Study unit 6: Sampling

Question 1

define population

Answer 1

the entire group of persons or objects and events of interest to the researcher

Question 2

what is accessible population

Answer 2

The population that the researcher can reach is defined as the accessible population or study population

Question 3

define elements

Answer 3

An
element is the unit or case about which information is obtained. The elements may be people who share certain characteristics (eg the same profession). Elements may also be objects, events, social groups, organisations, documents or provinces

Question 4

define parameter

Answer 4

A
parameter is a specified characteristic obtained by studying all the elements of a population

parameter is thus a measure or value collected from a population; a parameter describes a particular characteristic of the whole population.

Question 5

define sample

Answer 5

A
sample is a part of a whole (the whole is shown in figure 6.1), or a subset of measurements drawn from the population. A sample, then, is a selected group of elements from a defined population

Question 6

define samplying frame

Answer 6

The
sampling frame is a comprehensive list of all the units or elements in the target population. Note that a sampling frame is not always available. The researcher often has to prepare a sampling frame which contains a complete target population. An adequate sampling frame should not exclude any element of the population

Question 7

define a representative sample

Answer 7

A
representative sample means a sample that resembles the population in as many ways as possible and that allows the researcher to accurately generalise the results. A representative sample should replicate the population properties in approximately the same proportion as they occur in the target population

Question 8

define sampling error

Answer 8

Sampling error refers to the differences between population parameters (eg the average age of the population) and sample statistics (eg the average age of the sample)

Question 9

what is aim of sampling theory

Answer 9

sampling theory is to determine samples which reproduce, as closely as possible, the characteristics of a population, sampling never completely achieves this aim owing to the sort of error we have just been discussing.

Question 10

provide sampling error factors

Answer 10

chance factors,
in a particular sample one element may have been included rather than another. This type of error is called a chance factor error and can be calculated statistically

bias in selection
arises primarily from faulty technique and may or may not be deliberate.

non-response errors.
This occurs when an element of the population does not respond to a measurement instrument (for some unknown reason). These elements are then excluded from the sample, which changes the make-up and therefore the representativeness of the sample.

Question 11

what is sampling bias

Answer 11

when there is a difference between sample data and population data that can be attributed to an incorrect selection process;a threat to external validity of a study that occurs when subjects are not randomly selected from the population

Question 12

what are same causes of sampling bias

Answer 12

ƒThe language used when data is collected (thus excluding those who do not speak that language).
ƒ ƒThe extent to which personal views have influenced the data.
ƒ ƒThe time the data were collected (thus excluding those who are not present at the time).
ƒ ƒThe place where the data were collected

Sampling bias therefore refers to the overrepresentation or underrepresentation of a segment of the population which will then impact on the purpose of the study and its validity

Question 13

what are the essentials for samples

Answer 13

how similar or dissimilar is the population? A population that consists of people who are similar to each other is known as a homogeneous population. A population of people who are dissimilar is known as a heterogeneous population

The second important factor is the degree of precision with which the population is specified. We can be more confident that our sample is representative if we have carefully defined our population. The defined population from which the sample is drawn is called the sampling frame

Question 14

what is the probability sampling approach

Answer 14

Probability sampling or random sampling refers to an approach whereby each person (element) has an equal chance of being chosen in the sample. Gilbert (1993:71) refers to an equal chance as a known, non-zero chance of selection. In other words, random selection takes place when each element in the population has an equal, independent chance of being selected for the sample

Question 15

what is simple random sampling and the techniques thereof

Answer 15

Simple random sampling is the most basic of the probability sampling techniques. Once the population has been defined, the sampling frame is drawn up. Each element of the sampling frame then has an equal chance of being included in the sample

ƒThe most common are the lottery or fishbowl techniques. A symbol for each element (unit) of the population is written on identical pieces of paper and placed in a container, mixed well and then one number is drawn at a time. The sample size the researcher decided upon will determine how many papers are drawn.
ƒ ƒAnother technique that can be used is a random number table. This table is drawn up mathematically, so that the numbers are written in a random way, in rows or columns.

Question 16

what is systematic sampling of the sampling approaches

Answer 16

Systematic sampling (or interval sampling) involves drawing every ƒth element from a population. Elements are selected at equal intervals (eg every sixth, tenth or nineteenth element).

Systematic sampling works as follows:
1. Obtain a list of the total population (N). An example could be all the guest houses listed in the portfolio for guest houses in South Africa. The elements in this list must be listed randomly and not alphabetically, otherwise bias may occur in that the sample selection may not be truly representative of the population.
2. Determine the sample size (n).
3. Determine the sampling interval (k) by dividing the size of the population by the size of the sample. Choose a random starting point
between 1 and k. You could use a table of random numbers to do this. Select the other elements based on the sampling interval (k). For example, if there are 300 guest houses listed in the portfolio and the sample size is 60, the sampling interval is five. Suppose that the selected starting point is 3, the next elements would then be 8, 13, 18, etcetera, up to 298.
K=N/n=size of population
size of sample
Hence, in the example k = 300/60=5
Systematic sampling is more convenient than simple random sampling. If careful attention is paid to obtaining an unbiased list of the population elements and the first element is randomly selected, systematic sampling is classified as probability sampling. If any one of the above criteria is not met, it is not considered to be systematic sampling.

Question 17

what is stratified random sampling

Answer 17

Stratified random sampling is a sampling technique where the population is divided into different groups or subgroups called strata, so that each element of the population belongs to one and only one stratum. Random sampling is then done within each stratum, using either simple random sampling or systematic sampling.

Proportional stratified samples are samples where the number of elements selected from each stratum is proportional to the size of the stratum in the population.This type of sampling design ensures that each stratum is properly represented in the sample and decreases the chances of excluding members of the population because of the stratification process.

Disproportional stratified sampling occurs when the number of elements in each stratum is not proportional to the number of elements in each stratum within the population

Question 18

what is cluster sampling

Answer 18

Cluster sampling requires that the population be divided into groups or clusters. Unlike stratified sampling, the elements of the population are grouped in heterogeneous clusters instead of homogeneous strata. Cluster sampling is used when a complete list of elements (sampling frame) is not available.
this method is often referred to as multistage sampling.

Question 19

what is nonprobability sampling

Answer 19

a procedure where we do not know whether we have included each element of the population in a sample

Nonprobability sampling is used when probability sampling is extremely expensive or difficult or when representativeness is not essential. Nonprobability sampling is appropriate where the researcher’s aim is to generate theory and a wider understanding of social processes (

Question 20

what is convience or accidental sampling

Answer 20

Convenience sampling (or accidental sampling) is when the researcher selects those elements that he or she can access easily until the sample reaches the desired size. Convenience sampling is also referred to as accidental or availability sampling

Question 21

explain quota sampling

Answer 21

Quota sampling is a nonprobability sampling technique similar to stratified sampling, except that the final selection of elements is not random. Quota sampling generally requires that each stratum be represented in the sample in the same proportion as in the total population

Question 22

what is purposive or judgemental sampling

Answer 22

Purposive or judgmental sampling (also referred to as theoretical sampling) is when the researcher selects a sample that can be judged to be representative of the total population. This judgment is made on the basis of available information or the researcher’s knowledge about the population. This knowledge is used to hand-pick the elements for the sample

Question 23

define snallball sampling

Answer 23

Snowball sampling is a technique that involves research respondents’ obtaining other potential respondents. The term is taken from the analogy of a snowball. In the first stage of sampling, only a few respondents are identified as having the required characteristics; these respondents are interviewed by the researcher. These respondents are then used to identify other people who qualify for inclusion in the sample. The next stage is interviewing the new persons, and so it carries on until the researcher reaches data saturation.

Question 24

define sample size

Answer 24

The sample size refers to the number of elements in a sample. There are no hard and fast rules for determining sample size. Instead, the researcher must consider the research purpose, the design, the size of the population and the type of sample used.