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Simple Article Critique Wk10
Assignment: Article Critique
Assignment 1: The practice of quantitative research not only involves statistical calculations and formulas but also involves the understanding of statistical techniques related to real-world applications. You might not become a quantitative researcher nor use statistical methods in your profession but as a consumer, citizen, and scholar-practitioner, it will be important for you to become a critical consumer of research, which will empower you to read, interpret, and evaluate the strength of claims made in scholarly material and daily news.
For this Assignment, you will critically evaluate a scholarly article related to reliability, validity, and scale analysis.
To prepare
The Assignment
Writea 2- to 3-pg critique of the research you found in the Walden Library that includes responses to the following prompts:
Note: I attached an example of how it should look choose your own articles and cite all areas appropriately
Running head: ARTICLE CRITIQUE 1 Article Critique –
Name –
Class – RSCH
Professor –
Summer Term ARTICLE CRITIQUE 2
Assignment 2
Binary Logistic Regression Predicting Social Trust with Binary Logistic Regression
Article: Adwere-Boamah, J., & Hufstedler, S. (2015). Predicting Social Trust with Binary
Logistic Regression. Research in Higher Education Journal, 27.
Introduction
Predicting Social Trust with Binary Logistic Regression. An article Adwere-Boamah, and
Hufstedler (2015), seeks to identify the determinants of social trust on various individual adults
of a particular nation sample. The study used binary logistic regression in order to evaluate the
level of social trust individual adults using the five demographic variables identified as sex, race,
level of education, general happiness and the importance of personally assisting people
experiencing trouble. The research based its evaluation and study on sample individual adults
who participated in the General Social Survey (GSS).
Summary evaluation
The study identified the role each of the predictors/variables played in determining social
trust. The findings on the level of education in relation to the level of social trust in an individual
revealed that the level of education is the strongest predictor of low social trust as compared to
other variables/predictors. A school drop out for example, is likely to have a low social trust as
compared to an individual who is a degree graduate. This finding reveals how the level of
education had a profound effect on the social trust of a particular individual. The study also
established that race as a predictor had a fair share of influence on the social trust of a particular
individual such that the African American had a low social trust as compared to the whites. In ARTICLE CRITIQUE 3 relation to sex, females tend to be less trustful as compared to males. Happy people on the other
hand are more trustful than those people who are often less happy (Adwere-Boamah, &
Hufstedler, 2015). In summary, all the five variables displayed some impact on social trust as it
either affected one Social trust negatively or positively. Therefore, binary logistic regression is
multifaceted on various variables/predictors.
Appropriateness of the test
The binary logistic regression testing has proven in this case that it is appropriate since it
applied various dimensions in evaluating social trust of various adults. It applies various means
of estimating various parameters thus by application of these variables, it is then able to realize
better, reliable and efficient results. The application of the five predictors in this scenario makes
it appropriate as it makes it a multidimensional approach to establishing the factors affecting
social trust and how these variables affect the trust of particular individuals (Adwere-Boamah, &
Hufstedler, 2015). The choice to study specific individuals also makes the test achieve reliable
results as it widens the scope of study for the test thus making it quantifiable and efficient tool of
analysis. An application of data from the General Social Survey (GSS) also enables the tests to
achieve credible and reliable results thus making the test appropriate.
Use of figures and tables
The authors of this journal did not approach tabling as one of the strategy of delivery of
data though it analyzed its data in figures and ratios which also proved effective. An example is
the use of the 12.7 ratio scale to estimate the number of times an individual with low educational
level is going to have less social trust as compared to an individual with a higher educational
level such as a degree graduate who is likely to have 12.7 times more social trust. The use of ARTICLE CRITIQUE 4 figures and ratios helped the authors develop understanding in the reader of the article and thus
able to comprehend the relationship between the variables such as sex, race, general happiness,
the significance of an individual helping other individuals experiencing trouble and the level of
education of that individual in relation with their social trust (Harrell, 2015).
Presentation of results
The results of the test on “Predicting Social Trust with Binary Logistic Regression” stand
out and appear to be more precise, clear and easy to interpret therefore effective for testing. The
results clearly indicate the relationship between the distinct variables/predictors and how these
variables affect the social trusts of particular individuals. By approaching a multidimensional
approach to the study, the test therefore can achieve reliable and credible results (Allison &
Allison, 2012).
Conclusion
After a closer view and consideration into the effectiveness of the binary logistic
regression testing it is reasonable to justify that it is indeed effective and efficient in achieving
better and reflexive results thus I would recommend for this kind of testing for researchers. ARTICLE CRITIQUE 5
References Adwere-Boamah, J., & Hufstedler, S. (2015). Predicting Social Trust with Binary Logistic
Regression. Research in Higher Education Journal, 27.
Allison, P. D., Allison, P. D., & SAS Institute. (2012). Logistic regression using SAS: Theory and
application. Cary, NC: SAS Institute.
Harrell, F. E. (2015). Case Study in Binary Logistic Regression, Model Selection and
Approximation: Predicting Cause of Death. Regression Modeling Strategies, 275-289.
doi:10.1007/978-3-319-19425-7_11
Attachments:
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