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- 1. DATA ANALYSIS Dr. Jayesh Patidar www.drjayeshpatidar.blogspot.com
- 2. Introduction to Data Analysis Why do we analyze data? Make sense of data we have collected Basic steps in preliminary data analysis Editing Coding Tabulating www.drjayeshpatidar.blogspot.com 2
- 3. Introduction to Data Analysis Editing of data Impose minimal quality standards on the raw data Field Edit -- preliminary edit, used to detect glaring omissions and inaccuracies (often involves respondent follow up) Completeness Legibility Comprehensibility Consistency Uniformity www.drjayeshpatidar.blogspot.com 3
- 4. Introduction to Data Analysis Central office edit More complete and exacting edit Best performed by a number of editors, each looking at one part of the data Decision on how to handle item non-response and other omissions need to be made List-wise deletion (drop for all analyses) vs. case-wise deletion (drop only for present analysis) www.drjayeshpatidar.blogspot.com 4
- 5. Introduction to Data Analysis Coding -- transforming raw data into symbols (usually numbers) for tabulating, counting, and analyzing Must determine categories Completely exhaustive Mutually exclusive Assign numbers to categories Make sure to code an ID number for each completed instrument www.drjayeshpatidar.blogspot.com 5
- 6. Introduction to Data Analysis Tabulation -- counting the number of cases that fall into each category Initial tabulations should be preformed for each item One-way tabulations Determines degree of item non-response Locates errors Locates outliers Determines the data distribution www.drjayeshpatidar.blogspot.com 6
- 7. Preliminary Data Analysis Tabulation Simple Counts For example Number of Cars 1 74 families in the study own 1 car 2 families own 3 Missing data (9) 1 Family did not report Not useful for further analysis Number of Families 75 2 23 3 9 2 1 Total 101 www.drjayeshpatidar.blogspot.com 7
- 8. Preliminary Data Analysis Tabulation Compute Percentages Eliminate non-responses Note – Report without missing data Number of Cars 1 Number of Families 75% 2 23% 3 Total 2% 100 www.drjayeshpatidar.blogspot.com 8
- 9. Preliminary Data Analysis Cross Tabulation Simultaneous count of two or more items Note marginal totals are equal to frequency totals Allows researcher to determine if a relationship exists between two variables Number of Cars Lower Income Higher Income 1 48 27 75 2 or More 6 19 25 Total 54 46 Total 100 Used a final analysis step in majority of real-world applications Investigates the relationship between two ordinal-scaled variables www.drjayeshpatidar.blogspot.com 9
- 10. Preliminary Data Analysis To analyze the data Calculate percentages in the direction of the “causal variable” Does number of cars “cause” income level? Lower Income Higher Income Total 1 64% 36% 100% 2 or More 24% 76% 100% Total 54% Cross Tabulation 46% 100% Num ber of Cars www.drjayeshpatidar.blogspot.com 10
- 11. Preliminary Data Analysis Cross Tabulation To analyze the data Does income level “cause” number of cars? Seem like this is the case. In the direction of income – thus, income marginal totals should be 100% Lower Income Higher Income 1 89% 59% 75% 2 or More 11% 41% 25% Num ber of Cars Total Total 100% 100% 100% www.drjayeshpatidar.blogspot.com 11
- 12. Preliminary Data Analysis Cross Tabulation allows the development of hypotheses Develop by comparing percentages across Lower income more likely to have one car (89%) than the higher income group (59%) Higher income more likely to have multiple cars (41%) than the lower income group (11%) Are results statistically significant? To test must employ chi-square analysis www.drjayeshpatidar.blogspot.com 12
- 13. Measurement Scales & Types of Data Types of Data Discrete Continuous Nominal Ordinal Interval Ratio The Assignment of Numbers for Classification Purposes; Categorical Data Quantitative Values Providing a Classification According to Order or Magnitude Classification According to a Continuum With Interval Equality & Subdivision Sensibility Interval Data With An Absolute Value of 0 Eg: Temp. Eg: Height; weight Eg: VAS; SE Status E.g. Sex, Blood Gr www.drjayeshpatidar.blogspot.com 13
- 14. Statistical Tests: Overview Type of data Kind of comparison distribution two samples Comparison of two one test, groups sample Data Qualitative Quantitative Normal distribution Any 2-test, t-Test , Z test Z test (n>30) for proportion sign-test, one sample Mc.Nemar-test t-Test Wilcoxon;MannWhitney-test Chi Square signone-sample Wilcoxon-test Comparison independ. 2-test one-way analysis KruskalWallis-test of more samples of variance than two one Cochran’s two-way analysis Friedman-test groups sample Q-test of variance 14 www.drjayeshpatidar.blogspot.com
- 15. www.drjayeshpatidar.blogspot.com 15
- 16. www.drjayeshpatidar.blogspot.com 16

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