Provide a response to the below post using at least

Provide a response to the below post using at least 450 words. Response must be supported with at least 2 peer-reviewed sources and include 1 biblical application/integration

What are the independent and dependent variables in this study?

In the article titled, “Independent, Dependent, and Other Variables in Healthcare and Chaplaincy Research, Journal of Health Care Chaplaincy” the definitions of independent and dependent variables are clearly stated. According to this source, the independent variable is the “presumed cause in a cause-effect relationship” and the dependent variable is simply the “presumed effect”. After reading this case study more than once, the conclusion was drawn that there are multiple independent variables which consist of, the general national framework conditions, entrepreneurial framework conditions, entrepreneurial opportunities, entrepreneurial capacity and busines dynamics. Each of these independent variables causes the outcome of the dependent variables which are economic conditions, GDP, and jobs. All variables were found in the Exhibit C-GEM 1-1 Conceptual Model: The Entrepreneurial Sector and Economic Growth.

What are some of the intervening, extraneous, and moderating variables that the study attempted to control with its 10-nation design?

The intervening variable “transmits the effect of an independent variable to a dependent variable” (MacKinnon et al., 2002). In this study, the intervening variable can be assumed to be the connection or relationship between the government and entrepreneurial efforts. In the case, GEM identified entrepreneurship as “any attempt at new business or new venture creation, such as self-employment, a new business organization, or the expansion of an existing business, by an individual, a team of individuals, or an established business.” This connection or relationship (intervening variable) can be positive, negative, or non-existent. The study is seeking to identify the extent to which this relationship affects entrepreneurial endeavors. The next variable, extraneous variables, are “all variables, which are not the independent variable, but could affect the results of the experiment” (McLeod, 2019). Out of all the variables in a study, extraneous variables have the least influence but are still taken into consideration when evaluating the results. There are a few extraneous variables that participate in the study but are believed to not have any significant effect on the outcome. These variables are factors of the data collection methods including, two rounds of adult population surveys, 1,000 adults per country, hour-long personal interviews, 4-39 experts in each country, and the detailed 12-page questionnaire. If the study were to be recreated a second time and these extraneous variables slightly changed, the results would likely be nearly the same as the first study, given that all other variables remain unchanged. Lastly, a moderating variable “is a variable that modifies the form or strength of the relation between an independent and a dependent variable” (MacKinnon, 2011). In this study, the moderating variables are promoting entrepreneurship to people outside the age group of 25-44, targeting women with resources, committing to long-term postsecondary education, promoting entrepreneurship, and developing a capacity for society. All of these variables can be manipulated by researchers for the purpose of the study.

Can you do a causal study without controlling intervening, extraneous, and moderating variables?

Controlled variables are a pivotal part of conducting an accurate and structure study. When certain variables are controlled, research results are more reliable, accurate, and contain a smaller margin of error. Control variables “play a critical role in organizational research, and many authors have argued that the proper use of control variables is essential for drawing valid statistical conclusions” (Sturman et al., 2021). If researchers in a study value discovering accurate statistical conclusions, then it is important that they have some sort of ability to manipulate specific variables. With this said, it is possible to conduct a causal study without controlling intervening, extraneous, and moderating variables, but it is not recommended in the name of accuracy. Obviously, if nothing more is being studied than the relationship one variable has on another this can be achieved without controlling variables. However, data will be more detailed and accurate if variables are manipulated to achieved different results.

What is the impact on study results of using national experts (key informants) to identify and weigh entrepreneurial framework conditions?

First and foremost, it is important that any individual in a position of influence exercises integrity. Proverbs 10:9 states, “Whoever walks in integrity walks securely, but he who makes his ways crooked will be found out.” In this case, using national experts (key informants) to identify and weigh entrepreneurial framework conditions means that the integrity of these people affects the results of the study. “Even when key informants discuss relatively straightforward concepts, research illustrates the need to consider how their respective positions within the community affect the information they provide” (McKenna & Main, 2013). If informants have malintent or are more interested in pursuing their own agendas rather than conducting an accurate study, then their lack of integrity will negatively affect the accuracy of the results. The key informants chosen for this case have significant experience with entrepreneurial framework conditions. Using these experts allowed for a deeper understanding of the results as well as the reasoning why those specific results occurred.

Can you do a causal study when much of the primary data collected is descriptive opinion and ordinal or interval data?

A causal study can be conducted when much of the data collected is descriptive opinion and ordinal or interval data. The purpose of descriptive statistics is “to summarize data in an organized manner by describing the relationship between variables in a sample or population” (Kaur et al., 2018). This leads us to believe that descriptive data is a great foundation for research questions to arise. When looking at detailed data we are able to see areas of discrepancy and therefore, we can identify variables and relationships for future study. Regardless of what kind of data is being presented, researchers can make inferences backed by concrete evidence and all areas of uncertainty can be tested in the future.

References

Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive Statistics. International Journal of Academic Medicine4(1), 60–63. https://doi.org/10.4103/ijam.ijam_7_18 

Laura T. Flannelly, Kevin J. Flannelly & Katherine R. B. Jankowski (2014) Independent, Dependent, and Other Variables in Healthcare and Chaplaincy Research, Journal of Health Care Chaplaincy, 20:4, 161-170, DOI: 10.1080/08854726.2014.959374

MacKinnon, D. P. (2011). Integrating Mediators and Moderators in Research Design. Research on Social Work Practice21(6), 675–681. https://doi.org/10.1177/1049731511414148

MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods7(1), 83–104.https://doi.org/10.1037/1082-989X.7.1.83

Mcleod, S. (1970, January 1). Extraneous Variable. Extraneous Variable | Simply Psychology. https://www.simplypsychology.org/extraneous-variable.html. 

Sturman, M. C., Sturman, A. J., & Sturman, C. J. (2021). Uncontrolled control variables: The extent that a researcher’s degrees of freedom with control variables increases various types of statistical errors. Journal of Applied Psychology. https://doi.org/10.1037/apl0000849