appropriate sample size for this research study. The use of component of G*Power3.1.2  analysis  is considered to be an excellent power analysis software tools to analyze sample size of doctoral  research study(Faul,  Erdfelder , Buchner,  & Lang, 2009).

References

 

 

Binde, P., & Romild, U. (2019). Self-reported negative influence of gambling advertising in a Swedish population-based sample. Journal of gambling studies, 35(2), 709-724.doi:10.1007/s10899-018-9791-x

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.doi:10.3758/BRM.41.4.1149

Han, Q. (2019). A Literature Review on the Study of Income Distribution, Education and Education Return Rate-Based on Family Background and Gender Differences. Journal of Accounting, Business and Finance Research, 5(2), 43-50.doi:10.20448/2002.52.43.50

Wei, P., Song, J., Bi, S., Broggi, M., Beer, M., Lu, Z., & Yue, Z. (2019). Non-intrusive stochastic analysis with parameterized imprecise probability models: I. Performance estimation. Mechanical Systems and Signal Processing, 124, 349-368.doi:1 0.1016/j.ymssp.2019.01.058

Joey

Bruns, A., & Moon, B. (2019). One Day in the Life of a National Twittersphere. NORDICOM 

            Review, 40, 11–30. https://doi-org /10.2478/nor-2019-0011

            The authors explore the Twitter social platform.  This article addresses the different types of imbalances such as loudest voices and hashtag activities etc. by exploring in-depth the day-to-day patterns of activity within the Australian Twittersphere for a day.  Bruns and Moon (2018) use a very different methodology.  The authors explore a shift in perspective through focus groups, medial diaries, and other forms of self-reporting.  The used data set that for the analysis is tracking infrastructure for Social Media Analysis (TrISMA).  This infrastructure uses the public activities of all the national Twittersphere for sports and politics.

Hambrick, M. E. (2017). Sport communication research: A social network analysis. Sports

            Management Review, 20(2), 170-183. doi:10.1016/j.smr.2016.08.002

            Hambrick (2017) researches the way sport communication research has grown through social network analysis (SNA).  The author uses a methodological approach to show different research collaborations and help understand many areas for growth. The author focuses on the research field within social networks for sample and data collection.  Hambrick (2017) uses three different approaches to increase validity.  Hambrick (2017) uses studies from other researchers, snowball sampling from popular scholars, and combines citations.           

Harker, J. L., & Saffer, A. J. (2018). Mapping a subfield’s sociology of science: A 25-year

network and bibliometric analysis of the knowledge construction of sports crisis communication. Journal of Sports & Social Issues, 42(5), 369–392. https://doi-org.ezp.waldenulibrary.org/10.1177/0193723518790011

            The author of this research wants to evaluate the development of different authors work to find other authors, journals, and theories involved in the subfield’s knowledge construction process.  Harker and Saffer (2018) use the network analysis and bibliometric method to analyze 25 years of scholarship in over 20 journals to reveal significant areas of focus in sports crisis communication.  The author also focuses on applied and critical cultural learning.