Sampling
Random Sampling: This is a sampling method where every member of the population has an equal chance of being
selected. It could be as simple as asking every 10th person on a list. A random sample is meant to be an unbiased
representation of a group.
Advantages Disadvantages
It is easy to collect the data as you only ask every for Results maybe unrepresentative since the selection process is completely
example 10th person. This means that the data is easier down to chance. This means the results collected and bias and will lead to
to collect than having to ask everyone. This is relatively mistakes if used.
easy to use as a selection process. One of the best things
about simple random sampling is the ease of assembling
the sample. It is also considered as a fair way of selecting
a sample from a given population since every member is
given equal opportunities of being selected.
Because random sampling offers an unbiased selection Random sampling method is its need of a complete list of all the members of
and it is highly representative, it enables researches to the population. The list of the population must be complete and up-to-date.
draw clear conclusions from results gotten from the study. This list is usually not available for large populations. In cases as such, it is
Keep in mind that the main objective of doing research is wiser to use other sampling techniques. The list may be protected by privacy
the capability of making conclusions concerning the whole policies or require a lengthy process to attain permissions. Many lists will not
population from results obtained from sampling. This be in the public domain and their purchase may be expensive. There may be
means that the representativeness of samples got no single list detailing the population you are interested in. As a result, it may
through using random sampling can be used for making be difficult and time consuming to bring together numerous sub-lists to
generalizations concerning the specific population. create a final list from which you want to select your sample.
Random sampling allows firms to accurately estimate the In the case of human populations, to avoid potential bias in your sample, you
size of any potential sampling error as this will be based will also need to try and ensure that an adequate proportion of your sample
on the laws of probability. This means the business will be takes part in the research. This may require re-contacting non-respondents,
well prepared for any errors. can be very time consuming, or reaching out to new respondents.
Stratified Sampling: A method of sampling that involves the division of a population into smaller groups known as strata
that are mutually exclusive. In stratified random sampling, the strata are formed based on members' shared attributes or
characteristics.
Advantages Disadvantages
The biggest advantage of sampling that has been stratified is that it When examining multiple criteria, stratifying variables may be related
produces results that are both largely unbiased and accurate. When surveys to some, but not to others, further complicating the design, and
are conducted by via stratified sampling, they often produce data that is potentially reducing the utility of the strata.
more representative of the entire population because of the special
attention it pays to the smaller subgroups within the population. It is also
the best way to obtain results that reflect the diversity of the population in
question. This advantage makes stratified sampling much more effective
than simple sampling for large and diverse populations.
Relative to the simple random sample, the selection of units using a In some cases (such as designs with a large number of strata, or those
stratified procedure can be viewed as superior because it improves the with a specified minimum sample size per group), stratified sampling
potential for the units to be more evenly spread over the population. can potentially require a larger sample than would other methods.
Furthermore, where the samples are the same size, a stratified sample can Which increases the amount of time the research need and by the time
provide greater precision than a random sample. Because of the greater of completion it may be out-of-date in fast changing markets.
precision of a stratified sample compared with a simple random sample, it
may be possible to use a smaller sample, which saves time and money.
Using a stratified sample will always achieve greater precision than a simple Disadvantages of this method are the same as the disadvantages of any
random sample, provided that the strata have been chosen so that survey – there is always room for a sampling error. Also, it may not be
members of the same stratum are as similar as possible in terms of the right for every survey situation. For example, it wouldn’t make sense to
characteristic of interest. The greater the differences between the strata, use this approach when surveying a population whose subjects are not
the greater the gain in precision. similar enough to create subgroups or when there is not as much data
available from one subgroup as there is from the others.
Random Sampling: This is a sampling method where every member of the population has an equal chance of being
selected. It could be as simple as asking every 10th person on a list. A random sample is meant to be an unbiased
representation of a group.
Advantages Disadvantages
It is easy to collect the data as you only ask every for Results maybe unrepresentative since the selection process is completely
example 10th person. This means that the data is easier down to chance. This means the results collected and bias and will lead to
to collect than having to ask everyone. This is relatively mistakes if used.
easy to use as a selection process. One of the best things
about simple random sampling is the ease of assembling
the sample. It is also considered as a fair way of selecting
a sample from a given population since every member is
given equal opportunities of being selected.
Because random sampling offers an unbiased selection Random sampling method is its need of a complete list of all the members of
and it is highly representative, it enables researches to the population. The list of the population must be complete and up-to-date.
draw clear conclusions from results gotten from the study. This list is usually not available for large populations. In cases as such, it is
Keep in mind that the main objective of doing research is wiser to use other sampling techniques. The list may be protected by privacy
the capability of making conclusions concerning the whole policies or require a lengthy process to attain permissions. Many lists will not
population from results obtained from sampling. This be in the public domain and their purchase may be expensive. There may be
means that the representativeness of samples got no single list detailing the population you are interested in. As a result, it may
through using random sampling can be used for making be difficult and time consuming to bring together numerous sub-lists to
generalizations concerning the specific population. create a final list from which you want to select your sample.
Random sampling allows firms to accurately estimate the In the case of human populations, to avoid potential bias in your sample, you
size of any potential sampling error as this will be based will also need to try and ensure that an adequate proportion of your sample
on the laws of probability. This means the business will be takes part in the research. This may require re-contacting non-respondents,
well prepared for any errors. can be very time consuming, or reaching out to new respondents.
Stratified Sampling: A method of sampling that involves the division of a population into smaller groups known as strata
that are mutually exclusive. In stratified random sampling, the strata are formed based on members' shared attributes or
characteristics.
Advantages Disadvantages
The biggest advantage of sampling that has been stratified is that it When examining multiple criteria, stratifying variables may be related
produces results that are both largely unbiased and accurate. When surveys to some, but not to others, further complicating the design, and
are conducted by via stratified sampling, they often produce data that is potentially reducing the utility of the strata.
more representative of the entire population because of the special
attention it pays to the smaller subgroups within the population. It is also
the best way to obtain results that reflect the diversity of the population in
question. This advantage makes stratified sampling much more effective
than simple sampling for large and diverse populations.
Relative to the simple random sample, the selection of units using a In some cases (such as designs with a large number of strata, or those
stratified procedure can be viewed as superior because it improves the with a specified minimum sample size per group), stratified sampling
potential for the units to be more evenly spread over the population. can potentially require a larger sample than would other methods.
Furthermore, where the samples are the same size, a stratified sample can Which increases the amount of time the research need and by the time
provide greater precision than a random sample. Because of the greater of completion it may be out-of-date in fast changing markets.
precision of a stratified sample compared with a simple random sample, it
may be possible to use a smaller sample, which saves time and money.
Using a stratified sample will always achieve greater precision than a simple Disadvantages of this method are the same as the disadvantages of any
random sample, provided that the strata have been chosen so that survey – there is always room for a sampling error. Also, it may not be
members of the same stratum are as similar as possible in terms of the right for every survey situation. For example, it wouldn’t make sense to
characteristic of interest. The greater the differences between the strata, use this approach when surveying a population whose subjects are not
the greater the gain in precision. similar enough to create subgroups or when there is not as much data
available from one subgroup as there is from the others.
