To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
![]() Thesis Chapter 3 Statistical Treatment Upgrade Your BrowserFive rules in categorizing research information by Kerlinger: 1. Categories are set up according to the research problem. The categories are exhaustive. Each category is derived from one classification principle. ![]() Any categorization scheme must be one level of discourse. Coding of data Information from the questionnaire, tests, interview schedules, rating scale and many others must be transformed into coded items to facilitate tabulation of data. Tabulation of data this is done by tallying and counting the raw data to arrive at a frequency distribution and to facilitate in organizing them in a systematic order in a table or several tables. ![]() Statistical Treatment - It is a must that researchers diagnose the problem by using the appropriate statistical tool to arrive at accurate and definite interpretation of results. Incorrect Statistical Tool - Percentage is incorrect or inappropriate statistical tool to scale options due to vague interpretation of results. Univariate Statistical Treatment - The appropriate statistical tool for univariate problem is the weighted arithmetic mean and the like. Bivariate Statistical Treatment in Experimental Research - The statistical tools for bivariate problem in experimental research are t-test and linear correlation. Bivariate Statistical Treatment in Descriptive Research - The statistical tool used in bivariate descriptive research problems are z-test and linear correlation. Z-test as Bivariate Statistical Tool in Descriptive Research - Z-test between percentages. The z-test is used to determine the significant difference between two percentages of related individuals in which the data are collected through survey. Z P1 P2 PQ1N1 1N2 Multivariate Statistical Treatment - Two statistical tools used in multivariate experimental research problems with three or more variables are F-test or ANOVA, Kruskal-Wallis One-Way ANOVA and Friedmans Two-way ANOVA. F-test as Statistical Tool in Multivariate Experimental Research - F-test or two-way analysis of variance (ANOVA) involves three or more independent variables as bases for classification. Friedmans Two-Way ANOVA as Statistical Tool for Multivariate Experimental Research - Friedmans two-way analysis of variance (ANOVA) is also a statistical tool used both in experimental and descriptive research problems. The formula is as follows: Xr2 l2 (ri2) 3N(le 1) Nk (k 1) Kruskal-Wallis One-Way ANOVA as Statistical Tool for Multivariate Experimental Research Kruskal-Wallis one-way analysis of variance (ANOVA) by ranks is another statistical tool used in multivariate research problems both in experimental and descriptive researches. The formula is as follows: H 12 Ri2n 3(N 1) N(N 1) Chi-Square (X2) as Statistical Tool for Multivariate Descriptive Research Chi-Square (X2) 2 X 2 table. In chi-square (X2) 2 x 2 table or fourfold table, two discrete variables are involved to test if these variables are independent from each other. It is also called nine-fold which involves three discrete variables to test if these variables are independent from each other. Friedmans Two-Way ANOVA by Ranks as Statistical Tool used in Multivariate Descriptive Research - It is used when the data from k related samples consist of at least an ordinal scale and have been drawn from the same set of observations to different population. Kruskal-Wallis One-Way ANOVA (H) by Ranks a Statistical Tool in Multivariate Descriptive Research (Tied Observations) The formula used is: 1 - T N2 - N.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |