Lecture 2

• Sampling design• Analysis of data.

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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.

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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.

Objectivity

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Sampling design, the where?

• Statistical qualities of plots /study units, are the plots independent?

• Lumping of plots? – How could lumping of plots have effected the results in the

study by Peres et al. (the study of Brazil nut)?

• Redundancy – are common types too redundant (common)?

• Are rare types represented among the samples?

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Sampling design, size and shape?

• 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?

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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!

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Sampling design, the what? qualitative and quantitative approaches.

– 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.

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• Qualitative and quantitative approaches work together, both may be important in a study. In what way?

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.

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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).

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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.

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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.

Categories of use

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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.

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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, than a table.

– Graphs, typically relate two dimensions such as quantity of time.

– Graphs show trends or movements over time.

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Graphical presentationXate harvest

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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.

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Statistical analysis of data

• Everything varies, if you measure two things twice they will be different.

• Thus, finding that things varies is simply not interesting. That is why statistics are needed.

• If we measure bigger differences than would have expected by chance, then we say that the result is statistically significant.

• So, why doesn’t all research projects use statistics?

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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.

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.

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!

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.

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.

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.

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.