POPULASI DAN SAMPEL

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Populasi dan Sampel
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Population
the larger group from which individuals are
selected to participate in a study Misalnya,
penelitian pada perusahaan go publik di bursa
efek Jakarta (BEJ). Perusahaan go publik ini
kemudian disebut dengan populasi. Bahkan, satu
perusahaanpun dapat dikategorikan sebagai
populasi, kalau di dalamnya terdapat banyak
karakteristik, misalnya gaya kepemimpinan,
motivasi kerja, harga saham, ratio keuangan,
konflik kerja, minat, hobi, dan sebagainya.
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Sampling

• The process of selecting a number of individuals
  for a study in such a way that the individuals
  represent the larger group from which they were
  selected

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• Sampel merupakan sebagian dari populasi yang
  akan diketahui karakteristiknya. Ada beberapa
  alasan pengambilan sample penelitian, yaitu
• Uji coba yang membahayakan.
• Meningkatkan ketelitian
• Populasi terlalu besar
• Meningkatkan
• Menanggulangi kendala waktu, tenaga dan biaya.

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Regarding the sample
POPULATION (N)
IS THE SAMPLE REPRESENTATIVE?
SAMPLE (n)
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The sampling process
POPULATION
INFERENCE
SAMPLE
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Validity in term of research finding

• Internal validity is related to what actually
  happens in a study.  In terms of an experiment it
  refers to whether the independent variable really
  has had an effect on the dependent variable or
  whether the dependent variable was caused by some
  other confounding variable. 
• External validity refers to whether the findings
  of a study really can be generalised beyond the
  present study.  External validity can be broken
  down into two types.
• Population validity - which refers to the extent
  to which the findings can be generalised to other
  populations of people.
• Ecological validity - which refers to the extent
  to which the findings can be generalised beyond
  the present situation.

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Steps in sampling...
1. Define population (N) to be sampled
2. Determine sample size (n)
3. Select sample
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Sampling error and bias

• Sampling error
• a. Random error
• b. Systematic error (sample parameters is
  different from population parameters)
• Bias sampling (non random sampling)
• a. Researcher preference
• b. Methodological bias

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Faktor penentu sample size

• Ukuran anggota populasi
• Teknik sample yang dipilih
• Heterogenitas anggota populasi
• Tingkat risiko penelitian yang dilakukan
• Tingkat kesalahan yang diinginkan peneliti
  (generalization rate)
• Metode statistik yang akan digunakan (parametrik
  / nonparametrik)
• Kemampuan peneliti (waktu, tenaga, biaya, dan
  perijinan).

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Define population to be sampled...

• Identify the group of interest and its
  characteristics to which the findings of the
  study will be generalized

called the target population (the ideal
selection)
oftentimes the accessible or available
population must be used (the realistic selection)
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Determine the sample size...

• The size of the sample influences both the
  representativeness of the sample and the
  statistical analysis of the data

larger samples are more likely to detect a
difference between different groups
smaller samples are more likely not to be
representative
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Rules of thumb for determining the sample size...
1. The larger the population size, the smaller
the percentage of the population required to get
a representative sample
2. For smaller samples (N 100), there is little
point if we have sampling. Survey the entire
population. (Central limit theorem gt 30)
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3. If the population size is around 500 (give or
take 100), or 50 should be sampled.
4. If the population size is around 1500, 20
should be sampled.
5. Beyond a certain point (N 5000), a sample
size of 400 may be adequate.
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Approaches to quantitative sampling...
1. Random allows a procedure governed by chance
to select the sample controls for sampling bias
2. Nonrandom (nonprobability) does not have
random sampling at any state of the sample
selection increases probability of sampling bias
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TEKNIK SAPLING
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Non-probability sampling

• Accidental sample
• Subjects who happen to be encountered by
  researchers
• Example observer unfair practice in a general
  election.
• Quota sample
• Elements are included in proportion to their
  known representation in the population
• Snowball sampling
• a useful technique in situations where one
  cannot get a list of individuals who share a
  particular characteristic. It is useful for
  studies in which the criteria for inclusion
  specify a certain trait that is ordinarily
  difficult to find. It relies on previously
  identified members of a group to identify other
  members of a population. As one member was
  identified, he or she gave the names of the
  others to contact.
• Purposive/criterion/convenience sample
• Researcher uses best judgment to select elements
  that typify the population
• Example Interview all burglars arrested during
  the past month

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SNOWBALL SAMPLING
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Probability Sampling

• 1. Simple random sample.
• 2. Stratified random sample.
• Proportional
• Disproportional
• 3.Cluster(multistage) sample
• 4.Systematic sample

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• Simple random sampling the process of selecting
  a sample that allows individual in the defined
  population to have an equal and independent
  chance of being selected for the sample.

Online link www.random.org/nform.html
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Steps in random sampling...
1. Identify and define the population.
2. Determine the desired sample size.
3. List all members of the population.
4. Assign all individuals on the list a
consecutive number from zero to the required
number. Each individual must have the same
number of digits as each other individual.
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5. Select an arbitrary number in the table of
random numbers.
6. For the selected number, look only at the
number of digits assigned to each population
member.
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7. If the number corresponds to the number
assigned to any of the individuals in the
population, then that individual is included in
the sample.
8. Go to the next number in the column and repeat
step 7 until the desired number of individuals
has been selected for the sample.
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advantages
easy to conduct
strategy requires minimum knowledge of the
population to be sampled
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disadvantages
need names of all population members
may over- represent or under- estimate sample
members
there is difficulty in reaching all selected in
the sample
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2. Stratified sampling the process of selecting
a sample that allows identified subgroups in the
defined population to be represented in the same
proportion that they exist in the
population. Stratified random sampling involves
dividing the population into subgroups , and then
random samples are chosen from these groups. Eq.
Managers in service industries in BEI
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• Proportional stratified sampling, samples are
  chosen from each stratum, and these samples are
  in proportion too the size of that stratum in the
  total population. Stratified random sampling
  achieves a greater degree of representativeness
  with each subgroups, or stratum, of population.
• Disproportional stratified sampling When strata
  are unequal in size. May be used to ensure
  adequate samples from each stratum.

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Steps in stratified sampling...
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify the variable and subgroups (strata)
for which you want to guarantee appropriate,
equal representation.
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4. Classify all members of the population as
members of one identified subgroup.
5. Randomly select, using a table of random
numbers) an appropriate number of individuals
from each of the subgroups, appropriate meaning
an equal number of individuals
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advantages
more precise sample
can be used for both proportions and
stratification sampling
sample represents the desired strata
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disadvantages
need names of all population members
there is difficulty in reaching all selected in
the sample
researcher must have names of all populations
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3. Cluster sampling the process of randomly
selecting intact/all groups, not individuals,
within the defined population sharing similar
characteristics Eq. Going public companies in
BEI are consisted of many industrial types
managers in banking industries etc.
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• Cluster sampling (multistage sampling), groups
  not individuals randomly selected. Cluster
  sampling is used for convenience when the
  population is very large or spread over a wide
  geographical area. Selection of individuals from
  with in clusters may be performed by random or
  stratified random sampling.

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Steps in cluster sampling...
1. Identify and define the population.
2. Determine the desired sample size.
3. Identify and define a logical cluster.
4. List all clusters (or obtain a list) that make
up the population of clusters.
5. Estimate the average number of population
members per cluster.
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6. Determine the number of clusters needed by
dividing the sample size by the estimated size of
a cluster.
7. Randomly select the needed number of clusters
by using a table of random numbers.
8. Include in your study all population members
in each selected cluster.
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advantages
efficient
researcher doesnt need names of all population
members
reduces travel to site
useful for educational research
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disadvantages
fewer sampling points make it less like that the
sample is representative
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4. Systematic sampling the process of selecting
individuals within the defined population from a
list by taking every K th name.
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• Systematic sampling individuals or elements of
  the population are selected from a list by taking
  every ( Kth) individual. The "K", which refers to
  a sampling interval, depends on the size of the
  list and desired sample size. After the first
  individual is selected, the rest of the
  individuals to be included are automatically
  determined.

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Steps in systematic sampling...
1. Identify and define the population.
2. Determine the desired sample size.
3. Obtain a list of the population.
4. Determine what K is equal to by dividing the
size of the population by the desired sample size.
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5. Start at some random place in the population
list. Close you eyes and point your finger to a
name.
6. Starting at that point, take every Kth name on
the list until the desired sample size is reached.
7. If the end of the list is reached before the
desired sample is reached, go back to the top of
the list.
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advantages
sample selection is simple
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disadvantages
all members of the population do not have an
equal chance of being selected
the Kth person may be related to a periodical
order in the population list, producing
unrepresentativeness in the sample
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• Quota sampling is similar to stratified random
  sampling, except that the desired number of
  elements for each stratum are selected through
  convenience sampling.

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Approaches to qualitative sampling...
qualitative research is characterized by
in-depth inquiry, immersion in a setting,
emphasis on context, concern with participants
perspectives, and description of a single
setting, not generalization to many settings
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because samples need to be small and many
potential participants are unwilling to undergo
the demands of participation, most qualitative
research samples are purposive
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MENENTUKAN UKURAN SAMPLE

• Tabel Krecjie (Table 1)
• Nomogram Harry King (Chart 1)
• Isaac and Michael (Table 1, 2, and Chart 1 are
  here)
• Slovin Method

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THE END
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• True or false

the size of the sample influences both the
representativeness of the sample itself and the
statistical analysis of study data
true
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• True or false

both quantitative and qualitative researchers
who use samples must provide detailed information
about the purposive research participants and how
they were chosen
true
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• True or false

a good researcher can avoid sampling bias
true
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• True or false

the important difference between convenience
sampling and purposive sampling is that, in the
latter (purposive sampling), clear criteria guide
selection of the sample
true
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• True or false

a good sample is one that is representative of
the population from which it was selected
true
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• True or false

a table of random numbers selects the sample
through a purely random, or chance, basis
true
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• True or false

qualitative research uses sampling strategies
that produce samples which are predominantly
small and nonrandom
true
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• Fill in the blank

the group to which research findings are
generalizable
population
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• Fill in the blank

the extent to which the results of one study can
be applied to other populations or situations
generalizability
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• Which type of sample

identified subgroups in the population are
represented in the same proportion that they
exist in the population
stratified
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• Which type of sample

selecting a few individuals who can identify
other individuals who can identify still other
individuals who might be good participants for a
study
snowball
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• Which type of sample

selecting participants who permit study of
different levels of the research topic
intensity
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• Which type of sample

selects intact groups, not individuals having
similar characteristics
cluster
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• Which type of sample

selecting by random means participants who are
selected upon defined criteria and not who are
too numerous to include all participants in the
study
random purposive
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• Which type of sample

selecting participants who are very similar in
experience, perspective, or outlook
homogeneous
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• Which type of sample

all individuals in the defined population have
an equal and independent chance of being selected
for the sample
random
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• Which type of sample

a sampling process in which individuals are
selected from a list by taking every Kth name
systematic
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• Which type of sample

selecting all cases that meet some specific
characteristic
criterion
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MENENTUKAN UKURAN SAMPLE

• Tabel Krecjie (Table 1)
• Nomogram Harry King (Chart 1)
• Isaac and Michael (Table 1, 2, and Chart 1 are
  here)
• Slovin Method

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Sample Size Calculator

• Creative Research Systems www.surveysystem.com/ss
  calc.htm 

Population Size Confidence Interval Confidence Level Sample Size
1,000 5 95 278
5,000 5 95 357
10,000 5 95 370
50,000 5 95 381
100,000 5 95 383
1,000,000 5 95 384
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TEKNIK SAPLING
Snowball sampling
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Sampling error and bias

• Sampling error
• a. Random error
• b. Systematic error (sample parameters is
  different from population parameters)
• Bias sampling (non random sampling)
• a. Researcher preference
• b. Methodological bias