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analysis plan.ppt

  1. 1. 1 DATA PROCESSING & ANALYSIS PLAN OLUSINA BAMIWUYE (PH.D)
  2. 2. Learning Objectives  Identify important issues related to quality control and processing of data.  Describe how data can be best be analysed and interpreted based on the objectives and variables of the study.  Prepare a plan for the processing and analysis of data (including data master sheets and dummy tables) for the research proposal you are developing. 2
  3. 3. 3 NATURE OF STATISTICAL DATA  Statistics refers to a body of logic or techniques for collecting, organizing, analyzing and interpreting statistical data.  Statistical data are facts expressed either in quantitative or qualitative form
  4. 4. 4 Types of Data  Primary and Secondary data - Primary data: Data originally collected in the process of any statistical inquiry - Secondary data: Data collected by other individual/people/organization  Demographers prefer primary source to secondary source.
  5. 5. VARIABLES Quantitative Vs Qualitative  Any phenomenon with difference in magnitude Types of Variables  Quantitative – numbers, percent, means..  Qualitative- explore why?, how?  Quantitative: Weight, Height, BMI, SBP, DSP, age, age at first marriage, CEB etc.  Qualitative: Reason for not using contraceptive method; 5
  6. 6. PURPOSE OF DATA ANALYSIS  Provide answers to research questions being studied - Distributional Characteristics of data - Variance in the data - Differences within the data - Relationships between/among variables
  7. 7. Why Analysis Plan? (1)  Such a plan helps the researcher assure that at the end of the study:  all the information (s)he needs has indeed been collected, and in a standardised way;  (s)he has not collected unnecessary data which will never be analysed.  Provides you with better insight into the feasibility of the analysis to be performed as well as the resources that are required.7
  8. 8. Why Analysis Plan? (2)  Provides an important review of the appropriateness of the data collection tools for collecting the data you need. 8
  9. 9. Guide in Preparing Analysis Plan  The plan for data processing and analysis must be made after careful consideration of the objectives of the study as well as of the tools developed to meet the objectives.  The procedures for the analysis of data collected through qualitative and quantitative techniques are quite different. 9
  10. 10. What should the plan include? When making a plan for data processing and analysis the following issues should be considered:  Sorting data,  Performing quality-control checks,  Data processing, and  Data analysis. 10
  11. 11. Sorting of Data (1)  An appropriate system for sorting the data is important for facilitating subsequent processing and analysis.  If you have different study populations (for example village health workers, village health committees and the general population), you obviously would number the questionnaires separately. 11
  12. 12. Data Sorting (2) In a comparative study it is best to sort the data right after collection into the two or three groups that you will be comparing during data analysis. eg:  Users and Non-Users of Family Planning  rural and urban samples  in a case-control study obviously the cases are to be compared with the controls. Ensure you number the questionnaires in each of these categories separately 12
  13. 13. Quality Control Checks For completeness and internal consistency:  On the Spot Field Editing before processing  Office Editing  A decision to exclude data of doubtful quality is ethically correct and it testifies to the scientific integrity of the researcher.  keep track of any questions you had to exclude because of incompleteness or inconsistency in the answers, and discuss it in your final report. 13
  14. 14. Data Processing Stages of Data Processing:  Categorising  Coding  Data entry  Data validation Decision must be made on each stage outlined above before analysis of data 14
  15. 15. Data Analysis  Descriptive:describes the problem under study.  Analytic: Groups are compared to determine differences, or explore relationships between variables.  A descriptive cross-tabulation would, for example, relate smoking behaviour to sex or occupational background  An analytic cross-tabulation serves to investigate relationship between variables 15
  16. 16. Construction of Dummy Tables  When the plan for data analysis is being developed the data, of course, is not yet available. However, in order to visualise how the data can be organised and summarised it is useful at this stage to construct DUMMY TABLES.  A DUMMY TABLE contains all elements of a real table, except that the cells are still empty. 16
  17. 17. Dummy Frequency Table of Percentage Distribution of Respondents by Age Age Frequency Percentage 15-19 20-24 25+ Total 100.0 17
  18. 18. Dummy Crosstab: Residence and Contraceptive use RESIDENCE Currently using any method of contraceptive N (%) Not currently using any contraceptive N (%) Total Urban Semi-Urban Rural Total Chi-square= ****; df= ********; p< ***** 18
  19. 19. Hints in Constructing Dummy Crosstabs  If a dependent and an independent variable are cross-tabulated, the headings of the dependent variable are usually placed horizontally  All tables should have a clear title and clear headings for all rows and columns.  All tables should have a separate row and a separate column for totals to enable you to check if your totals are the same for all variables and to make further analysis easier. 19
  20. 20. ANALYSIS PLAN FOR QUALITATIVE DATA  a decision on whether all or some parts of the data should be processed by hand or computer;  dummy tables for the description of the problem guided by the objectives of the study;  a decision on how qualitative data should be analysed;  an estimate of the total time needed for analysis  an estimate of the total cost of the analysis. 20
  21. 21. 21 Quantitative Data Analysis Software  EPI-INFO  SPSS  SAS  ISSA  Statistica  STATA etc
  22. 22. Qualitative Software  Text Base Alpha  Text Base Beta  CDC EZ-Text  Ethnograph  Nnvivo 22
  23. 23. The use of ZY index table Though a manual analysis of qualitative data but the method is quite useful. It is a way of summarizing the result of qualitative data into tables by using themes and sub-themes in a study without attempting to use numbers or percents (quantification) 23
  24. 24. Example of ZY Index Table Key questions LGA 1 (MALE) LGA 1 (FEMALE) LGA 2 (MALE) LGA 2 (FEMALE) Major theme 1 ++ + - + Major theme 2 - ++ + + Major theme 3 ++ ++ ++ ++ Major theme 4 + + + - Major theme 5 - ++ ++ + - opinion not expressed at all + opinion expressed by not less than 2 respondents ++ opinion expressed by at least 3 respondents 24
  25. 25. Some Quick Guide in Choosing Appropriate Statistics.  Possible effect a single non-metric independent variable (factor) on a metric dependent variable – One Way Analysis of Variance.  Simultaneous effects of ‘n’ factors (non-metric) on a metric dependent variable – “n” way analysis of variance.  Possible effects of both metric and non-metric independent variables on a single metric dependent variable – analysis of covariance.  Possible effect of one metric independent variable on a single metric dependent variable – bivariate regression analysis 25
  26. 26. Some Quick Guide in Choosing Appropriate Statistics (2)  Possible effects of two or more metric variables on metric dependent variable – multiple regression  Extent of relationship between 2 metric variables – Pearson Correlation coefficient  Possible effects of metric and non-metric dummy variables on dichotomous dependent variable – binary logistic regression analysis  Possible effects of metric and non-metric dummy variables on dependent variable with more than two categories – multinomial logistic regression analysis 26
  27. 27. Some Quick Guide in Choosing appropriate Statistics (3)  Possible association between two categorical variables – chi square test  Differences between a metric dependent variable and a non-metric (2 categories) – Independent T-test  Test of differences between two metric variables in a pre-post test design –paired t- test. 27
  28. 28. Some Quick Guide in Choosing Appropriate Statistics (4)  Measuring the relative importance of the metric independent variables on a metric dependent variable – Beta Coefficient  Measuring the proportion of variations in a metric dependent variable explained by metric independent variables – coefficient of multiple determinations (R-Square). 28
  29. 29. 29 Possible association among three or more categorical variables – loglinear analysis Nonparametric alternative to ANOVA(K>2) – Kruskal Wallis Nonparametric alternative to independent T- test – Mann Whitney Nonparametric alternative to paired T-test - Wilcoxon Sign-ranked test Some Quick Guide in Choosing appropriate Statistics (5)
  30. 30. 30 THANK YOU FOR LISTENING

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