AC 1.2 present the survey methodology and sampling frame used Census and Sampling What is a census? What is sampling? AC 1.2 present the survey methodology and sampling frame used Assignment Task 1.2

Learning Outcomes By the end of the session, learners would be able to describe: Sampling and sampling terms Types of sampling methods Rationale for sampling Sampling methods Sampling errors and biasness

POPULATION & SAMPLING Census or population refers to the entire market segment we want to get information from. Sampling is a representative of the market segment or population The sampling method selected must be fair & accurate for the information to be statiscally reliable If the sampling method is incomplete and inaccurate, it is said to be biased.

What exactly is a “sample”? A subset of the population, selected by either “probability” or “non-probability” methods. If you have a “probability sample” you simply know the Probablity of any member of the population being included (not necessarily that it is “random.”)

Sampling Who do you want to generalize to? The Theoretical Population What population can you get access to? The Study Population How can you get access to them? The Sampling Frame Who is in your study? The Sample

Sample Design Benefits of Sampling Aim and Stages of Sampling Aims at avoiding bias and achieving maximum precision from a given outlay of time and money Stages in sample design: Decide the objective of the research Define the study population Choose a sample method Decide sample size Saves time Faster results Saves money Enables more surveys to be carried out Can concentrate on a small number of units A carefully chosen sample can yield statistically valid information about the population as a whole

Sampling Methods Nature of the population, time and money Unbiased (each object in the population to be equally chosen as part of the sample) Representative of the population (e.g. more female than male …) Population https://www.youtube.com/watch?v=be9e-Q-jC-0 Sample

Sampling frame Random sampling requires a sampling frame. The means of identifying sampling units. “A list of members of a population…” (Morris, 2000, p41) from which a sample can be chosen. Eg: Electoral Roll, telephone directory, etc. It should be comprehensive, complete, accurate and up to date. Care should be taken for any ‘Bias’: eg: ownership of telephone may indicate certain social class only.

Sample Bias A Sample is Biased if it differs from the population in a systematic way To be able to generalize from a sample population without bias, select random sample Selection Bias (exclude or under-represent part of the population) Measurement or response bias (measurement faulty ) Non response bias

Choice of Sampling Method Is a sample frame available? If so, random sampling is possible Is the population homogeneous or heterogeneous? What degree of precision is required? To lower the margin of error the sample size must be increased What is the budget for the exercise? Available budget Accuracy required How quickly the information is needed Accessibility of the population

Types of Sampling Non random sampling Involves human judgement Probability Methods Non-probability Methods Non random sampling Involves human judgement No sampling frame required Quota sampling Convenience sampling Judgement sampling Random sampling Does not involve human judgement Requires a sampling frame Simple random sampling Systematic sampling Stratified random sampling Cluster sampling

RANDOM SAMPLING METHODS SIMPLE RANDOM SAMPLING: This means selecting a sample with every item or respondent in the sampling frame having an equal chance of being selected. SYSTEMATIC SAMPLING: selecting items from the list at regular intervals e.g. every 5th customer that buys hamburger or every 10th car that buys petrol.

Cluster Sampling Population is divided into clusters then chosen at random (e.g. department of a business) Within a cluster all the objects are included in the sample If clusters are different from each other regarding to the element we measure it can introduce bias or non representative. More convenient than simple random sampling

Stratified Sampling Students Strata Random subsamples of n/N Similar to Cluster Complex to administer To make the sample more representative the population is divided into a number of strata (groups or levels). If the list of 600 students consists of 360 males and 240 females, the sample of 50 students is more representative if it reflects these proportions as follows: Number of males  360/600x50 =30 Number of females  240/600x50 = 20 Students Females Males Strata Random subsamples of n/N

Stratified Sampling Advantages • Every unit in a stratum has the same chance of being selected. • Using the same sampling fraction for all strata ensures proportionate representation in the sample of the characteristic being stratified. • Adequate representation of minority subgroups of interest can be ensured by stratification and by varying the sampling fraction between strata as required. Disadvantages • The sampling frame of the entire population has to be prepared separately for each stratum. • Varying the sampling fraction between strata, to ensure selection of sufficient numbers in minority subgroups for study, affects the proportional representativeness of the subgroups in the sample as a whole.

Non-probability Methods Convenience Sampling Quota Sampling This involves gathering information from anyone available for the interview, no matter their background, this is not a very reliable method because the respondents may not really be the ideal people to provide the information we require. Getting information only from respondents exhibiting certain characteristics e.g. sex, age, socio-economic group or other demographic details up to a maximum quota or number

Judgement Sampling This means the researcher using his or her own judgement to select respondents based on his belief that they fit quite into the profile of the people he wishes to get information from

HOW TO HAVE A GOOD SAMPLE Have an accurate and comprehensive sample frame or records such as electoral or census data, sales data etc. The sample population must be potential customers Choose a suitable sample size Identify the person who buys the product (always differentiate between customers & consumers)

Errors in research Sampling error Non response error Data collection error Data analysis error

Sampling Error The difference between the estimate of value obtained from a sample and the actual value Arises because a sample cannot exactly represent the population as a whole Bias as a consequence of the way in which a sample is structured or the way it was selected Sampling error can be reduced either by increasing the size of the sample or by improving the amount of knowledge on the structure of the market prior to drawing up the sample

Inarticulate respondents Ambiguous questions Data Collection Errors Errors in Analysis Leading questions Clerical errors Misrepresentation Inarticulate respondents Ambiguous questions Unintended interviewer bias Errors in completing pre-coded answers Omission of important factors in questionnaire design Error in statistical analysis Drawing the wrong conclusion Confusion between cause and correlation Misrepresentation of the data False definitions