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Data analysis

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Data analysis

  1. 1. DATA ANALYSIS Dr. Jayesh Patidar www.drjayeshpatidar.blogspot.com
  2. 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 2www.drjayeshpatidar.blogspot.com
  3. 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 3www.drjayeshpatidar.blogspot.com
  4. 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) 4www.drjayeshpatidar.blogspot.com
  5. 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 5www.drjayeshpatidar.blogspot.com
  6. 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 6www.drjayeshpatidar.blogspot.com
  7. 7. Preliminary Data Analysis  Tabulation  Simple Counts  For example  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 Cars Number of Families 1 75 2 23 3 2 9 1 Total 101 7www.drjayeshpatidar.blogspot.com
  8. 8. Preliminary Data Analysis  Tabulation  Compute Percentages  Eliminate non-responses  Note – Report without missing data Number of Cars Number of Families 1 75% 2 23% 3 2% Total 100 8www.drjayeshpatidar.blogspot.com
  9. 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  Used a final analysis step in majority of real-world applications  Investigates the relationship between two ordinal-scaled variables Number of Cars Lower Income Higher Income Total 1 48 27 75 2 or More 6 19 25 Total 54 46 100 9www.drjayeshpatidar.blogspot.com
  10. 10. Preliminary Data Analysis  Cross Tabulation  To analyze the data  Calculate percentages in the direction of the “causal variable”  Does number of cars “cause” income level? Num ber of Cars Lower Income Higher Income Total 1 64% 36% 100% 2 or More 24% 76% 100% Total 54% 46% 100% 10www.drjayeshpatidar.blogspot.com
  11. 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% Num ber of Cars Lower Income Higher Income Total 1 89% 59% 75% 2 or More 11% 41% 25% Total 100% 100% 100% 11www.drjayeshpatidar.blogspot.com
  12. 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 12www.drjayeshpatidar.blogspot.com
  13. 13. Types of Data ContinuousDiscrete Nominal The Assignment of Numbers for Classification Purposes; Categorical Data E.g. Sex, Blood Gr Ordinal Quantitative Values Providing a Classification According to Order or Magnitude Eg: VAS; SE Status Interval ClassificationAccording to a Continuum With Interval Equality & Subdivision Sensibility Eg: Temp. Ratio Interval Data With An Absolute Value of 0 Eg: Height; weight Measurement Scales & Types of Data 13www.drjayeshpatidar.blogspot.com
  14. 14. Type of Data data Kind of Quantitative comparison Qualitative Normal distribution Any distribution two 2-test, t-Test , Z test Wilcoxon;Mann- samples Z test (n>30) Whitney-test Comparison for proportion Chi Square of two one sign-test, one sample sign- test, groups sample Mc.Nemar-test t-Test one-sample - Wilcoxon-test Comparison independ. 2-test one-way analysis Kruskal- Wallis-test of more samples of variance than two one Cochran’s two-way analysis Friedman-test groups sample Q-test of variance Statistical Tests: Overview 14www.drjayeshpatidar.blogspot.com
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