Sampling Design: Achieving Accurate Results with Efficient Workload

1. Lecture 1.2 • Sampling design • Analysis of data.

2. Sampling design, - the what, the where, and the how.. • It is never possible to compile a complete data set, what shortcuts do we take to reduce our workload and still achieve accurate results! • The method used for deciding which member of a statistical population will be included in the sample is called the sampling design.

3. Sampling design, the where? • Random or subjectively chosen plots? • Do you seek to record general or specific data? • Objectivity demands a high number of study units/plots to ensure representativity. • Complete random sampling ensures that all units have the same probability of entering the sample. Representativity Work load Objectivity

4. Sampling design, the where? • Statistical qualities of plots /study units, are the plots independent? • Are rare types represented among the samples? • Redundancy – are common types too redundant (common)? • Lumping of plots? • How could lumping of plots have effected the results in the study by Peres et al. (the study of Brazil nut)?

5. Sampling design, size and shape? 30 m • Size of plots? The size must be so that the plots are both representative, but also homogenous. • Shape of plots? Transects or quadrates? Smaller sub-quadrates within quadrates? 330 m 100m 10 000 m2 10 000 m2

6. Sampling design, how many? • How many plots / study units? • High variation requires a higher number of plots/study units. • As many as you can… • Permanent plots? • Do you want to revisit? For other scientists to revisit? • Metal bars in the soil, metal tags on trees etc., what more.. • Remember that several statistical analyses assume random sampling!

7. Sampling design, the what? qualitative and quantitative approaches. • Qualitative and quantitative approaches work together, both may be important in a study. • Qualitative approaches are useful and necessary for in depth knowledge of a situation, or when describing the study area or units of research, • Quantitative approaches is useful for more objective comparison of different systems, and may enable statistical analysis.

8. The data, quantitative approaches • Examples of explanatory variables, data to collect: soil variables (moisture, soil type, and more), aspect, shadow sun , income, parents occupation.. • Data collected /ecological variables must be tied directly to the sample plots /sample units. • All variables within a category must be quantified with the same unit in the study. • A questionnaire in social science may give quantitative variables.

9. The data, qualitative approaches. Observations, important in every discipline (ranging from non-participant to participant). • Interviews (ranging from semi-structured to open-ended). • Open ended, initial interviews • Documents • Private – public. • Audio visual (including materials such as photographs, compact disks and videotapes).

10. Analysis of data • The purpose of analysing the data is explore different, interesting characteristics inherent in the results. • Characteristics that the study units have in common. • Characteristics that the study units are different from. • Categorization is the way that something is divided up into a set off of different classes.

11. Analysis, categorization • Selecting categories. • Categorization of the data into different levels, how many levels? • Value can be assigned to different categories • Beware of units and scales, need to be the same.

12. Analysing quantitative forms of data. • The number of individuals and species. • The structure of the data set. • Quantitative data in social sciences: how many inhabitants, different age classes etc.

13. Analysis of data • Graphical presentation • Tables and figures; permits us to present a simplified version of the results. • Can be used to report precise numbers or to illustrate a trend. • A trend might be better illustrated with a figure. • Graphs, typically relate two dimensions such as quantity of time. • Graphs show trends or movements over time. • Use the program “excel”, free and relatively simple.

14. Graphical presentation

15. Analysis of data • An important tool for analyzing data is statistics, a mathematical way of summarizing and interpreting quantifiable research results. • Do your study allow for statistics? • Must be considered when designing the study. • It is important to understand when to apply each statistical tool, and how to interpret the results.

16. Statistical analysis of data • Everything varies, if you measure two things twice they will be different. • P value -the power of a test is the probability of rejecting the null hypothesis when it is false. • The null hypothesis, nothing happens. • The alternative hypothesisthere is significant divergence or pattern in the data

17. Some important concepts in the lecture: • Bias • Random selection • Randomization of treatments • Reliability • Representativity • Objectivity

18. An example, a research project “Poverty has been a major barrier to a healthy lifestyle”. The abstract • The elderly have chronic health problems attributed to obesity. • Research suggests that exercise can reduce the risk of some health problems. • The hypothesis of the study: “that older African American women living above the poverty level will practice more health promoting behaviours as measured by the Health-Promoting Lifestyle profile (HPLP) than women living below the poverty level.

19. The method, including the instruments and procedure. • What were good aspects of the design? Could there be aspects of bias in the design? • What were questionable aspects of the design? • What factors other than the projects might have resulted in positive attitudes.

20. Results • Try to evaluate the results! • Are the presented results supported by the study? • Do the results answer the purpose of the study? • Important part of interpreting the results - Do tables and figures present the results in a comprehendible way? • Are some results missing, are results confounding? • Look for speculation only!

21. Results, the example • Results lacking, would have been informative! • What percentage of those above poverty level had been graduated from college and high school. • A large percentage were married or widowed, but we don’t know their economic level. • Those below the poverty level had a large range of scores, along with greater variability.

22. Discussion • Finally you will evaluate the experimentor’s discussion of the results in terms of the extent to which the conclusion is justified, can be generalized and has limitations. • Statements – are they justified? • Look out for statements of which there is no good arguments based on own results. • Or statements where references are lacking.

23. Discussion / conclusion, the example • The conclusion is inappropriate! • Health-promoting behaviors were not observed., they were reported. • we don’t know the extent to which test items accurately reflect behavior. • we don’t know the accuracy of the self-reports • Note that score might have been higher if all forms of exercise, not just recreational were reported.

24. Conclusion, the example • This is misleading, because it implies that exercise is a main factor that accounts for the difference in HPLP between the two groups. • Thus if groups were matched on all non-poverty level variables and were tested by a naive (with respect to the purpose of the study) individual, it would be possible to reach a valid conclusion.