The Practice of Social and Psychological Report Order Instructions: The writer will have to read each of this post and react to them by commenting, analyzing and supporting his reactions with relevant articles.
The writer will have to read carefully before giving constructive comments on the article. The writer should write one paragraph of at least 150 words. APA and in-text citation must be used as each respond to the two posts must have in-text citations. The writer will have to use an article to supports his comments in each of the articles. Address the content of each post below in one paragraph each, analysis and evaluation of the topic, as well as the integration of relevant resources.
Please, it is important that the writer use in-text citations for each of the articles and it must be a peer review article.
The Practice of Social and Psychological Report Sample Answer
The writer will have to read each of this post and react to them by commenting, analyzing and supporting his reactions with relevant articles. The writer will have to read carefully before giving constructive comments on the article. The writer should write one paragraph of at least 150 words. APA and in-text citation must be used as each respond to the two posts must have in-text citations. The writer will have to use an article to supports his comments in each of the articles. Address the content of each post below in one paragraph each, analysis and evaluation of the topic, as well as the integration of relevant resources.
To predict behavior, researchers need to measure behavior. Because it is difficult to observe behavior, reliable measurement is difficult. Reliability and Item analysis allow researchers to construct scales of measurement, improve existing scales and to evaluate scale reliability. A reliability assessment is based on the correlation between the items that define the scale (Green &Salkind, 2014).
(Schriesheim& Kerr, 1974) Schriesheim& Kerr (1974) assessed the validity, reliability, and scaling of different versions of the Ohio State leadership scales; leadership opinion Questionnaire (LOQ), Leader behavior description questionnaire (LPDQ), and supervisory behavior description questionnaire (SBDQ). Their assessment identified a few flaws in the validity of these scales. All of these scales have a high response skewness and have high correlations. The instruments show issues with scaling. A sufficient number of reflected structure items do not exist. The response intervals show that the Ohio State leadership scales produce ordinal data instead of interval data. Internal consistency tests indicate that these leadership scales meet acceptable reliability standards (Schriesheim& Kerr, 1974).
Current research in leadership does not adequately explain the leadership potential of college students. Early attempts by Karnes and Chauvin (1985) identified some criteria to evaluate leadership potential. Although these areas are a good starting point further research is a need to identify all areas of leadership potential in college students. S. Lee et al. (2015) developed a measurement scale for leadership potential in Korean undergraduate students. A random group of 13 leaders with at least five years’ experience was used to test sample questions. The data obtained was tested to determine if the psychometric qualities of the proposed leadership potential scale. A confirmatory factor analysis confirmed construct validity. As a result, of the statistical analysis, a 12 factor model for evaluating leadership potential in Korean undergraduate students was produced. Although the scale passed initial validity, further tests are needed to confirm reliability (S. Lee et al., 2015).
The Practice of Social and Psychological Report References
Green, S. B., &Salkind, N. J. (2014). Using SPSS for windows and Macintosh (7th ed.). Upper Saddle River, NJ: Pearson.
Lee, S., Kim, H., Park, S., Lee, S., & Yu, J. (2015). Preliminary development of a scale to measure leadership potential. Psychological Reports, 117, 51-71. doi:10.2466/01.07.PR0.117c13z4
Schriesheim, C., & Kerr, S. (1974). Psychometric properties of the Ohio State leadership scales. Psychological Bulletin, 81(11), 756-765. doi:10.1037/h0037277
Include the one paragraph comments hear using and a peer review article to support your comments. Also, include in-text citations in APA.
It is indeed important for the researcher to develop appropriate measurement scales that are reliable and valid. Measuring behaviors is one way of helping an individual to understand the potential of a person. I concur with this observation that sometimes it is difficult to come up with reliable measuring scales. Nevertheless, the fact that different scales such as those for measuring leadership exist, it points out that, there is still a room for improvement (Schriesheim & Kerr, 1974). This as well points out that the available tools may not be reliable per se. These flaws are manifest in many of the Ohio leadership scales that have some flaws. On the same note, the article has acknowledged that the scales may present other flaws such as skewness, high correlations, and validity issues. Therefore, it is very important that researchers invest their time to test these scales for them to rely on them. In general, the article is precise and concise. It is objective as it has incorporated or supported the arguments with references.
Correlation provides a “unitless” measure of association between two variables, ranging from −1 (indicating perfect negative association) to zero (no association) to one (perfect positive association). Both variables are treated equally in that neither is considered a predictor nor an outcome (Crawford, 2006). Regression is particularly useful to understand the predictive power of the independent variables on the dependent variable once a causal relationship has been confirmed (O’Brien, & Scott, 2012). According to O’Brian and Scott (2012), regression helps a researcher understand to what extent the change of the value of the dependent variable causes the change in the value of the independent variables, while other independent variables are held unchanged. Regression seeks to show how a fixed variable predicts the values of the random values. In a correlation, both variables are random and the researcher shows how they change in relation to one another. Both procedures seek to show a relationship between variables (Cohen, Cohen, West, & Aiken 2013). Neither regression nor correlation analyses can be interpreted as establishing cause-and-effect relationships. They can indicate only how or to what extent variables are associated with each other. The correlation coefficient measures only the degree of linear association between two variables (Wiley, 2015). The main difference between correlation and regression is that in correlation, you sample both measurement variables randomly from a population, while in regression you choose the values of the independent (X) variable. One would use regression if you determine the X values before you do the experiment (McDonald, 2014).
The bivariate analysis involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. Bivariate analysis can be helpful in testing simple hypotheses of association. Bivariate analysis can help determine to what extent it becomes easier to know and predict a value for one variable if we know the value of the other variable (Babbie, 2009). Since using a bivariate analysis is best when dealing with multiple variables, it would be best to use this in my study since I will have multiple variables to test.
The Practice of Social and Psychological Report References
Earl R. Babbie (2009). The Practice of Social Research, 12th edition, Wadsworth Publishing, pp. 436–440
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.
Crawford, S. L. (2006). Correlation and regression. Circulation, 114(19), 2083-2088. Retrieved from http://circ.ahajournals.org/content/114/19/2083.full.
McDonald, J.H. (2014). Handbook of Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland pp 190-208. Retrieved from http://www.biostathandbook.com/linearregression.html
O’Brien, D., & Sharkey Scott, P. (2012). Correlation and Regression. Retrieved from http://arrow.dit.ie/cgi/viewcontent.cgi?article=1006&context=buschmanbk
Wiley, J. F., & Pace, L. A. (2015). Correlation and Regression. In Beginning R (pp. 121-137). Apress.
Include the one paragraph comments hear using a peer review article to support your comments. Also, include in-text citations in APA.
Understanding different concepts in statistics is expected to be in a position to analyze any given data. Researchers have to understand the relationships in their data sets, and how to analyze them statistically. This is also important as it guides them when making decisions on the best approach to testing their information. It is important to differentiate between regressions, correlation, correlation coefficient and others terms such as bivariate analysis. These concepts have been clearly discussed in the article and it is easy to understand them. Any layperson with less knowledge in statistical analysis can understand the concepts easily by reading the article. The article has as well made the clear definition and pointed out the differences between various concepts. Furthermore, the article is well formatted and is supported by enough sources. I also concur that using bivariate analysis is preferred in a situation where a researcher intends to analyze multiple variables (Babbie, 2009).