Sample-Sampling-Pengelompokan Data

Important statistical terms Population: a set which includes all measurements of interest to the researcher Sample: A subset of the population (representative/ miniature of population) Sample Populasi Sampling: the process of selecting a sample

The purpose for sampling…… To get information about large populations Reasons for sampling: Less costs Less field time Getting more accurate results When it’s impossible to study the whole population

The sampling process… POPULATION INFERENCE (kesimpulan) SAMPLE

Regarding the sample… POPULATION (N) SAMPLE (n) IS THE SAMPLE REPRESENTATIVE? SAMPLE (n)

Regarding the inference… POPULATION (N) INFERENCE IS THE INFERENCE GENERALIZABLE? SAMPLE (n)

Steps in sampling... 1. Define population (N) to be sampled 2. Determine sample size (n) 3. Control for bias and error 4. Select sample

1. Define population to be sampled... Identify the group of interest and its characteristics  Inference should be generalizable …called the “target” population (the ideal selection) …often times the “accessible” or “available” population must be used (the realistic selection)

2. Determine the sample size... The size of the sample affects 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

3. Control for sampling bias and error... Be aware of the sources of sampling bias and identify how to avoid it

4. Select the sample... A process by which the researcher attempts to ensure that the sample is representative of the population from which it is to be selected …requires identifying the sampling method that will be used

Types of sampling Non-probability samples Probability samples

Non probability samples Sample is not drawn randomly Convenience samples (ease of access) sample is selected from elements of a population that are easily accessible Purposive sampling (judgemental) We chose who we think should be in the study In the case of non probability sample we usually cannot draw conclusions about the whole population

Non probability samples Probability of being chosen is unknown i.e Cheaper sample - but unable to generalise potential for bias

Probability samples Random sampling Each subject has a known probability of being selected Allows application of statistical sampling theory to results to: Generalise Test hypotheses

Conclusions Probability samples are the best Ensure Representativeness Precision

Methods used in probability samples Simple random sampling Systematic sampling Stratified sampling Multi-stage sampling Cluster sampling

Simple Random Population Sample Taken from simple population

Stratified Population Sample Sample Guarantee that all groups are represented like in the population Population Sample 18-29 Proportional allocation 30-49 65+ 50-64 Even allocation Compare groups Sample

Systematic Select picking interval e.g every fifth. Choose randomly one among the first five (or whatever the picking interval is). Pick out every fifth (or whatever the picking interval is) beginning from the chosen one.

Cluster Divide population into clusters (schools, districts,…) Choose randomly some of the clusters. Draw sample from chosen clusters using appropriate sampling method (or investigate chosen groups in whole). Sample

Cluster sampling Cluster: a group of sampling units close to each other i.e. crowding together in the same area or neighborhood

Cluster sampling Section 1 Section 2 Section 3 Section 5 Section 4

Sampling Error Sample 1 mean 43,5 Population mean 40,8 Sample 2 mean 40,3 Different samples from the same population give different results Sample 3 mean 45,4

Errors in sampling Systematic error (or bias) Inaccurate response (information bias) Sampling error (random error)