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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 www.drjayeshpatidar.blogspot.com 2
  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 www.drjayeshpatidar.blogspot.com 3
  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) www.drjayeshpatidar.blogspot.com 4
  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 www.drjayeshpatidar.blogspot.com 5
  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 www.drjayeshpatidar.blogspot.com 6
  7. 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. 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. 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. 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. 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. 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. 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. 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
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