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**Research Methods for Business Students**

Mark Saunders, Philip Lewis and Adrian Thornhill Second Edition Chapter Research Methods for Business Students Dr. Wasim Al-Habil.

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**Research Methods for Business Students**

Chapter 6 Research Methods for Business Students

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Key Topics Understand the need for sampling in business and management research Be aware of a range of probability and non-probability sampling techniques and the possible need to combine techniques within a research project Be able to select appropriate sampling techniques for a variety of research scenarios and be able to justify their selection Be able to use a range of sampling techniques Be able to assess the representativeness of respondents Be able to apply the knowledge, skills and understanding gained to your own research project

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**Differing approaches to research (Review)**

Data collection Methods Involve: Sampling Secondary data Observation Interviews Questionnaires (See future notes)

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**Sampling The research Process: Progressive Problem solving**

Wish to research The research Process: Progressive Problem solving Formulate and clarify your Research topic Critically review the literature Sampling Choose your research approach and strategy Negotiate access and address ethical issues Plan your data collection and collect the data using one or more of : Sampling Secondary data Observation Semi-structured and in-depth interviews Questionnaires Analyse your data using one or both of: Quantitative methods Qualitative methods Write your project report Submit your report

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6.1 Introduction Sampling techniques provide a range of methods that enable you to reduce the amount of data you need to collect by considering only data from a subgroup rather than all possible cases or elements. Techniques for selecting samples are very important.

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**Key Words A CENSUS is where you collect data from every single case.**

With SAMPLING you take only data from a sub-group rather than all possible cases or ‘elements’. A POPULATION refers to the full set of cases from which a sample is made. SAMPLING ERROR: The difference between the sample and the population from which you selected your sample. You can take a ‘POPULATION’ from UNRWA or Jawwal organizations in Palestine.

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**6.1 Introduction The need to sample:**

It would be impracticable for you to survey the entire population. Your budget constraints prevent you from surveying the entire population. Your time constraints prevent you from surveying the entire population. You have collected al the data but need the results quickly.

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**Population, sample and individual cases**

Case or element

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**6.1 Introduction An overview of sampling techniques:**

Probability or representative sampling Non-probability or judgmental sampling

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Sampling techniques Sampling Probability Non-Probability

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**Overview of sampling techniques**

PROBABILITY SAMPLING: The chance or probability of each case being selected from the population is known and is equal for all cases. Thus you CAN estimate the characteristics of the population from the sample.

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**Overview of sampling techniques**

NON-PROBABILITY SAMPLING: The chance or probability of each case being selected from the population is NOT known. Thus you CANNOT estimate the characteristics of the population from the sample. GENERALISATION is possible, but not on statistical grounds. Associated with case studies.

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6.2 Probability sampling 1. Identify a suitable sampling frame based on your Research question(s) or objectives; 2. Decide on a suitable sample size; 3. Select the most appropriate sampling technique and select the sample; 4. Check that the sample is representative of the pop.

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**6.2 Probability sampling Deciding on a suitable sample size**

Generalizations about populations from data collected using any probability sample are based on probability. The larger your sample’s size the lower the likely error in generalizing to the population. The final sample size is almost always a matter of judgment as well as calculation. Researchers normally work to a 95 percent level of certainty.

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**6.2 Probability sampling Identifying a suitable sampling frame**

The sampling frame for any probability sample is a complete list of all the cases in the population from which your sample will be drawn. The completeness of your sampling frame is very important. An incomplete or inaccurate list means that some cases will have been excluded and so it will be impossible for every case in the population to have a chance of selection. It is important to ensure that the sampling frame is unbiased, current and accurate.

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**6.2 Probability sampling Deciding on a suitable sample size:**

The confidence you need to have in your data, The margin of error that you can tolerate The types of analyses you are going to undertake The size of the total population from which your sample is being drawn

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**Deciding on a suitable sampling size**

Your choice is influenced by: 1. The confidence you have in your data – the level of certainty that the characteristics of the data collected will represent the total population; 2. The margin of error you can tolerate, I.e. the accuracy you require for any estimates from your sample

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**Table 6.2 sample sizes at a 95% level of certainty**

Margin of error Population 5% 3% 2% 1% 50 44 48 49 200 132 168 185 196 300 234 267 291 400 334 384 1000 278 516 706 906

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**Deciding on a suitable sampling size**

Your choice is influenced by: 3. The types of statistical analyses you are going to undertake 4. The size of the total population from which Your sample is being drawn.

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**6.2 Probability sampling The importance of a high response rate:**

A perfect representative sample is one that exactly represents the population from which it is taken. In reality, you are likely to have non-responses. Non-respondents are different from the rest of the population because they have refused to be involved in your research for whatever reason. In addition, any non-responses will necessitate extra respondents being found to reach the required sample size, thereby increasing the cost of your survey.

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**6.2 Probability sampling The importance of a high response rate:**

Non-response is due to four inter-related problems: Refusal to respond Ineligibility to respond Inability to locate respondent Respondent located but unable to make contact

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**6.2 Probability sampling The importance of a high response rate:**

Total Response Rate. Active Response Rate. Estimating response rates and actual sample size required

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**The importance of a high response rate**

Total Response rate = total number of responses total number in sample – ineligible Active Response rate = total number of responses total number in sample – (ineligible+unreachable)

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6.2 Probability sampling Selecting the most appropriate sampling technique and the sample: Simple random Systematic Stratified random Cluster Multi-stage

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**Probability Sampling Probability Sampling Stratified random Simple**

Systematic Cluster Multi- stage

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6.2 Probability sampling Simple random sampling: It involves you selecting the sample at random from the sampling frame using either random number tables or a computer: Number each of the cases in your sampling frame with a unique number. The first case is numbered 0, the second 1 and so on. Select cases using random numbers until your actual sample size is reached.

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6.2 Probability sampling Systematic sampling: It involves you selecting the sample at regular intervals from the sampling frame. Number each of the cases in your sampling frame with a unique number. The first cases is numbered 0, the second 1 and so on. Select the first case using a random number. Calculate the sampling fraction (Actual Sampling Size/Total Population). i,g. ¼ Select subsequent cases systematically using the sampling fraction to determine the frequency of selection.

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6.2 Probability sampling Stratified random sampling: It is a modification of random sampling in which you divide the population into two or more relevant and significant strata based on one or a number of attributes. Choose the stratification variable or variables. Divide the sampling frame individually into the discrete strata; i.g. male or female, BA or Master’s graduates. Number each of the cases within each stratum with a unique number, as discussed earlier. Select your sample using either simple random or systematic sampling, as discussed earlier.

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6.2 Probability sampling Cluster (Grouping) sampling: Your sampling frame is the complete list of clusters rather than a complete list of individual cases within the population. You then select a few clusters, normally suing simple random sampling. Data are then collected from every case within the selected clusters. Choose the cluster grouping for your sampling frame. I.g. geographical sub-areas. Number each of the clusters with a unique number. The first cluster 0, the second 1, and so on. Select your sampling using some form of random sampling as discussed earlier. Examples: Random sample of customers from north, middle, and south areas of Gaza OR random sample of all 2nd Year students from our cluster of Gazan Universities.

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6.2 Probability sampling Multi-stage sampling: A development of cluster sampling used whenever a researcher is facing problems in a wide and large geography where population is dispersed. Because multi-stage sampling relies on a series of different sampling frames you need to ensure that they are all appropriate and available. See figure 6.4 in page 168

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**6.2 Probability sampling Checking the sample is representative:**

Often it is possible to compare data you collect from your sample with data from another source for the population. If there is no statistically significant difference then the sample is representative with respect to these characteristics. You could use Kolmogorov-Smirnov one sample test.

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**6.3 Non-probability sampling**

Quota Purposive Extreme case Heterogeneous Homogeneous Critical case Typical case Snowball Self-selection convenience

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**Non-probability sampling**

Snowball Quota Purposive Self- selection Con- venience Homogeneous Critical case Typical case Extreme case Heterogeneous

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**6.3 Non-probability sampling**

Quota sampling It is based on the premise that your sample will represent the population as the variability in your sample for various quota variables is the same as that in the population. It is often used for Interview Surveys; i.g. market surveys depends on gender, age, and socioeconomic status. Divide the population into specific groups. Calculate a quota for each group based on relevant and available data. Give each interviewer an assignment, which states the number of cases in each quota from which they must collect data. Combine the data collected by interviewers to provide the full sample.

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**6.3 Non-probability sampling**

Quota sampling is normally used for large populations. Calculations of quotas are based on relevant and available data and are usually relative to the proportions in which they occur in the population. Your choice of quota is dependent on two main factors: Usefulness as a means of stratifying the data. Ability to overcome likely variations between groups in their availability for interview.

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**6.3 Non-probability sampling**

Purposive sampling Purposive or judgmental sampling enables you to use your judgment to select cases that will best enable you to answer your question and the meet your objectives. This form of sample is often used when working with very small samples such as in case study research and when you wish to select cases that are particularly informative.

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**6.3 Non-probability sampling**

Purposive sampling: Such samples cannot be considered to be statistically representative of the total population. The logic on which base your strategy for selecting cases for a purposive sample should be dependent on your research question and objectives.

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**6.3 Non-probability sampling**

Purposive sampling Extreme case or deviant sampling focuses on unusual or special cases on the basis that the data collected about these unusual or extreme outcomes will enable you to learn the most and to answer your research question and to meet your objectives most effectively. This is often based on the premise that findings from extreme cases will be relevant in understanding or explaining more typical cases. Example of Case Study: Why some business organizations have excellent and super performances.

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**6.3 Non-probability sampling**

Purposive sampling Heterogeneous or maximum variation sampling enables you to collect data to describe and explain the key themes that can be observed. In addition, the data collected should enable you to document uniqueness. To ensure maximum variation within a sample, it is suggested that you identify your diverse characteristics (sample selection criteria) prior to selecting your sample. Example of case study: The impact of a certain pay system on the performance of all types of managers in three organizations.

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**6.3 Non-probability sampling**

Purposive sampling: Homogeneous sampling focuses on one particular subgroup in which all the sample members are similar. This enables you to study the group in great depth. Example of case study: The impact of a certain pay system on the performance of middle managers in three organization.

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**6.3 Non-probability sampling**

Purposive sampling Critical case sampling selects critical cases on the basis that they can make a point dramatically or because they are important. The focus of data collection is to understand what is happening in each critical case so that logical generalizations can be made. Example of case study: X crumbled banks after the global financial crisis.

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**6.3 Non-probability sampling**

Purposive sampling Typical case sampling is usually used as part of a research project to provide an illustrative profile using a representative case. (In contrast to Critical case sampling) Such a sample enables you to provide an illustration of that is “typical” to those who will be reading your research report and may be unfamiliar with the subject matter. It is not intended to be definitive. Example of case study: X profitable and well-performed banks after the global financial crisis.

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**6.3 Non-probability sampling**

Snowball sampling It is commonly used when it is difficult to identify members of the desired population, for example people who are working while claiming unemployment benefit. Make contact with one or two cases in the population. Ask these cases to identify further cases. Ask these new cases to identify further new cases (and so on). Stop when either no new cases are given or the sample is as large as is manageable.

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**6.3 Non-probability sampling**

Self-selection sampling It occurs when you allow a case, usually an individual, to identify their desire to take part in the research. Publicize your need for cases, either by advertising through appropriate media or by asking them to take part in. Collect data from those who respond.

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**6.3 Non-probability sampling**

Convenience or haphazard sampling It involves selecting haphazardly those cases that are easiest to obtain for your sampling, such as the person interviewed at random in a shopping center for a television program. The sample selection process is continued until your required sample size has been reached. Often the sample is intended to represent the total population

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6.4 Summary Your choice of sampling techniques is dependent on the feasibility and sensibility of collecting data to answer your research question and to address your objectives form the entire population. Factors such as the confidence that is needed in the findings, accuracy required and likely categories for analyses will affect the size of the sample that needs to be collected.

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6.4 Summary Sample size and the technique used are also influenced by the availability of resources, in particular financial support and time available to select the sample and to collect, enter into a computer and analyze the data. Probability sampling techniques all necessitate some form of sampling frame, so they are often more time consuming than non-probability techniques.

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6.4 Summary Non-probability sampling techniques also provide you with the opportunity to select your sample purposively and to reach difficult-to-identify members of the population. For many research projects you will need to use a combination of different sampling techniques. All your choices will be dependent on your ability to gain access to organizations. The considerations summarized earlier must therefore be tempered with an understanding of what is practically possible.

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