Statistical Research and Critical Thinking Sample Essay
This paper is an essay that provides responses to four questions related to the study of statistical research and critical thinking.
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Choose a dependent variable from the following list: CO2 emissions, voter turnout, house prices, life expectancy. Choose any two independent variables you believe are causally related to your dependent variable, one with an expected positive relationship and one with an expected negative relationship. State the causal mechanism connecting each independent variable to the dependent variable. You may assume a Canadian context.
House prices as a dependent variable can be affected directly by inflation rates and population levels. The cost of buying a house is directly impacted by these two parameters that double up as independent variables. The level of inflation has a negative relationship with house prices because it causes the latter to increase significantly. Inflation is an economic factor that affects the circulation of money and the cost of important commodities in the market (Pentos et al, 2015). This means that the ability of a person to purchase a house can be determined by how much inflation rates have risen (Blydenburgh, 2011). Statistical Research and Critical Thinking Sample Essay
On the other hand, population levels impact positively on house prices because a higher population creates more demand for housing. Further, a stable population growth means that it is possible to come up with a costing mechanism that can price houses depending on their location and availability. The causal mechanism that connects inflation rates to house prices is money where the ability to afford a house ultimately depends on whether one is financially stable or not (Gillespie, 2007). On the other hand, house prices are connected to population levels by demand because it is only through wanting a house that housing agencies can determine how much to charge for a decent home. The link between the independent and dependent variables is perfectly illustrated in Canada where the cost of most homes depends on demand, inflation levels as well the number of people living in a given region.
Using her intuition and prior knowledge, a researcher expected that there would be a negative relationship between each of her two independent variables, A and B, and a dependent variable Y. However, her results were somewhat surprising. While there was a strong negative correlation between B and Y, her analysis found no correlation between A and Y. In her report she concluded that the relationship between A and Y is spurious while the relationship between B and Y is not. Is this conclusion correct? Explain.
The relationship between an independent and dependent variable lies in the ability of one parameter to influence another. The conclusion made by this researcher with regards to her study can be deemed correct because she found no relationship between A and Y. The opposite was quite true for the B independent variable which she found has a string correlation with Y. This shows that the existence of B does not have any impact on Y in any way whatsoever. If these results were to be explained in the form of an example, the dependent variable Y can be assumed to be drug addiction whereas A and B would be weight and peer pressure respectively. Statistical Research and Critical Thinking Sample Essay
There is a strong correlation that exists between peer pressure and drug consumption where a person can be influenced by the company they keep to pick up this habit (Nimon et al, 2015). However, a person?s weight has no relationship with drug abuse whatsoever and this means that there is a negative correlation between both variables. The same case applies in this context and it would be correct to assert that the conclusion made is correct.
Three people are discussing the recent rise in support for far-right parties in Europe. Mr. Nationalist argues that the rise is explained by a growing disillusionment with the EU. Ms. Economy disagrees and argues that it was caused by the low rate of economic growth since the global financial crisis. Mrs. Reasonable disagrees with the monocausal reasoning of Mr. Nationalist and Ms. Economy since she believes both factors matter. On top of this, Mrs. Reasonable also believes there is a relationship between the rate of economic growth and EU disillusionment. Does Mrs. Reasonable think that Mr. Nationalist overestimates or underestimates the impact EU disillusionment on the rise of the far-right? What about for Ms. Economy? Explain using the omitted variable bias formula. Statistical Research and Critical Thinking Sample Essay
The omitted variable bias is a statistical formula that is normally experienced when a mathematical model is created from a process that leaves out or omits important factors and elements (Roncek and Swatt, 2016). The discussion in this case is centered in the impact of supporting the far-rights parties within Europe. In the views of Mrs. Reasonable, economic growth and disillusionment both play a fundamental role in influencing the popularity of these political parties. However, Mr. Nationalist does not agree with her claims as he believes that disillusionment is the only important factor that affects the rise in popularity of far-right parties in Europe.
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On the other hand, Ms. Economy strongly believes that the low rate of economic growth is what has resulted in this situation altogether. The reasoning of both Ms. Economy and Mrs. Reasonable has been done using the omitted variable bias where each of them totally disregards one factor to arrive at their conclusion. Therefore, their final declaration cannot be deemed correct because it is not possible to make an argument by considering one side alone. A feasible and practical argument or thesis can only be formed once different factors are put into consideration and evaluated in detail (Berry and Mielke, 2012). Mrs. Reasonable is correct in her quest to think that Mr. Nationalist has underestimated the impact of EU disillusionment when it comes to the rise of the far-right parties. Statistical Research and Critical Thinking Sample Essay
A researcher collected data from one (hypothetical) country across four years and noticed a positive relationship between police corruption and the level of organized crime. The researcher reasoned that police corruption may be causally related to the level of organized crime because a more corrupt police force will misallocate many of its resources, leaving it less capable of combating organized crime. Confident in his theory, he showed a colleague a table (left side). The colleague was intrigued, but thought that the researcher had left out a few important confounding variables. The colleague handed the researcher a second table (right side) and said that unless he can control for income inequality, the level of infrastructure spending, and the level of democracy, the positive relationship he found may be spurious. Should the researcher take his colleague?s advice? If so, which of his colleague?s variables should he attempt to explicitly control for and why? If not, why not? Statistical Research and Critical Thinking Sample Essay
The findings of the second colleague in this study should have been followed as they show that there are other factors that affect the relationship between organized crime and police corruption. For instance, income inequality is bound to affect the rate of crime because people without jobs are forced to resort to other means in order to provide for their daily needs (Howell et al, 2016). The researcher in this case ought to follow his colleagues advice so that he can investigate more on these variables and understand how they relate to his study topic. The variable that should be specifically chosen is income inequality because it has positive correlation with the level of crime reported in a given region (Leatham, 2012). The failure to consider this variable would weaken his argument and make it appear as though it has been formed from a prejudiced judgment. Statistical Research and Critical Thinking Sample Essay
References
Pentos, K., Luczycka, D., & Kaplon, T. (2015). The identification of relationships between selected honey parameters by extracting the contribution of independent variables in a neural network model. European Food Research & Technology, 241(6), 793-801. doi:10.1007/s00217-015-2504-0
Howell, I. P., Dorfman, P. W., & Kerr, S. (2016). Moderator Variables in Leadership Research. Academy Of Management Review, 11(1), 88-102. doi:10.5465/AMR.1986.428263
Leatham, K. R. (2012). Problems Identifying Independent and Dependent Variables. School Science & Mathematics, 112(6), 349-358.
Nimon, K., & Henson, R. K. (2015). Validity of a Residualized Dependent Variable After Pretest Covariance Adjustments: Still the Same Variable?. Journal Of Experimental Education, 83(3), 405-422. Statistical Research and Critical Thinking Sample Essay
Roncek, D. W., & Swatt, M. L. (2016). For Those Who Like Odds: A Direct Interpretation of the Logit Coefficient for Continuous Variables. Social Science Quarterly (Wiley-Blackwell), 87(3), 731-738.
Berry, K., & Mielke Jr., P. (2012). A family of multivariate measures of association for nominal independent variables. Educational & Psychological Measurement, 52(1),
Blydenburgh, J. C. (2011). Probit Analysis: A Method For Coping With Dichotomous Dependent Variables. Social Science Quarterly (Southwestern Social Sciences Association), 51(4), 889-899.
Gillespie, M. W. (2007). Log-Linear Techniques And The Regression Analysis Of Dummy Dependent Variables. Sociological Methods & Research, 6(1), 103.
lecture 2(1)
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Statistical Research and Critical Thinking Sample Essay
