Tag Archives: sample

Topic 0013: Choosing a Sampling Method

There are many methods of sampling when doing research such as;

Probability methods: This is the best overall group of methods to use as you can subsequently use the most powerful statistical analyses on the results.
Simple Random Whole population is available
Stratified There are specific sub-groups to investigate (eg. demographic groupings).
Systematic When a stream of representative people are available (eg. in the street).
Cluster When population groups are separated and access to all is difficult, eg. in many distant cities.
Quota methods: For a particular analysis and valid results, you can determine the number of people you need to sample. In particular when you are studying a number of groups and when sub-groups are small, then you will need equivalent numbers to enable equivalent analysis and conclusions.
Quota You have access to a wide population, including sub-groups
Proportionate Qouta You know the population distribution across groups, and when normal sampling may not give enough in minority groups
Non-Proportionate Quota There is likely to a wide variation in the studied characteristic within minority groups
Selective methods: Sometimes your study leads you to target particular groups
Purposive You are studying particular groups
Expert You want expert opinion
Snowball You seek similar subjects
Modal Instance When sought ‘typical’ opinion may get lost in a wider study, and when you are able to identify the ‘typical’ group
Diversity You are specifically seeking differences, eg. to identify sub-groups or potential conflicts

Topic 0012: Sampling Terminology

Population

A population is the total group of people about who you are researching and about which you want to draw conclusions.

It is common for variables in the population being denoted by Greek letters and for those in the sample to be shown by Latin letters. For example standard deviation of the population is often shown with s (sigma), whilst of a sample is ‘s’. Sometimes as an alternative, capital letters are used for the population.

Sample frame

The list of people from whom you draw your sample, such as a phone book or ‘people shopping in town today’, may well be less than the entire population and is called a sample frame. This must be representative of the population otherwise bias will be introduced.

Screen Shot 2016-11-01 at 11.32.50 AM.png

Terminology Definition

Sample

 

When the population is large or generally inaccessible then the approach used is to measure a subset or sample.

Unit

A unit is the thing being studied. Usually in social research this is people. There may also be additional selection criteria used to choose the units to study, such as ‘people who have been police officers for at least five years.

Sample size

In order to be representative of the population, the sample must be large enough. There are calculations to help you determine this. The required sample size depends on the homogeneity of the population, as well as its total size.

Generalizing

After sampling you then generalize in order to make conclusions about the rest of the population.

Validity

Validity is about truth and accuracy. A valid sample is representative of the population and will allow you to generalize to valid conclusions. This aligns with external validity. A valid sample is both big enough and is selected without bias so it is representative of the population.

Bias

Bias, a distortion of results, is the bugbear of all research and it can be introduced by taking a sample that does not truly represent the population and hence is not valid.

Assignment

Having drawn the sample, these may be assigned to different groups. A common grouping is an experimental group which receive the treatment under study and a control group that gives a standard against which experimental results can be compared. To sustain internal validity, this is usually random assignment. Non-random assignment is sometimes ok, for example where two school classes are selected as coherent groups and one chosen as the control.

Sampling fraction

When there a sample of n people are selected from a population of N, then the sampling fraction is calculated as n/N. This may be expressed as a number (eg . 0.10) or a percentage (eg. 10%).

Sampling distribution

If the sample is described as a histogram (a bar chart showing numbers in different measurement ranges) it will have a particular shape. Multiple samples should have similar shapes, although random variation means each may be slightly different. The larger the sample size, the more similar sample distributions will be.

Sampling error

This is the standard error for the sample distribution and measures the variation across different samples. It is based on the standard deviation of the sample and the gap between this and the standard deviation of the population. Larger sample sizes will lead to a smaller sampling error. An estimate calculation for a single sample is: sm = sx / sqrt(N), Where:

  • sx  is the standard deviation of the sample
  • N is the sample size

Systematic error

A systematic error is one caused by human error during the design or implementation of the experiment.

Strata

Strata (singular: stratum) are sub-groups within a population or sample frame. These can be random groups, but often are natural groupings, such as men and women or age-range groups. Stratification helps reduce error.

Oversampling

Occurs when you study the same person twice. For example if you selected people by their telephone number and someone had two phone numbers, then you could end up calling them twice. This can cause bias.

Topic 0009: N24 vs Correlation

Is it possible for me to execute correlation with a sample n=24 to see the relationship between variables? Here are some answer guide me as well;

Experts

Verbatim

Eddie Seva See (Bicol University)

Hi Syamsul Nor Azlan Mohamad, correlations are characterized by strength (magnitude), and if a random sample, by significance. Not by reliability. To calculate the magnitude, 3 samples would be mathematically sufficient. But for a very small sample size, a very strong magnitude would more likely still result to a non-significant relationship. On the other hand, in a very large sample size, a very low magnitude may still result to a significant relationship. For a sample size of 24, t-test of r ( which is parametric), is used to determine significance of relationship.

Adel Al Sharkasi (University of Benghazi, Faculty of Science)

Statistically, you can calculate the correlation for data of size 24 or less but the question is how to calculate it as the method of calculation depends on the type of data (quantitative or qualitative).

However, if you want to test whether or not the correlation is the significant, you need to worry about the normality of your data and the size. In your situation as you mention that N=24 is population so you do not worry about the size of population and you can calculate any parameter using tools of descriptive statistics not inferential statistics

Benjamin Chris Ampimah (Jiangsu University)

­

Yes it is possible to compute correlation for a sample of size 24. A scatter plot give you a rough and fair idea of the the relationship between the variables. this you can see pictorially. Since correlation talks about the relationship between variables, it beholds on you to go a step further to ascertain the predictive power of your independent variable(s) by computing the coefficient of determination. This helps you to know know the extent to which the independent variable can explain or predict your dependent various. You can compute this by squaring the value of your correlation coefficient and expressing it in percentage form to know how much of the dependent variable has been explained by the independent variable. Remember the number of variables you are studying also need to be considered.

Rainer Duesing (Universität Osnabrück)

If you collect data from the whole population, you do not need any inferential statistics! Inferential statistics is a method to estimate the population parameters from a sample and estimate the precision of your estimate. When the 24 samples are your population, then any parameter, mean, standard deciation, correlation is that of this specific population, there is no need to claculate any singnificance or anything like that. The evaluation of these parameters, e.g. correlation is  high vs. low, is up to you and your expertise. You will not find answers to this questions in inferential statistics.

Rainer Duesing (Universität Osnabrück)

Ok, than everything of inferential statistics applies to your sample, including the Fisher’s transformation, significance testing, power analysis etc., but also Spearman’s correction for attenuation, I hope this helps, otherwise just ask.

Saedah Siraj (Universiti Malaya)

If your study utilized  DDR as a design approach,  24 experts as your panel of FDM or ISM is ok.  But for the survey method you should add more sample.


Full discussion at: