Business Research Report Proposal The Intelligent Quotient

Question: Describe about the Business Research Report Proposal for The Intelligent Quotient. Answer: Introduction The intelligent Quotient is the measure of the intelligence. It is calculated as the ratio of the mental age and chronological age. The intelligent quotient varies from person to person. It is measured by using different types of tests. There are so many offline and online tests available for checking the intelligent quotient. Intelligent quotient is nothing but the score derived from test for intelligence. Intelligent quotient tests are generally reliable for their use. It is also observed that there is an effect on intelligent quotient due to some factors. These factors include education, nutrition, pollution, drug and alcohol abuse, mental illness, diseases, etc (Mackintosh, 2011). From the previous studies it is also found that there is a generic effect on the intelligence quotient of the person. The IQ scores have social correlations with the school performance, job performance, income, crime, etc. Let us see this research study in detail explained in the next topics. Operational definition and Measurements Intelligent quotient is nothing but the percentage score derived from test for intelligence. We use the ratio scale of measurement for the intelligent quotient. For this research study, we are dealing with the variables such as IQ score, gender of student as male or female, country of student, income of family of student, educational performance of the student, height of the student, and weight of the student. We would use the nominal scale of measurement for the variable gender of the student. We would use the code 0 for male and 1 for female. Also, we would use the nominal scale of measurement for the variable country of the student. We would use the ratio scale of measurement for the variable income of family of student. We would use the ratio scale of measurement for the variable educational performance of the student. We would count the educational performance as an average percentage or GPA of the last three or four exams. For the variables height and weight we would use the ra tio scale of measurement. Research Methodology: Data Collection and Analysis Sampling techniques For this research study, we have to collect the data for the variables described in above topic. We would use the method of random sampling techniques for the selection of students in the class. We would choose students randomly and there would not be any bias during the selection procedure (Bickel and Doksum, 2000). Also, there would be no any gender bias during data collection process. The sample size should be adequate and we would use the sample size more than 30. If the sample size is less, then there is a possibility of getting the biased results. After selection of students from the class, we would conduct the online test for the measuring the IQ scores of the students. Also, we would collect the data for the variables such as gender of student as male or female, country of student, income of family of student, educational performance of the student, height of the student, and weight of the student. Method of analysis For this research study, we would use the proper methods of statistical data analysis for the collected data. We would use the descriptive statistics and inferential statistics for the research study regarding the variation in the intelligent scores. We would find the mean, mode, median, standard deviation, etc. for the variable intelligent scores, income of family of student, educational performance of the student, height of the student, and weight of the student. The descriptive statistics gives general idea about the nature of data and variation of the data. Also, we would use some graphical analysis for the variables under research study. We can get some basic idea about IQ by using the graphs. We would use the histograms, bar graphs, box plots for this research study. Also, we would use the inferential statistics or the testing of hypothesis for checking the different claims or hypotheses established for research study (Evans, 2004). We always use the t tests or z tests for chec king the significant difference in the population means (Casella and Berger, 2002). We would use the two sample t test for the population mean for checking the claim whether there is any significant difference in the average IQ scores of the male and female student. We would use the one way analysis of variance or ANOVA test for checking the claim or hypothesis whether there is any significant difference in the average IQ scores of the students from different countries. We would also use the correlation coefficients for checking the extent of amount of relationship exists between the different pairs of variables under study. We would find out the relationship among the two variables intelligent scores and income of the family of student, intelligent scores and height of the student, intelligent scores and weight of the student, etc. We would also use the regression model for the prediction of the intelligent quotient based on the other independent variables. For this regression mode l we would use the intelligent quotient as the dependent variable and we will use the independent variables as the gender of student as male or female, country of student, income of family of student, educational performance of the student, height of the student, and weight of the student. Research Process For this research study, first of all we would establish the research questions or the hypotheses for this research study regarding the variation of intelligent quotients among the students. We would use the method of random sampling techniques for the selection of students in the class. The biases due to sampling errors and instrumental errors would be avoided during data collection (Babbie, 2009). We would use the online IQ test for collection of the data. After collection of data, we would use the proper methods of statistical data analysis. We would use the descriptive statistics and inferential statistics for the collected data. We would use the graphical analysis for easy understanding of the facts related to the intelligent quotients. We would use the inferential statistics or testing of hypothesis for checking the different claims developed for this research study regarding the variation in IQ scores among students. We would use the t tests instead of z tests because we do no t have any prior knowledge regarding the variation or population standard deviations for the IQ scores (Cox and Hinkley, 2000). After this statistical analysis we would find out the conclusions for this research study based on statistical analysis. We would use the decision rule for rejecting or do not rejecting the hypothesis (Dobson, 2001). We use the comparison of the critical values and test statistic values or the p-values and alpha value for taking decisions regarding hypotheses (Liese and Miescke, 2008). Expected Research Outcomes The conclusions for any research study should be drawn carefully from the statistical analysis (Degroot and Schervish, 2002). For this research study, it is expected that there would be no any significant difference in the average IQ scores of the male and female students. We would also expect that there would be no any significant difference in the IQ scores of the students from different countries. We expect small extent of relationship exists between the IQ scores and the income of the family. We would also expect the relationship between the educational performance and IQ scores of the students. We would not expect any relationship between the dependent variable IQ score and height or weight of the students. References Babbie, E, R, 2009, The Practice of Social Research, Wadsworth. Bickel, P, J, and Doksum, K, A, 2000, Mathematical Statistics: Basic Ideas and Selected Topics, Vol I, Prentice Hall. Casella, G, and Berger, R, L, 2002, Statistical Inference, Duxbury Press. Cox, D, R, and Hinkley, D, V, 2000, Theoretical Statistics, Chapman and Hall Ltd. Degroot, M, and Schervish, M, 2002, Probability and Statistics, Addison - Wesley. Dobson, A, J, 2001, An introduction to generalized linear models, Chapman and Hall Ltd. Evans, M, 2004, Probability and Statistics: The Science of Uncertainty, Freeman and Company. Liese, F, and Miescke, K, 2008, Statistical Decision Theory: Estimation, Testing, and Selection, Springer. Mackintosh, N.J. (2011) IQ and human intelligence. 2nd edn. New York: Oxford University Press.