case study 1

1. The dependent variable (DV) is the attempt at a new business or new venture creation. It’s the primary interest to the project directors of the Global Entrepreneurship Monitor and the Kauffman Center for Entrepreneurial Leadership of Ewing Marion Kauffman Foundation, the London School of Business, and Babson College. An attempt at a new business or new venture creation can be monitored, predicted, or measured and it is expected to change from a controlled independent variable (IV).

The IVs are:

· Promoting entrepreneurship, especially outside the most active age group (25–44), with specific programs that support entrepreneurial activity.

· Facilitating the availability of resources to women to participate in the entrepreneurial process.

· Committing to long-term, substantial postsecondary education, including training programs designed to develop skills required to start a business.

· Emphasis on developing an individual’s capacity to recognize and pursue new opportunities.

· Developing the capacity of a society to accommodate the higher levels of income disparity associated with entrepreneurial activity.

· Creating a culture that validates and promotes entrepreneurship throughout society.

The IVs are manipulated into affecting the DV, which is the attempt at a new business or new venture creation. This study researches the interaction of these two variables and is the basis of causal studies. A study on role conflicts at the workplace and its affects on work stress demonstrates the relationship of the two variables. “Studies have shown that role conflicts can reduce employee’s work satisfaction and increase their anxiety, which in turn lead to work stress” (Fu-Chiang, 2019).

2. The intervening variables are the perception of opportunity, potential of the population capacity, and the potential of the population motivation. These variables are thought come from IVs and may be the actual cause towards DVs. The lack of control of extraneous variables was compensated by using data from 10 nations with diversity in framework conditions, entrepreneurial sectors, business dynamics, and economic growth. Extraneous variables have little effect on DV. They are included to eliminate any biases, resulting a more accurate study. The moderating variables are entrepreneurs outside the most active age group (25-44), and women entrepreneurs. They are secondary IVs that might have a strong influence on the IV – DV relationship. It is believed that entrepreneurs who are women and entrepreneurs who are not the age of 25-44 would make a huge difference on the DV. A study on using instrumental variables and quasi-trial analysis for ESME, Epidemiological Strategy and Medical Economics, included additional variables in its study to lower any biases towards the study. “The SIMEX method reduces the bias induced by measurement error by establishing a relationship between measurement error-induced bias and the variance of the error” (Ezzalfani, 2021).

3. A causal study can be done without controlling intervening, extraneous, and moderating variables. Only a dependent and IV is required. Intervening variables are not directly controlled, are phenomena created by IVs, and observed to be the manipulator of the DV. There are two kinds of extraneous variables, control variables and confounding variables. Control variables affect the DV but not often, and confounding variables divide the DV stating only certain parts of the DV are influenced. Extraneous variables are measured to exclude any biases. The moderating variable narrows down an IV to a more specific range. It has significant effects towards the relationship between the IV and DV, but it’s not a necessity. The purpose of these variables is to refine the results with more thorough research. “A moderator variable can be considered when the relationship between a predictor variable and dependent variable is strong, but most often it is considered when there is an unexpectedly weak or inconsistent relationship between a predictor and a dependent variable” (Farooq 2017).

4. National experts can give their professional opinion on the openness to new entrepreneurship, their view on the role of the government, advise on financial markets, and information on technology and research and development. They can also assist in following a legal infrastructure, give management support, apply knowledge on labor markets, and provide knowledge on institutions. National experts give an extra step to the conceptual model for The Entrepreneurial Sector and Economic Growth. By using national experts, entrepreneurial framework conditions and entrepreneurial capacity can be skipped, leaving a path to economic growth through entrepreneurial opportunities and business dynamics. A study on how the government impacts entrepreneurial rice farmers reports positive results. “… the more conducive the role of the government is in optimizing an agroecosystem, the more it will increase the entrepreneurial orientation…” (Anisa, 2021).

5. A causal study can be done when much of the primary data collected is descriptive opinion and ordinal or interval data. It can be grouped into four types of data that are based on the type of measurement scale used to collect the data. The four types of data are nominal, ordinal, interval, and ratio. From these four types of data nominal data has classification but does not have order, equal distance, or natural origin. Ordinal is the same as nominal but includes order, and interval is the same as ordinal but includes equal distance.

Secondary data is collected from primary data. Primary data is interpreted to at least one level to form secondary data. When a causal study is made, a DV and an IV is needed to conclude a correlation. Primary data is collected on a DV, and secondary data can be used as IVs. In a recent study on depression and pain, it concluded that depression and pain is strongly found among low- and middle-income countries (LMICs). Primary data was collected on LMICs and secondary data such as the gender and age of residents were chosen as IVs. “Female sex, older age, lower levels of education and wealth, anxiety, arthritis, diabetes, angina, and asthma were significantly associated with higher prevalence of severe pain” (Stubbs, 2017).