multipurpose tree species research for small farms: strategies ... - part

in which each population element has an equal
chance of being included, and every combination
of the sample is just as likely as any other
combination to be chosen.
A stratified sample is where the parent
population is divided into mutually exclusive
subsets, and a sample of elements is drawn from
each subset. Stratified samples are tie most
statistically etficient. They have the smallest
standard error for a given size, and allow the
investigation of variables for particular subgroups
within the population.
Stratified samples are further divided into
proportionate and disproportionate samples. In
proportionate stratified sampling, the size of the
sample taken from each stratum depends on the
relative size of the stratum in the population,
whereas in disproportionate stratified sampling,
the sample size depends on the variability within
the stratum as well,
A cluster sample is where the parent population
is divided into mutually exclusive and exhaustive
subsets and then a random sample is selected. If
each of the elements within the selected subsets is
studied, it is called one stage cluster sampling. If
the selected subsets are also sampled, the
procedure is two-stage cluster sampling. A
systematic sample is a form of cluster sample in
which every element is selected after a randomly
determined start.
An area sample is one of the most important
types of cluster samples in applied, large-scale
studies. By defining areas as clusters, then
randomly selecting areas, the investigator develops
lists of population elements for the selected areas,
An illustration of stratified random sampling is
the 1972 fuclwood survey conducted by the Forest
Products Research Institute (FPRI) (Sumarna and
Sudiono 1973). The levels and territories of the
government at the province, district, subdistrict,
and village levels dictated the application of
multi-stage sampling. Random sampling was
carried out from the district level through the
household level as consecutive elementary units.
A distinction was made between urban and rural
areas based on the assumption that levels of
fuelwood consumption would differ. This was
further subdivided into household, industry, and
railway enterprise sectors. The industrial sector
was divided into food and chemical industries,
In a later FPRI survey (Dwiprobowo el al.
1980), household and industrial samples were
taken using a similar technique. The sample at the
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district level was non-random, whereas
subdistrict, village and household samples were
taken at random. The earlier sample was
designed to estimate fuelwood consumption in
the entire province. The later survey
emphasized the pattern of fuelwood
consumption relative to other factors.
Analysis and Interpretation of Data
The purpose of data analysis is to interpret the
collected information. It consists of preliminary
steps and statistical analysis.
Preliminary Steps
The preliminary analytical steps after
obtaining field data are editing, coding and
tabulation. Editing involves a careful scrutiny
and correction of the completed data collection
forms. Particular attention is paid to
unanswered questions, inconsistent answers and
'do not know" responses.
At the coding stage, raw data is transformed
into numbers that can be tabulated and counted.
Coding involves a two step process of specifying
the categories into which the responses are
placed, and assigning code numbers to the
categories.
Tabulation consists of counting the number
of cases that fall into various categories. Simple,
or one way tabulation involves counting a single
variable. In cross-tabulation two or more
variables are treated simultaneously. An
example would be counting the number of cases
with characteristics in common. It studies the
relationship among and between variab!es so
the results can be easily understood. It can also
provide an insight into the nature of a
relationship since the addition of one or more
variables to a two way cross-tabulation analysis
is equivalent to holding each of the variables
constant.
Statistical Analysis
Some studies stop after tabulation and
cross-tabulation. Others involve additional
analyses in a search for statistical significance.
A recurring problem is the selection of the
statistical testing procedure. The choice of an
appropriate technique depends on the type of
data, the research design, the assumption
underlying the statistical test, and the power of
the test.