# Sampling.

## Presentation on theme: "Sampling."— Presentation transcript:

Sampling

A famous sampling mistake
That’s Truman They only asked rich, white people with telephones who’d they vote for. Sadly, they published their mistake

What is a sample? A sample is any part of a population of individuals on whom information is obtained: students, teachers, young learners, etc. Selection of the sample of individuals who will participate in the study is an important part of research. Sampling refers to the process of selecting these individuals

Samples and Populations
A sample is the group on which information is obtained. The population is the larger group to which the researcher hopes to apply the results. All university students studying English Language Teaching in Turkey can be the population; the ones studying ELT in the regions of Adana and Mersin can be a sample. Sometimes the sample and the population may be identical.

Most populations are large, diverse and scattered, so it may be difficult to obtain data from all. In that case, sampling is needed. E.g. You are interested in the way English teachers are assessing young learners in primary schools in Adana. There are 1,500 students in primary schools in that city. You can select 150 students in different schools as a sample for your study.* *The number generally depends what methodology you will use.

Defining the Population
To define the population, we should answer the question, “What am I exactly interested in?”, “What is the group to whom I want to generalize the results of my study?” Some examples: All high school principals All fifth-grade classrooms in Mersin All language teachers teaching young learners

Target vs. Accessible Populations
The actual population (target population) is rarely available. Then the population to which a researcher is able to generalize is the accessible population. E.g. Research Problem: The effects of computer-assisted instruction on the reading achievement of 1st- and 2nd-graders in Turkey. Target population: All 1st- and 2nd-graders in Turkey Accessible population: All 1st- and 2nd-graders in Seyhan region of Adana OR-- All 1st- and 2nd-graders in Celalettin Sayhan Primary School Sample: 10% of the 1st- and 2nd-graders in Seyhan region of Adana OR– 150 students attending 1st- and 2nd-graders in Celalettin Sayhan Primary School

Random (Probability) vs. Nonrandom (Non-probability) Sampling
Random sampling (probability) means selecting the samples without criteria (drawing 10 teachers out of 50 to interview) Nonrandom sampling (non-probability) means selecting the samples based on a kind of criteria (the ones who have at least 5 years of experience)

Scenario Hypo: Students with low self-esteem demonstrate lower achievement in school subjects. Target population: all eighth-graders in Turkey Accessible population: all eighth-graders in Adana Feasible sample size: n=

Random Sampling Methods
1. Simple Random Sampling: The one in which each and every member of the population has an equal and independent chance of being selected. If the sample is large, this is the best method. This can be done using a table of random numbers (can be found in statistics books) or just drawing out the names/numbers, etc.

Example on Scenario Identify all eight-graders in Adana (private and public schools). Assign each student a number and select a sample of students using a table of random numbers PS. Time-consuming to reach all schools

2. Stratified Random Sampling
The process in which certain subgroups (strata) are selected for the sample in the same proportion as they exist in the population. E.g. If you want to compare students’ achievements regarding their gender, you should ensure the proportion of males and females is the same. 500 students (population) 200 males and 300 females You want to use 20% So you select 40 males and 60 females (20% from each group)

Example on Scenario Obtain data for all eighth-graders in Adana and determine the proportion of each type (e.g. 80% public; 20% private) Public 80% of 200= 160 Private 20% of 200= 40 Randomly select students

3. Cluster Random Sampling
Selection of groups of subjects, clusters, not individuals Used when it is not possible to select a sample of individuals (list of all individuals not available, target populations is too big, administrative reasons…) E.g. You want to see all elementary students’ attitudes towards English. Not possible to get their names and use simple or stratified random sampling. Then use some classes from selected schools

Example on Scenario Identify all private and public schools in Adana (having 8th grade). Assign each school a number and select randomly 4 schools. All 8th graders in these schools are your samples. Estimate of 2 classes per school x 30 students each x 4 schools = 240 students

4. Two-Stage Random Sampling
Combining individual and cluster random sampling E.g. first cluster sampling: select N number of classes from the population Second individual sampling: select N number of students from each class

Example on Scenario Randomly select 25 schools in Adana.
Then randomly select 8 students from each 25 x 8 = 200

Nonrandom Sampling Methods
1. Systematic Sampling Selecting every Nth individual in the population. Get the names of all the population (alphabetically listed) and select every nth number Be careful! if the names are not alphabetically listed (e.g. listed according to the success level), your results may be biased as you might not have any students who have poor/high performance

Example on Scenario Identify the students in all schools
Identify every 5th student if there are students in total (250 students as sample)

2. Convenience Sampling Selecting individuals who are available. Generally, this sampling is not considered to represent a population so is avoided. If this is a must, you should include as much information about the sample as possible.

Example on Scenario Select all 8th graders in 4 schools to which you can access. Estimate of 2 classes in each school X 30 students X 4 = 240

3. Purposive Sampling Selecting samples based on researcher’s judgment Main disadvantage: researcher’s judgment may be wrong.

Example Select 8 classes from all schools on the basis of data you have. Be sure they are representative of all 8th graders

Choosing the method Method Best when Simple random sampling
Whole population is available. Stratified sampling (random within target groups) There are specific sub-groups to investigate (eg. demographic groupings). Systematic sampling (every nth person) When a stream of representative people are available (eg. in the street). Cluster sampling (all in limited groups) When population groups are separated and access to all is difficult, eg. in many distant cities. Purposive sampling (based on intent) You are studying particular groups Convenience sampling (use who's available) You cannot proactively seek out subjects.