Cross-tabulation is one of the most useful analytical tools and a mainstay of the market research industry. Cross-tabulation analysis, also known as contingency table analysis, is most often used to analyze categorical (nominal measurement scale) data. For reference, a cross-tabulation (or crosstab) is a two- (or more) dimensional table that records the number (frequency) of respondents that have the specific characteristics described in the cells of the table. Cross-tabulation tables provide a wealth of information about the relationship between the variables. Cross-tabulation analysis goes by several names in the research world including crosstab, contingency table, chi-square and data tabulation. Cross-tabulation analysis has its own unique language, using terms such as “banners”, “stubs”, “Chi-Square Statistic” and “Expected Values.” When conducting survey analysis, cross tabulations (also referred to as cross-tabs) are a quantitative research method appropriate for analyzing the relationship between two or more variables. Cross tabulations provide a way of analyzing and comparing the results for one or more variables with the results of another (or others). The axes of the table may be specified as being just one variable or formed from a number of variables. The resulting table will have as many rows and columns as there are codes in the corresponding axis specification. In many research reports, survey results are presented in aggregate only – meaning, the data tables are based on the entire group of survey respondents. Cross tabulations are simply data tables that present the results of the entire group of respondents as well as results from sub-groups of survey respondents.

This is a-2 unit course covering the following syllabus: Electronic Survey Design Online Methods of Data Collection, Questionnaire Administration, Electronic Analyses of Responses, and Electronic Reporting Techniques. Simulation of Survey Datasets Analyses of simulated Survey Data with R: Simulating the data, Exploring the Data, Presenting the Output in SPSS Format, Graphing and Visualizing the Results using ggplot Command. Sampling Theory (Part I) Simple Random Sampling with Replacement, Simple Random Sampling Without Replacement using R Software, Stratified Random Sampling, Systematic Sampling, Cluster Sampling, etc Sampling Theory (Part II) Writing self-codes for the theoretical aspects of the Survey Methods and Sampling Theory Analyses of Raw Likert Survey Data Importing and Exporting Raw Datasets from Excel and SPSS into R for analyses and interpretation

This is to teach modelling

This would serve as your note for the class