Metode Penelitian Pertemuan 10.

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Presentation transcript:

Metode Penelitian Pertemuan 10

Sampling 2

Sampling Sampling: the process of selecting a sufficient number of elements from the population, so that results from analyzing the sample are generalizable to the population.

Relevant Terms - 1 Population refers to the entire group of people, events, or things of interest that the researcher wishes to investigate. An element is a single member of the population. A sample is a subset of the population. It comprises some members selected from it.

Relevant Terms - 2 Sampling unit: the element or set of elements that is available for selection in some stage of the sampling process. A subject is a single member of the sample, just as an element is a single member of the population.

Relevant Terms - 3 The characteristics of the population such as µ (the population mean), σ (the population standard deviation), and σ2 (the population variance) are referred to as its parameters. The central tendencies, the dispersions, and other statistics in the sample of interest to the research are treated as approximations of the central tendencies, dispersions, and other parameters of the population.

Statistics versus Parameters

Advantages of Sampling Less costs Less errors due to less fatigue Less time Destruction of elements avoided

The Sampling Process Major steps in sampling: Define the population. Determine the sample frame Determine the sampling design Determine the appropriate sample size Execute the sampling process

Sampling Techniques Probability versus nonprobability sampling Probability sampling: elements in the population have a known and non-zero chance of being chosen

Sampling Techniques Probability Sampling Nonprobability Sampling Simple Random Sampling Systematic Sampling Stratified Random Sampling Cluster Sampling Nonprobability Sampling Convenience Sampling Judgment Sampling Quota Sampling

Simple Random Sampling Procedure Each element has a known and equal chance of being selected Characteristics Highly generalizable Easily understood Reliable population frame necessary

Systematic sampling Procedure Characteristics Each nth element, starting with random choice of an element between 1 and n Characteristics Idem simple random sampling Easier than simple random sampling Systematic biases when elements are not randomly listed

Cluster sampling Procedure Characteristics Divide of population in clusters Random selection of clusters Include all elements from selected clusters Characteristics Intercluster homogeneity Intracluster heterogeneity Easy and cost efficient Low correspondence with reality

Stratified sampling Procedure Divide of population in strata Include all strata Random selection of elements from strata Proportionate Disproportionate Characteristics Interstrata heterogeneity Intrastratum homogeneity Includes all relevant subpopulations

(Dis)proportionate stratified sampling Number of subjects in total sample is allocated among the strata (dis)proportional to the relative number of elements in each stratum in the population Disproportionate case: strata exhibiting more variability are sampled more than proportional to their relative size requires more knowledge of the population, not just relative sizes of strata

Example

Overview

Choice Points in Sampling Design

Tradeoff between precision and confidence We can increase both confidence and precision by increasing the sample size

Sample size: guidelines In general: 30 < n < 500 Categories: 30 per subcategory Multivariate: 10 x number of var’s Experiments: 15 to 20 per condition

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