The primary goal of the replies is to discuss the threads by offering analysis and critiques to include specific strengths and weaknesses and other insights for considera

· What are the independent and dependent variables in this study?

The GEM study is based on an effort to determine which types of policies would lead to increased entrepreneurial activity. GEM project directors have suggested that a variety of factors would increase entrepreneurship, including specific programs that support entrepreneurial activity and facilitating access to resources for women and people outside the most active age group in order to foster an environment that makes entrepreneurial activity more attractive. Additional resources that are proposed by project directors at GEM include creating a culture that validates entrepreneurship and emphasizing individual capacity to recognize entrepreneurial opportunities.  A primary intervening variable in this study is the lack of control of extraneous variables in this study. This could take many forms, including macroeconomic factors within individual countries and cultural factors that precipitate or impede entrepreneurship. For example, a country that shows high value for entrepreneurship may not be as directly affected by programs designed to increase the attractiveness of business opportunities for women or people outside of the active age group.  

The independent variables for the GEM study include efforts to promote entrepreneurship, making resources for women available to participate in the entrepreneurial process, increasing access to post-secondary education, developing a society that accommodates higher levels of income disparity, and the culture that validates and promotes entrepreneurial efforts. The independent variables are characterized by an overarching conceptual model that includes general national framework conditions, entrepreneurial opportunities, business dynamics, economic growth, economic capacity, and the social, cultural, and political context (Pokhariyal, 2020).. The dependent variable includes overall economic development as characterized by entrepreneurship.

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

Moderating variables include culture, equity, debt, education, and R&D. The primary intervening variables relate to the economic conditions of a country. In countries in which economic growth is already high, the independent variables may have a direct positive impact that magnifies the benefits of the intervention. Extraneous variables would include those which may potentially impact the outcomes of the research study but which are not part of the defined relationship between independent and dependent variables (Namazi & Namazi, 2021). In this case, these variables could include market trends that increase opportunities for entrepreneurship but which are not included in this study.

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

Conducting a causal study in business research without controlling intervening, extraneous, and moderating variables can lead to flawed and unreliable findings. These variables play crucial roles in the research process and failing to account for them undermines the validity and generalizability of the study's outcomes. Intervening variables are factors that come between the independent and dependent variables, potentially influencing the relationship being examined. Ignoring these variables may result in biased or inaccurate conclusions. For these reasons, a causal study could not be conducted without controlling intervening, extraneous, and moderating variables (Schindler, 2019). These variables influence the extent of a relationship between independent and dependent variables. They also could impose confounding variables that complicate the relationship or which.

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

The impact of using national experts to evaluate entrepreneurial framework conditions is the inclusion of potential subjectivity or bias based on national variables or personal beliefs. A causal study would not be feasible in this case because of the inclusion of personal opinion or subjective bias. The inclusion of national experts brings valuable insights and expertise to the research process (Budsaratragoon et al., 2019). These individuals often possess in-depth knowledge and experience in the specific domain being studied, enabling them to provide unique perspectives on the entrepreneurial framework conditions. Their expertise can enhance the validity and accuracy of the study's findings, ensuring a comprehensive understanding of the factors at play. Additionally, the use of key informants can help capture nuanced information that may not be readily available through other sources. However, it is important to consider the limitations of relying solely on national experts. Their perspectives may be subjective and influenced by personal biases or interests. The selection of key informants itself can introduce bias if not conducted rigorously. Moreover, the experts' viewpoints may not necessarily represent the diversity of entrepreneurial activities or framework conditions in the entire population.

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

Yes, it is possible to conduct a causal study even when a significant portion of the primary data collected consists of descriptive opinions and ordinal or interval data. While causal studies typically rely on quantitative data with clearly defined variables, including descriptive opinions and ordinal or interval data can still contribute to the overall analysis. However, it is important to recognize the limitations and potential challenges associated with such data types. Descriptive opinions provide valuable qualitative insights into the experiences and perceptions of participants, offering a deeper understanding of the context surrounding the causal relationship being studied. Ordinal and interval data, although not as precise as interval or ratio data, still provide information about the relative rankings or differences between variables.

In James 1:22, it is written, "But be doers of the word, and not hearers only, deceiving yourselves." This biblical reference emphasizes the importance of taking action and applying knowledge. Similarly, in the context of conducting a causal study, it is not enough to merely collect descriptive opinions and ordinal or interval data. The passage reminds us that true understanding comes from putting our knowledge into practice. In the realm of research, this means utilizing appropriate analysis techniques and methods to derive meaningful insights and draw valid conclusions.

Budsaratragoon, Jimenez, P., Jitmaneeroj, B. (2019). Measuring causal relations and identifying critical drivers for corporate sustainability: The quadruple bottom line approach.  Measuring Business Excellence, 23(5), DOI: 10.1108/MBE-10.2018-0080.

Namazi, M., Namazi, N. (2021). Conceptual analysis of moderator and mediator variables in business research.  Procedia Economics and Finance, 36(3), DOI: 10.1016/S2212-5671(16)30064

Pokhariyal, G. (2020). Importance of moderating and intervening variables on the relationship between independent and dependent variables.  International Journal of Statistics and Applied Mathematics, 4(5), 1-8.

Schindler, P. (2022). Business Research Methods, 14th Edition.

Wathanakom, N., Khlaisang, J., Songkram, N. (2020). The study of the causal relationship between innovativeness and entrepreneurial intention among undergraduate students.  Journal of Innovative Entrepreurship, 9(3), DOI: 10.1186/s13731-020-00125-5

Post 2

 A GEM of a Story

             Leatham (2012) states that the independent variable is the one in which we have control and choose to vary where the dependent variable is the one in which we do not have direct control over and are seeking to measure as we change the independent variable. Schindler (2023) defines the dependent variable as the “primary interest to the researcher; it is measured, predicted, or otherwise monitored and is expected to be affected by manipulation of an independent variable” (p. 15). The dependent variables in this study are business dynamics, national economic growth, and start up rate. Business dynamics, economic growth, and start up rates vary across nations based on the culture, political, and entrepreneurial opportunities. The independent variables in this study are culture, equity, political, context, framework conditions, entrepreneurial opportunities and capacity. All of these independent variables play a role in determining business dynamics, economic growth, and start up rates.

Q2. What are some of the intervening, extraneous, and moderating variables that the study attempted to control with its 10-nation design?

            Schindler (2023) defines a moderating variable as the second independent variable that is believed to have significant causal effects on the original independent and dependent variable relationship; a extraneous variable exist in an almost infinite number to possibly affect a given relationship; and an intervening variable as a factor that theoretically affects the dependent variable but cannot be observed or measured, its effect must be indirect from the effects of the independent and moderating variables of the experiment (pgs 17-18). Flannelly, Flannelly & Jankowski (2014) describe extraneous variables as ones researchers are concerned about because they alter the relationship between the independent and dependent variables, researchers try to control them by controlling the conditions of the experimental environment. The study used culture, equity, debt, R&D, education, subcontractor, and legal, banking as variables in an attempt to control the data with its 10-nation design. The chosen nations were also variables.

Q3. Can you do a causal study without controlling intervening, extraneous, and moderating variables?

             “In a causal study we estimate the probability that  A “produces”  B based on what we observe and measure” (Schindler, 2023, p. 178). Causal studies can be done without controlling the variables but would lead to results that may not be accurate or that were skewed. Carlson and Wu (2012) discusses how researchers attempt to prevent extraneous influences by limiting the experimental conditions to hold persistent the factors that might independently influence outcomes. In their research they will note their intent to control extraneous influences that could affect results. When doing a study the researcher is looking to get the most accurate answer as possible so they try to control some of the variables that might skew the answer and make it inaccurate. Some studies use surveys or participants which can at times not be the best source of information. Surveys may not be answered completely, honestly, or at all. Participants may give answers that they think are the “right” answer instead of what they are actually experiencing.  

Q4. What is the impact on study results of using national experts (key informants) to identify and weigh entrepreneurial framework conditions?

            Cossham & Johanson (2019) define a key informant as an individual who is knowledgeable providing a prospective on research that the researcher themselves lack, they are usually not participants in the study. Having experts that have knowledge of the entrepreneurial framework and conditions brings value to the research as they have information that is directly from the community being studied, a sort of insider information so to speak. They are impartial and able to help the researcher eliminate bias. The impact of using national experts is a positive one on the research to help it to be more accurate when it comes to facts about entrepreneurial conditions in their nation. What better way to prove your research than with experts.

Q5. Can you do a causal study when much of the primary data collected is descriptive opinion and ordinal or interval data?

            Schindler (2022) defines ordinal data as “provide classification and order but no equal distance or natural origin” and interval data as “reveal classification, order, equal distance, but no natural origin”. While Ebrahimi, Tavana, Rahmani, & Santos-Arteaga (2018) define ordinal data as expressing a special relation among observations when their actual values are unknown and interval data as an approach to treat vague observations where the exact value is unavailable. Opinions most always come into play with any research. The hypothesis usually starts out as a question of the researchers opinion where they are trying to provide themselves correct. Casual studies can be done with descriptive opinions and ordinal or interval data. A researcher wants to use in their study all the data they can to get the most accurate answer to their question.

Romans 12:2 says “Do not be conformed to this world, but be transformed by the renewal of your mind, that by testing you may discern what is the will of God, what is good and acceptable and perfect”. To me this verse encourages us to question, study, and research things. We are to have our own thoughts and opinions making us leaders not just mere followers of others.

References