1. Business Project Nicos Rodosthenous PhD 04/11/2014 5 04/11/20141Dr Nicos Rodosthenous

2. Selecting samples 1. Introduction Whatever your research question(s) and objectives are, you will need to collect data to answer them. However, it will be impossible to collect or to analyze all the data due to restrictions of time, money and access. Sampling techniques can provide a range of methods that enable you to reduce the amount of data, as per Figure 6.1 below. 04/11/20142Dr Nicos Rodosthenous

3. Selecting samples 04/11/20143Dr Nicos Rodosthenous

4. Selecting samples The full set of cases from which a sample is taken is called population. 2. The need to sample Sampling provides a valid alternative to a census when:  It would be impracticable for you to survey the entire population.  There are budget constraints  There are time constraints  You have collected all the data but you need the 04/11/20144Dr Nicos Rodosthenous

5. Selecting samples  results quickly.  3. An overview of sampling techniques  It can be divided into two types:  1) probability or representative sampling  2) non-probability or judgemental sampling  With probability samples the chance of each case being selected is known and is equal for all cases.  For non-probability samples each case selected is not known and by this way it is impossible to answer research questions. (used for case study) 04/11/20145Dr Nicos Rodosthenous

6. Selecting samples 04/11/20146Dr Nicos Rodosthenous

7. Selecting samples 4. Probability sampling Probability sampling is mainly associated with survey-based research and can be divided into 1) identify a suitable sampling frame based on your research questions or objectives. 2) decide the sample size 3) select the most appropriate sampling technique and select the sample 4) check that the sample is representative of the population 04/11/20147Dr Nicos Rodosthenous

8. Selecting samples 4.1 Identifying a suitable sampling frame It is a complete list of all cases in the population from which your sample will be drawn. For example, for the members of a local video club, the sampling frame will be the complete membership list of the club. In an incomplete list means that some cases will be excluded and the sample may not be representative of the total population. 04/11/20148Dr Nicos Rodosthenous

9. Selecting samples 4.2 Deciding on a suitable sample size The larger your sample’s size is, the lower the likely error will be. Probability sampling is a compromise between accuracy of the findings and the amount of time and money you invest in collecting, checking and analyzing the data. The choice of sample size is governed by:  the confidence you will have in your data  The margin of error that you can tolerate 04/11/20149Dr Nicos Rodosthenous

10. Selecting samples  The types of analyses you will undertake-the number of categories into which you will put your data  The size of the total population from which your sample is being drawn  4.3 The importance of a high response rate  A part of your research report will need to include the response rate.  Total response rate = total number of responses/total number in sample 04/11/201410Dr Nicos Rodosthenous

11. Selecting samples 4.4 Selecting the most appropriate sampling technique and the sample Once you have chosen a suitable sampling frame and established the actual sample size required, then you need to select the most appropriate sampling technique. There are five main techniques:  Simple random sampling: you select the sample at random from the sampling frame. 04/11/201411Dr Nicos Rodosthenous

12. Selecting samples  Systematic sampling: you select the sample at regular intervals form the sampling frame.  To calculate the sampling fraction you use the formula: sampling fraction=actual sample size/total population.  If for instance, the sampling fraction is 1/3 then you need to select one in every 3 cases.  Stratified random sampling: is a modification of random sampling in which you divide the population into two or more relevant strata. 04/11/201412Dr Nicos Rodosthenous

13. Selecting samples  Cluster sampling: is similar to stratified sampling as you need to divide the population into groups prior to sampling.  The groups are termed clusters, and you can for example group your data by type of manufacturing firm or geographical area.  Multi-stage sampling: sometimes is called multi- stage cluster sampling, which is a development of cluster sampling.  Because multi-stage sampling relies of different 04/11/201413Dr Nicos Rodosthenous

14. Selecting samples sampling frames you need to ensure that they are all appropriate and available. 5. Checking the sample is representative To compare data you collect from your sample with data from another source for the population. (i.e. age and socioeconomic data) If there is no statistically difference then the sample is representative. When working an organization comparisons can also be made about salary, gender, years of service and place of work. 04/11/201414Dr Nicos Rodosthenous

15. Selecting samples 6. Non-probability sampling Non probability sampling provides a range of alternative techniques based on your subjective judgment. 6.1 Selecting the most appropriate sampling technique and the sample It depends on your research questions and objectives-in particular what you need to find out, what will be useful, what will have credibility and what can be done within your available resources. 04/11/201415Dr Nicos Rodosthenous

16. Selecting samples 6.2 Quota sampling: is entirely non-random and is normally used for large populations with interview surveys. To select a quota sample you:  Divide the population into specific groups  Calculate a quota for each group based on relevant and available data.  Give each interviewer an assignment  Combine the data collected to provide the full sample. 04/11/201416Dr Nicos Rodosthenous

17. Selecting samples 6.3 Purposive sampling: enables you to use your judgment to select cases that will best help you to answer your research questions and meet your objectives. This form of sample is often used for very small samples. 6.4 Snowball sampling: is commonly used when it is difficult to identify members of the desired population, i.e. working people claiming unemployment benefit. 04/11/201417Dr Nicos Rodosthenous

18. Selecting samples 6.5 Self-selection sampling: occurs when you allow an individual to identify his desire to take part in the research. You therefore: 1) Publicize your need for cases 2) Collect data from those who respond. 6.6 Convenience sampling: involves selecting haphazardly those cases that are easiest to obtain for your sample, such as the person interviewed at random in a shopping center for a TV program. 04/11/201418Dr Nicos Rodosthenous