Document Sample

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. 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 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. Regarding the sample… POPULATION (N) IS THE SAMPLE REPRESENTATIVE? SAMPLE (n) The sampling process… POPULATION INFERENCE SAMPLE Validity in term of research finding 1. 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. 2. 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. Steps in sampling... 1. Define population (N) to be sampled 2. Determine sample size (n) 3. Select sample 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 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). 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) 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 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 => 30) 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. 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 TEKNIK SAPLING 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 SNOWBALL SAMPLING Probability Sampling 1. Simple random sample. 2. Stratified random sample. Proportional Disproportional 3.Cluster(multistage) sample 4.Systematic sample 1. 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 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. 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. 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. advantages… …easy to conduct …strategy requires minimum knowledge of the population to be sampled 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 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 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. 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. 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 advantages… …more precise sample …can be used for both proportions and stratification sampling …sample represents the desired strata disadvantages… …need names of all population members …there is difficulty in reaching all selected in the sample …researcher must have names of all populations 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. 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. 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. 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. advantages… …efficient …researcher doesn’t need names of all population members …reduces travel to site …useful for educational research disadvantages… …fewer sampling points make it less like that the sample is representative 4. Systematic sampling: the process of selecting individuals within the defined population from a list by taking every K th name. 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. 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. 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. advantages… …sample selection is simple 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 Quota sampling is similar to stratified random sampling, except that the desired number of elements for each stratum are selected through convenience sampling. 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 …because samples need to be small and many potential participants are unwilling to undergo the demands of participation, most qualitative research samples are purposive 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 True or false… …the size of the sample influences both the representativeness of the sample itself and the statistical analysis of study data true 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 True or false… …a good researcher can avoid sampling bias true 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 True or false… …a “good” sample is one that is representative of the population from which it was selected true True or false… …a table of random numbers selects the sample through a purely random, or chance, basis true True or false… …qualitative research uses sampling strategies that produce samples which are predominantly small and nonrandom true Fill in the blank… …the group to which research findings are generalizable population Fill in the blank… …the extent to which the results of one study can be applied to other populations or situations generalizability Which type of sample… …identified subgroups in the population are represented in the same proportion that they exist in the population stratified 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 Which type of sample… …selecting participants who permit study of different levels of the research topic intensity Which type of sample… …selects intact groups, not individuals having similar characteristics cluster 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 Which type of sample… …selecting participants who are very similar in experience, perspective, or outlook homogeneous Which type of sample… …all individuals in the defined population have an equal and independent chance of being selected for the sample random Which type of sample… …a sampling process in which individuals are selected from a list by taking every Kth name systematic Which type of sample… …selecting all cases that meet some specific characteristic criterion 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 Sample Size Calculator Creative Research Systems: www.surveysystem.com/sscalc.htm Confidence Confidence Population Size Interval 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 68 TEKNIK SAPLING Snowball sampling 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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