Transcript Use and traditional knowledge of plant resources by the resettlers in

Lecture 1.2
• Sampling design
• Analysis of data.
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.
Sampling design, the where?
• Random or subjectively chosen plots?
– 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.
Work load
– Do you seek to record general or specific
data?
Objectivity
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)?
Sampling design, size and shape?
30 m
• Size of plots?
The size must be so that the plots are both
representative, but also homogenous.
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330 m
• Shape of plots?
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Transects or quadrates?
Smaller sub-quadrates within quadrates?
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10 000 m2
100m
10 000 m2
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!
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.
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.
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).
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.
Analysis, categorization
• Selecting categories.
• Beware of units and scales, need to
be the same.
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Species used seldomly
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Number of speices
• Value can be assigned to different
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Categories of use
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• Categorization of the data into
different levels, how many levels?
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.
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.
Graphical presentation
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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.
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 hypothesis there is significant
divergence or pattern in the data
Some important concepts in the lecture:
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Bias
Random selection
Randomization of treatments
Reliability
Representativity
Objectivity
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 HealthPromoting 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.