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Promoting Good Statistical Practice

Promoting Good Statistical Practice. Roger Stern Parin Kurji SSC, Reading BUCS, Nairobi. Contents. Understanding the present situation: Training in statistics Developments in statistical computing And in statistical analyses Possibilities for the future Resources

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Promoting Good Statistical Practice

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  1. Promoting Good Statistical Practice Roger Stern Parin Kurji SSC, Reading BUCS, Nairobi

  2. Contents • Understanding the present situation: • Training in statistics • Developments in statistical computing • And in statistical analyses • Possibilities for the future • Resources • statistical software (freely available in Africa) • materials to promote good statistical practices • training materials • In conclusion • These are exciting times - let’s look forwards not backwards PROMOTING GOOD STATISTICAL PRACTICE

  3. Training in statistics • It is difficult to practice good statistics • unless we have had appropriate training • Training for (non-statistician) users in the past has been problematical • consequently they fear statistics • andhence also statisticians • Similarly, insufficient soft training for statisticians • consequently they sometimes lack communication skills • and marketing skills • and are often side-lined in important development and researchprojects PROMOTING GOOD STATISTICAL PRACTICE

  4. Common training problems(for non-statisticians) • Training dominated by analysis • with little on data management • or on design • A recipe-book approach used • hence e.g. overuse of irrelevant significance tests • little understanding of principles • Emphasis on hand computation • for understanding (which they don’t get!) • but not needed later • and little experience of computers for statistical work PROMOTING GOOD STATISTICAL PRACTICE

  5. Common training problemscont. Additionally • Presentation too mathematical • not conceptual • AND often taught by someone who has little interest in the student’s main subject areas PROMOTING GOOD STATISTICAL PRACTICE

  6. RESULT! • Graduates with near universal dislike of statistics • and statisticians? • Strong demand for relevant in-service training in statistics Most of these past weaknesses in training are the same for statisticians • who can be too pedantic and inflexible in their advice • and are then feared and ignored, where possible, by potential clients We see later how this can now easily change • for both statisticians • and for others who need to generate and use statistics PROMOTING GOOD STATISTICAL PRACTICE

  7. Advances in statistical computing • History • 1960’s SASand SPSS started • A long way back in computer terms • 1971 GenStat started • 1972 Generalised linear models and GLIM • By early 1980’s • Statistics packages well established • Micro-computers appeared – too small for these packages • So lots of other statistics packages • that made the same mistakes as SAS and SPSS a generation earlier • it is easy to write statistical software, but difficult to write good software PROMOTING GOOD STATISTICAL PRACTICE

  8. Statistics packages : THEN • Early 1990’s • Standard statistics packages dominant again • compare other types of software • with some additions e.g. Stata, S-PLUS • All command-driven • So you had to learn the language (for SPSS, or SAS, or S-PLUS) • So people and training courses used just one package • Data transfer between packages was difficult • Training courses often confused • learning the package with learning statistics • c.f. data management – learning concepts or learning Access PROMOTING GOOD STATISTICAL PRACTICE

  9. A big advance….. Windows appeared & EXCELruled the world for better for worse! PROMOTING GOOD STATISTICAL PRACTICE

  10. Statistics packages : NOW • All common packages are in Windows • Very similar interface • So very easy to learn • And to add to Excel • so you can still keep your “security blanket” • And easy to add another package • hence not so critical what package is used for statistics training • Data transfer has also become easy Hardly need a training course for the software • so can concentrate on training in statistics again! PROMOTING GOOD STATISTICAL PRACTICE

  11. Gaps • Some statisticians and researchers apparently use just Excel (for statistics work) • They need to add a statistics package to be able to analyse their own data completely • Menus and dialogues are often not enough for a professional statistician and researcher; • they need language (commands) sometimes which • helps efficiency • and keeps a record PROMOTING GOOD STATISTICAL PRACTICE

  12. Necessary approach • Can use many (statistics) packages • but be comfortable with the language for one of them • can still use Excel as well ! Discussion of Software Strategy to evaluate appropriate software to use PROMOTING GOOD STATISTICAL PRACTICE

  13. Advances in statistical analysis • The “estuary model” • ever-increasing unity to the methods • this makes training much easier • if we build a solid foundation • special methods are then seen as such PROMOTING GOOD STATISTICAL PRACTICE

  14. Start in 1960’s • In the mountains there were little streams • Regression and • Analysis of variance • These were for normally distributed data • In another valley • parameter estimation was for other distributions, like Poisson and binomial • And leading to another valley • the chi square analysis for categorical data PROMOTING GOOD STATISTICAL PRACTICE

  15. Then • In the late 1960’s • Chi-square tests joined with other ways of looking at multidimensional contingency tables • to become log-linear models • In the early 1970’s • log-linear models joined probit analysis • into the general stream of generalized linear models • that also included ANOVA and regression • for normal and non-normal data PROMOTING GOOD STATISTICAL PRACTICE

  16. And finally for us here • In the 1980’s • REML started • and is for data at multiple levels • By the 1990’s • it had joined the mainstream • and included methods for spatial modelling So now • same modelling ideas used for a wide range of problems • Making both training and analysis • simpler and more coherent • as long as the trainers know. BUT some are still up in the mountains! PROMOTING GOOD STATISTICAL PRACTICE

  17. So where are we now? • Statistical software has developed • and so has user’s computing skills • Statistical methods have developed • and are easier to use • And the resources to bring the two together • are now being made available • and are becoming accessible throughout We describe some of these resources PROMOTING GOOD STATISTICAL PRACTICE

  18. Software includes: • SSC-Stat • add-in for Excel to encourage good use • with a tutorial guide and other guides • Example: boxplots • Instat+ • first simple statistics package for ‘Excel-lers’ • stepping stone to other statistics packages • Various guides (tutorial, introductory, climatic) • Example for training • GenStat Discovery Edition • major statistics package • developed at Rothamsted (home of RA Fisher &GLMs) • promotes good statistical practice • free in Africa since late 2003 – licences from ICRAF, Nairobi (also possible from BUCS & ILRI) PROMOTING GOOD STATISTICAL PRACTICE

  19. Resources for good statistical practice • Books, guides, notes etc • Mini-guidesfor statistical sceptics • covering design, data management analysis and presentation Now a book : ‘Good Statistics Practice for Natural Resources Research’ • Working papers: e.g Participatory (QQA) • The Green Book • a guide to effective research • Accessible online notes ( from ICRAF) • Agro forestry experimentation • data management • Links • Through appropriate CDs or websites PROMOTING GOOD STATISTICAL PRACTICE

  20. Training resources include • Statistical games to help teach statistics • Reading and BUCS • For example PADDY, the rice survey game • Materials for distance learning • Now CASTin general • But can now be adapted for African needs • Again with support from the Rockefeller Foundation PROMOTING GOOD STATISTICAL PRACTICE

  21. Interesting ways of learning: Training Software • Statistics concepts through CAST PROMOTING GOOD STATISTICAL PRACTICE

  22. Interesting ways of learning: Statistical Games • Simulating a survey based on a real survey in Sri Lanka PROMOTING GOOD STATISTICAL PRACTICE

  23. In conclusion • The time is right: • Statistics has changed – as we have heard here • The resources are here – we have seen some here • Training methods can change –how ? • Challenge: • Evidence-based decision making? • Good statistical practice? • Adequate support for quality research? • Goal: • To make the dream come true……. PROMOTING GOOD STATISTICAL PRACTICE

  24. Dream.. • To help in the development of Africa through a ripple effect – touching an ever extending number of people producing quality statistical work PROMOTING GOOD STATISTICAL PRACTICE

  25. time to come down from the mountains ?? let us discuss how…….

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