Currently the whole country is on lockdown level 4. Meanwhile, our learners are losing valuable time to study. Our initial thought was to reduce the course fee for anybody who enrolls with Mentornet during the period of lockdown. The problem with this is that our current learners still cannot continue their studies and they do not benefit from our offer.

The 20% discount that we offered expired at the end of April. From 1 June 2020 and onwards, for as long as we cannot offer contact learning, we would rather give all new enrolments a laptop computer for free. This will enable them to do the courses online. We will also provide a video on how to use the online platform with every laptop. This will replace the discount and will apply for all future learners.

Currently, enrolled learners can purchase a laptop from Mentornet at a discount price, calculated on the number of days contact learning that they already completed. The details can be obtained from our Training Manager – you know her, and you can phone her on the Mentornet telephone number (012 653 29) or send her an email to evette@mentornet.co.za.

Only current learners who agree in writing to switch to online learning will qualify for a discount. New learners will, of course, need to enroll for online learning and not for contact learning.

Should the government decide that we may offer contact learning again, you will need to pay for the laptop, or pay the discount back, should you wish to switch back. You may then keep the laptop and switch over to contact learning. Mentornet, however, reserves the right to decide not to offer contact learning again regardless of what government decides if we feel that the health and lives of our learners and staff members will be at an unacceptably high risk.

In closing, it is unlikely that any private learning institutions will be allowed to offer contact learning again before we reach level 2. Now all universities and colleges must offer online learning only. Regardless of the levels, the viruses are still with us and they are as contagious and lethal as they were from the beginning. Do not let the fact that we cannot see them, and that government might relax the protective rulings fool you into thinking that the threat is over. Nothing will stop them from hunting us until we find an effective vaccine.

Dr J.P. Nel, CEO Mentornet

Written by Dr J.P. Nel, D. Com, D. Phil, Owner of Mentornet (Pty) Ltd

As much as I understand and sympathise with the pleas of businesspeople who wish to be allowed to do business without lockdowns, I do have a different stance to this terrible dilemma in which we find ourselves.

I am deeply concerned about businesses and business leaders who think that they will be able to carry on like before COVID-19. We need to accept change and we need to adapt to it. If we do not, most businesses will discover that doing business now will leave them drifting like the oil tankers on the oceans – empty.

Management of change can buy time and time is the scarcest and most expensive commodity on earth. For example, most governments, including the South African government, decided to follow a lockdown policy in order to delay the rate at which people contracted and perished because of the COVID-19 virus.

No government can stop the virus now. Therefore, no government can help us to do business and to earn a living. But government has a little bit of money (for now) with which to provide food to those in need, and they are rapidly growing in numbers. The point is this – government can play for time in the hope that a vaccine for the virus is found, but government cannot rekindle the markets that used to be.

Many financial experts believe the lockdown system will cost more lives than if the virus is allowed to run its natural course. They argue that the damage to the economies of countries and industries would lead to escalating unemployment, poverty, social unrest and crime, with the result that more lives will be lost than would otherwise have been lost through the virus.

Businesspersons believe that it is only the vulnerable that perish because of the virus. They should redo their homework – the virus no longer discriminates, and the businesspersons who think they can do business as before might well become the victims of the virus if the lockdown is lifted too soon, which is what is happening right now. Just look at what is happening in the USA at the moment, if you need a case study to learn from.

Strategic management of change rests on the premises that you should never just let things run its natural cause. You always, and as rapidly as possible, need to take steps to eradicate the threat. Nobody knows when a vaccine against the COVID-19 virus will be discovered and made available, especially to “unimportant” countries like South Africa. People would have died because of the virus, regardless of whether lockdowns were instituted or not. Only, they would have perished much faster if nothing were done to gain at least some control over the spread of the disease.

Delaying the pandemic would give medical scientist time to seek a solution. If the pandemic is allowed to continue unchecked many people who might have been saved if they were given more time, will perish. It would have been easier for government to gain some control over unrest and hunger because these are known factors to us. They currently have no control over COVID-19.

One of the fiercest enemies of business is procrastination. Military people would say that a static tank is a dead tank. Also, in business you need to move if you are to survive. A true and committed entrepreneur would use the time to plan, improve, position the business strategically, close new agreements, get rid of old and inefficient agreements and procedures.

Anything that you can do to bring your business closer to your vision is a step in the right direction. Lockdown is a step in the right direction, even if it is a detour. In fact, this is the time when entrepreneurs should review their visions and missions for their business. This is the time to adapt to change, and lockdown gives you the time and opportunity to do so.

    In closing, regardless of whether we lockdown or not, the end result of this pandemic will be terrible. The best we can do is to hope and work for a solution and to position ourselves for a completely new world and business environment. Those who can adapt will survive, those who resist change will not.

Introduction

Change starts when someone sees the next step.

This is often true, and it is when the need for change becomes critically important that somebody will be motivated enough to apply their minds to finding a solution.

There are many examples of people who discovered wonderful solutions for problems when it looked like everything was lost.

And yes, wars often stimulate the creative thinking ability of people to discover new inventions.

The jet engine is an example of an invention that was at least sped up by the second world war.

I will discuss transformative research in this article.

Transformative research deals primarily with research on change.

We are living in a dynamic environment where environmental, economic, technological, political, legislative, health and social change are the order of the day.

Transformative research focuses on the discovery and development of new ideas, procedures, products, etc.

Change can take place in any field of study, operations, or industry.

Transformative research challenges our current understanding and ways of doing things.

It provides new ways in which to do things, solve problems, even how we perceive life and the world around us.

Transformative research is often not planned.

Examples of things that were discovered by accident include penicillin, post-it notes, saccharine and the pacemaker.

It depends on a receptive and open mind.

It takes advantage of unpredictable events leading to novel hypotheses that might sometimes seem implausible.

It begins with learning, development of new ideas, visualization of problems and exploration of problem-solving techniques.

Communication and debate often prove beneficial in allowing the development of transformative ideas.

Accepted dogma is not allowed to stand in the way of the search for the truth in terms of a problem statement or hypothesis.

Researchers making use of a combination of qualitative and quantitative methods often favour a transformative research approach.

A good measure of logic, wisdom and creativity is necessary when using transformative research.

Cognitive errors can lead to serious, perhaps even tragically destructive implementation.

Experimenting with faulty assumptions can lead to serious damage.

Irresponsible and shoddy research can destroy industries, even countries and populations.

Tainted research with short-term political or economic gain in mind can lead to serious long-term damage.

Research on global warming and the resulting climate change is an example of this.

You can probably think of even more radical examples.

Despite a rather liberal approach, you should keep in mind that researchers remain accountable for their findings and the consequences of their work.

You should, therefore, work in an academic atmosphere and make use of reputable data sources and research methods.

Your research findings must be logical, accurate, authentic and valid.

The university, notably your study leader, will require of you to motivate your arguments and prove or at least explain the validity of your findings.

You will probably experience a feeling of elation and personal revelation when you discover something new or come to appreciate newly found information.

Discovering new knowledge or ideas may depend on optimism and hope.

And the development of the concept usually relies on persistence and mental discipline.

It is often claimed that revelations or discoveries happen by chance.

However, it is possible that you just had a better understanding of a system, keener observation or a better ability to think analytically than others.

New knowledge will change you and the environment in which you do your research.

Doctoral studies should lead to such intellectual evolvement and contribute new knowledge that can be used in a field of study.

Transformative discoveries leading to paradigm shifts can effect change at many levels and fields of study.

When this happens, there will often be sociological stages of resistance to the change.

First, the change is denied or ridiculed.

Then some people might get angry and resist the change, and

Finally, they will accept the change.

Some people might even claim that they knew all along that the change would happen.

Or that it was their idea.

Transformative research does not always lead to change.

You can expect to stumble upon some inaccuracies, especially in the beginning.

Creativity and an open mind invite trial and error, leading to a gradual progression towards new concepts and ideas.

Although sudden and unexpected change can happen.

However, observations and findings are often only approximations.

Summary

Transformative research deals with the search for change.

New ideas, procedures or products are often sought.

Change can be discovered by chance.

It can also be triggered through an open mind, creativity and analytical thinking.

Communication and debate facilitate transformation.

You need to be careful of making cognitive mistakes, because it can sometimes lead to serious damage.

Keep in mind that you are accountable for the outcomes and consequences of your research.

People sometimes resist change.

It can start with denial, followed by anger and resistance, and finally acceptance.

Transformative research does not always deliver creative solutions.

Change can happen suddenly, but it is mostly the result of a gradual process of transformation.

Written by Dr Hannes Nel

Introduction

It is said that Albert Einstein wrote in a letter to his daughter in 1938 that there is an extremely powerful force that governs all the universe, a variable that scientists often forget.

He confessed that he omitted the variable when he developed the relativity theory.

That variable is love.

I don’t know if the story is true.

And if it is true, then Einstein probably wrote a letter to his daughter in which he declared his love for her in a most romantic and creative manner.

Even so, it made me think. When scientists start their findings by writing: “all other factors being constant,” they are actually admitting that their findings are wrong.

Because other factors are never constant.

I guess I am just being difficult because it is sometimes necessary to investigate the influence of single factors on an event, behaviour or phenomenon.

Even so, the need for considering the interrelationship between different factors are also important.

I will discuss the following issues related to statistical research methods in this post:

1. Determining validity from statistical data.
2. Calculating statistical significance.
3. Analyzing statistics.
4. Coming to conclusions from statistical data.

Determining validity from statistical data

Statistical validity refers to whether conclusions drawn from a statistical study agree with statistical and scientific laws.

There are different kinds of statistical validities that are relevant to research.

The following are examples of such statistical validities.

1. Construct validity. Construct validity ensures that the results of the data that you collected conform to the theory of your research.

For example, a questionnaire on the quality of learning provided by universities and completed by employers must provide a true picture of the value that university studies have for the workplace.

• Content validity. Content validity ensures that the test or questionnaire that you prepared covers all aspects of the variable that is being studied.

For example, if you were to do research on the exam paper that students studying towards a degree in accounting must write, then the exam paper must test all the exit level outcomes of the subject to have content validity.

• Face validity. Face validity is related to content validity and is a quick initial estimate to check if the test that you will conduct is in line with the hypothesis that you are investigating. It is, however, more subjective than content validity.

For example, if you were to do research on the exam paper that students studying towards a degree in accounting must write, and it looks like a good exam paper that meets the requirements for assessment, then on appearance the exam paper can have face validity.

• Conclusion validity. Conclusion validity is achieved when the conclusions that you reach from the data that you collected are accurate and justified.

This will be the case if the sample or samples that you used are large enough, randomly chosen and taken from the population being investigated.

• Criterion validity. Criterion validity measures how closely the results that you obtain with a data collection instrument matches that of a different instrument.

For example, if you use a questionnaire to measure the extent to which university studies add value to the workplace, and you measure the same research question by making use of a different, proven questionnaire that was used for the same purpose previously, then your questionnaire will have criterion validity if it delivers the same results, or at least nearly the same results, as the proven questionnaire.

• Internal validity. Internal validity is achieved if you can claim that the results that you achieved with your research can be contributed to the factors that you considered and not to other factors which you did not consider.

It is a measure of the inherent cause and effect relationship between the factors that you considered in your research.

For example, if you can prove that a certain symptom is an indication of only one specific illness, then your finding will have internal validity.

• External validity. External validity relates to how you apply the results of your investigation to the wider population.

It tells you if your findings apply generally or only to the target group of your research.

For example, if you can prove that your findings, based on a sample taken from a certain population also apply to any other group from any other population, then your research findings will have external validity.

Calculating statistical significance

Statistical significance means that you are sure that the statistics that you generated are reliable.

It does not necessarily mean that your findings are important.

Statistical significance can, for example, be skewed by the size of the sample that you investigate.

The larger the sample, the more significant will small differences between two variables appear to be.

Analyzing statistics

There are many ways in which statistics can be analyzed.

Numbers as such seldom provide a clear picture of any value for coming to conclusions.

Dedicated computer software will mostly provide you with such numbers.

Ultimately, however, it is up to you to interpret the numbers and to make sense of them.

For this purpose, you might need to summarize the data or rearrange it in tabular or graphic format.

Some computer programmes might do this for you. They might even interpret the data to an extent, but they will not come to conclusions or findings.

You might, for example, need to group statistics in different age groups, gender, nominal ranges, etc. to see the bigger picture from which to come to conclusions.

This can be made visual in the forms of tables, line graphs, bar charts, histograms, etc.

The computer software might do some handy calculations for you, for example calculating averages, also called the mean; medians (the midpoint of the data); mode (most common value in a set of data); range (the difference between the smallest and the largest value); standard deviation (the average spread around the mean); variance (the square of the standard deviation), etc.

Coming to conclusions from statistical data

Statistics are often used as a basis for coming to conclusions about presumed effects and relationships.

There are several principles of statistics that, if violated, can affect the inferences made from results as well as subsequent conclusions of the research.

Sophisticated statistics do not guarantee valid conclusions.

You will, therefore, need to obtain the assistance of an expert statistician to help you interpret statistical data if you are not one.

However, coming to conclusions and developing findings from them are still your responsibility.

Summary

The internal validity of conclusions is tested against statistical and scientific laws.

There are different kinds of validities, depending on how and against what your conclusions are tested.

Kinds of validity include:

1. Construct validity.
2. Content validity.
3. Face validity.
4. Conclusion validity.
5. Criterion validity.
6. Internal validity.
7. External validity.

Statistical significance is achieved if your statistics are reliable.

Reliability is often damaged when the size or composition of your sample is wrong.

Statistics can be analyzed by consolidating them in a table or graphs.

Some analysis and calculations are often done by computer.

You will need to come to your own conclusions based on your interpretation of the data.

Finally, you will need to derive findings from your conclusions.

Written by Dr Hannes Nel

Introduction

The value of qualitative research has never been amplified as it is now that we are facing a virus that will drastically change the world and how we interact socially and economically.

And we have never needed quantitative research as much as we do now if we are to survive the COVID-19 onslaught.

Now we know that both approaches to research are critically important if we are to survive and cope with the new world order that has only just begun.

Statistical research methods can be complicated and specific to a field of natural science.

Therefore, even if I had the knowledge, it would have taken up many more than the 40 posts that I posted so far to share it with you.

Besides statistical research methods mostly require the use of software specifically developed for the purpose of a field of research.

Those of you who wish to embark on such research will probably already know the computer programme that you will use for your research.

Else you will need to familiarize yourself with the software before you do your research.

I will discuss the following issues related to statistical research methods in this article:

1. Investigating a statistical hypothesis.
2. Conducting statistical regression analysis.

Investigating a statistical hypothesis

You will mostly use a hypothesis in statistical research, although it is also possible to base your research on a problem statement or question.

You will need to formulate two opposing hypotheses – the null hypothesis and the alternative hypothesis.

The null hypothesis, indicated with H₀, (H-naught) is a statement about the population that you believe to be true.

The alternative hypothesis, indicated with H₁, is a claim about the population that is contradictory to H₀. It is what we will conclude when you reject H₀.

A null hypothesis can often be proved or disproved by means of statistical research.

One of your samples will support the H₀ hypothesis while the other will support the H₁ hypothesis.

You will reject the H₀ hypothesis if the sample information favours the H₁ hypothesis.

Or you will not reject the H₀ hypothesis if the sample information is insufficient to reject it.

For example, your H₀ hypothesis can be:

30% or less of the people who contracted the COVID-19 virus lived in rural areas.

You can also write the null hypothesis like this: H₀ ≤ .3

You H₁ hypothesis will then be:

More than 30% of the people who contracted the COVID-19 virus did not live in rural areas.

You can also write the alternative hypothesis like this: N₁ > .3

You will also need to calculate the size of the sample that you should use with a certain accuracy probability.

Dedicated computer programmes will do this for you.

Once you have composed a sample that will give you some answers with an acceptable level or probability, you will need to interpret the data that was probably analyzed with dedicated software.

You will need to set certain norms, or criteria, for the analysis of the data that you collected for the population first.

The samples also need to meet those norms, criteria or parameters.

A null hypothesis needs to be proven by comparing two sets of data.

If you reject the null hypothesis, then we can assume that there is enough evidence to support the alternative hypothesis.

That is: More than 30% of the people who contracted the COVID-19 virus did not live in rural areas.

You will probably compare the mean of observations or responses for the two sets of data.

It might sometimes be necessary to use the mode, median or correlation between the sets of data.

Random variability between different samples will also always be present.

There might also be small differences between the statistical relationship in the sample and the population.

This can be just a matter of sample error.

Dedicated computer software will do the statistical calculations for you.

A null hypothesis does not “prove” anything to be true, but rather that the hypothesis is false.

If you cannot prove the two phenomena or populations to be different, then they are probably the same.

Then again, if the statistical analysis does not enable you to reject the null hypothesis, it does not necessarily mean that the null hypothesis is true.

Conducting statistical regression

Statistical regression analysis is a generic term for all methods in which quantitative data is collected and interpreted to numerically express the relationship between two groups of variables.

The expression may be used either to describe the relationship between the two groups of variables.

It can also be used to predict values, although one must be careful of trying to predict future trends based on statistical data.

The two data groups, popularly represented by X and Y, are compared numerically or graphically to identify a relationship between the items or groups of items X and Y.

You can mostly use such comparisons to determine trends and correlation between variables.

It might, for example, be possible to identify a correlation between the hours that a student spends studying and his or her eventual performance in the exam.

Correlation measures the strength of association between two variables and the direction of the relationship.

In terms of the strength of the relationship, the values of the correlation coefficient will always vary between +1 and -1.

A value of +1 indicates a perfect degree of association between the two variables.

That means that if one thing happens, then something else will also happen.

For example, if you cut your arm you will bleed.

A value of -1 indicates a negative relationship between two variables.

For example, the faster you drive, the less time will it take you to reach your destination.

For example, the decrease in the number of new individuals that test positive for the COVID-19 virus does not enable us to predict when the pandemic will come to an end.

You can, perhaps, argue that correlation enables you to predict what will happen to one variable if a second variable changes.

However, predicting that such change will take place is often difficult, if not impossible in social sciences.

You can predict with a good measure of accuracy what will happen if you add certain amounts of yeast to the dough for baking bread.

But you cannot always predict how the baker will respond if she or he serves you the bread and you criticize it.

The situation is different in exact sciences, such as chemistry, where the scientist can initiate the change and control the size, measure and frequency of change.

Summary

You will probably use two opposing hypotheses in statistical research.

The null hypothesis is a statement about a population that you believe to be true.

The alternative hypothesis should contradict the null hypothesis.

You will use to samples to prove or disprove your hypothesis.

The findings that you gather from your analysis of the samples should apply to the population as well.

There might, however, be a sample error of which you should take note.

Statistical regression analysis investigates the relationship between two sets of variables.

It can show a correlation between the sets of variables.

It can also sometimes be used to predict values.

I would rather call it “foresee” values, because prediction based on statistics can be risky.

Relationships can be compared numerically or graphically.

Correlation between two variables can be anything between +1 and -1.

A value of +1 would indicate a perfectly positive correlation.

A value of -1 would indicate a perfectly negative correlation.

Written by Dr J.P. Nel

Introduction

Some academics and other scientists are of the opinion that the COVID-19 tests can be inaccurate by as much as 30 percent.

And there are those who believe that the mortality rate due to COVID-19 is in some countries much higher than the reported figures.

It shows how easily quantitative research findings can be invalid, inaccurate and sometimes deliberately manipulated.

The same applies to the quantitative opinions of some social scientists.

I will discuss typical case sampling, unique-case sampling and volunteer sampling in this article.

Typical-case sampling

You can use typical-case sampling when you need to collect data on general items.

A typical-case sample is composed by identifying and including people who can be regarded as typical for a community or phenomenon.

Such a sample is used to avoid information, or worse, research findings and recommendations being rejected because they have been obtained from suspect, perhaps even deviant, cases.

It can also be used to avoid using data or opinions that are politically laden or otherwise manipulated.

A typical case sample should allow you, as the researcher, to develop a profile of what would generally be agreed as being average or normal.

Typical-case samples are often useful when large communities and complex problems are investigated.

Such samples enable those who are not familiar with a community to gain an understanding of the nature of the community in a relatively short time.

Members of the community can often help you to select items for a sample by suggesting criteria for such samples.

An example of a typical-case sample is when you choose your sample from a middle-class suburb rather than from a poor or rich suburb if you wish to do research on the spending habits of a city.

The spending habits of the poor and the rich would probably not be typical for the community.

Of course, your sample will be more representative of the community if you were to include poor, middle class and rich people in your sample.

But then you would be using random sampling or systematic sampling.

Unique-case sampling

In unique-case sampling, you would identify and include cases that are rare, unique or unusual in terms of one or more characteristics.

Such cases might agree with typical cases in other respects.

An example of unique-case sampling can be if you were to do research on the genetic makeup of a nation or tribe that has not been affected by the COVID-19 virus or has been affected markedly less than the average for other countries.

Volunteer sampling

As in the case of snowball sampling, you will need to take intentional steps to gain access to individuals who are difficult to establish contact with.

Also, as in the case of snowball sampling, you might need to rely on volunteers for your sample group.

Then again, most researchers rely on the co-operation of volunteers.

You would typically start by approaching family members, friends, friends of friends, etc.

You can also ask for volunteers by posting an advertisement in a newspaper, ask for help on social media, write letters to possible volunteers, visit managers of businesses and ask them for assistance, etc.

Making use of volunteers whom you do not know as your sample can be risky.

You often do not have any control over the validity and accuracy, authenticity or sincerity of their responses to questions or any other contributions to your research.

It will, furthermore, be rather difficult to claim that your findings apply generally if you do not know the motives or reasons why volunteers became involved in your research project.

Summary

Typical-case sampling can be used with good effect to avoid using subjective data. This is done by collecting data from a general population, items or phenomena.

Unique-case sampling is used to investigate rare, unique or unusual cases or phenomena.

In volunteer sampling, people are asked to volunteer to be included in the sample.

Volunteers need to be screened to ensure that they are authentic and trustworthy.

Written by Dr J.P. Nel

Introduction

On face value sampling seems like a simple job.

And so it mostly is.

However, if you choose the wrong sample or you choose the sample incorrectly, you can destroy the validity and accuracy of your research project.

And you might have wasted a good number of years on fruitless research.

Think carefully about the examples that I offer in this post, especially the ones on systematic sampling.

I will discuss:

1. Stratified sampling.
2. Systematic sampling.
3. Theoretical sampling.
4. Time sampling.

In this post.

Stratified sampling

Stratified sampling is used to ensure that the sample is representative of a population with multiple characteristics that are evenly distributed amongst the population.

The research population is divided into homogeneous groups, with each group containing subjects with similar characteristics.

Such homogeneous groups are often called ‘sub-units’ or ‘strata’.

Samples are selected by making use of random selection procedures such as systematic sampling or simple random sampling.

For example, students at a specific university, which are the population on which the research will be conducted, can be grouped into a male and a female group.

Or they can be grouped per faculty.

Or per leisure time interests, etc.

If, for example, you wish to do research on the impact of leisure time interest on academic performance, you will need to investigate a representative sample from each leisure time interest group.

Representivity is achieved by identifying the parameters of the wider population and taking them into consideration when composing samples.

So, in this example leisure time utility will be the independent variable.

Factors such as gender, age, occupation, academic year, etc. can be the dependent variables.

For the sake of coming to valid conclusions and findings, the process of organizing a stratified sample requires ensuring that the characteristics typical of the wider community are present in the sample.

If you draw more than one sample, you must also ensure that the samples are homogeneous in terms of at least one characteristic, which would be your independent variable.

In qualitative research, you will probably not calculate the size of the sample in terms of a statistical level of accuracy.

Therefore, representivity will depend on your own judgement.

You do need to take certain factors into consideration, though.

For example, the size of the population, time and cost constraints, access to data, the need for simplicity and the purpose of the reach.

Systematic sampling

Although some simple calculations need to be done with systematic sampling, it is not enough to be called quantitative research.

Systematic sampling is a modified form of random sampling.

For example, in a population of 10,000 individuals, you might feel that you should have a sample of, say, 200 people.

This will mean that you should select every first person from each sub-unit of 50 for your sample.

Fifty is, thus, your sample interval, i.e. the distance between each element selected for the sample.

You can choose numbers 1, 51, 101, 151, etc. for your sample.

If, for example, the first 57 people on the list that you use are the only females, you will have only two females in your sample.

This will probably distort your findings.

Another example of a skewed list would be if you work on a number of name lists for different faculties and the faculty lists are ordered normatively with the best performing student as the first name on the list and the poorest performing student as the last name on the list.

To use an extreme example, if there are fifty names on each list, your sample will include only the poorest performing students.

Periodicity can be a problem with systematic sampling if the list is not a fully random one.

For example, if you start by selecting the first name on the list, then names numbered 1 to 49, 50 to 99, 100 to 149, etc. do not have any chance of being selected for the sample.

You can eliminate this problem by ‘randomizing’ the list first by ‘shuffling’ the names like you would a deck of playing cards, and then selecting the sample.

You can also deviate somewhat from a systematic selection. A sampling procedure is not cast in concrete and you can adapt it to your research needs.

Just as long as it improves the randomness of your sample.

You can, for example, use systematic sampling coupled with the condition that both genders are fairly represented in the sample.

This can be achieved by splitting the name lists for males and females and then selecting a number of males and females from each list randomly.

Theoretical sampling

Theoretical sampling is the process of investigating incidents, events, occurrences or people over a period based on their potential to represent or demonstrate important theoretical constructs.

Researchers who follow an approach where deductions can be made from data with which theory can be developed or extended would be interested in finding individuals with the right makeup, or cases that embody theoretical constructs.

Theoretical sampling, therefore, is a special type of criterion sampling.

As the name implies, theoretical sampling is best used when the research focuses on theory and concept development.

Your goal would be to develop theory and concepts that are connected to, are grounded in, or emergent from real life events and circumstances.

To generate new theory will often require substantial research and is, therefore, a time-consuming process.

You will need to analyze and probably reconstruct and deconstruct existing theory until you manage to develop new theoretical ideas.

As opposed to purposeful sampling, you cannot know in advance precisely what to sample for and where it will lead you when you use theoretical sampling.

That is why theoretical sampling fits in well with grounded theory and why you will probably work towards solving a problem statement or question rather than hypothesis testing.

Time sampling

Time sampling is where observations of occurrences or events need to be taken and recorded at certain times.

You can use it if it is important to know the chronology of events.

Time sampling can be instantaneous, at specific intervals or measured in terms of how long it takes, i.e. duration.

Instantaneous sampling is achieved by observing an event or occurrence at standard intervals, for example every minute, hour, etc. on the dot.

You will need to take observations at a specific time and intervals that you planned on, or that are prescribed or necessary for whatever reason and you will take notes of your observations.

In the case of interval recording, you will observe and record events or occurrences during each interval.

This means that you will record observations during each interval for the entire time of the interval and not just at the moment each interval starts.

For example, you might wish to conduct research on the emotional state of a patient over a period and at set intervals, say every fifteen minutes for a day.

You can then count the number of times that the patient is happy, sad, angry, etc. and how the patient expressed the emotions.

The patient might have smiled, cried, laughed, etc.

You might also need to take note of what possibly might have triggered the emotion.

Duration sampling is where you will measure how long it takes for an individual or group to complete a certain task.

With duration sampling, you might, for example, need to record how long it takes the patient to overcome an anger attack or any other emotional state.

Or you might need to record how often the individual switches between different emotions, and many more.

The format of your notes will depend on the type of data that you wish to collect.

Time sampling will often require a quantitative research approach or at least some calculations.

This, however, does not mean that it cannot be used in qualitative research.

It might also be necessary to use a mixed approach, i.e. quantitative and qualitative research.

Summary

Stratified sampling is where you will select a representative sample from a diverse community. You will use random sampling to select the sample or samples.

In systematic sampling you will select individuals or items from a population at structured intervals.

In theoretical sampling you will select the sample based on certain criteria that will enable you to develop new theories and concepts.

Time sampling is where you will take samples at certain times. It can be instantaneous, at specific intervals or measured in terms of duration.

Introduction

Sampling types mostly differ only in the way in which the sample is composed.

Ultimately, samples are all small extracts from a larger population.

They are studied because entire population are too large for viable research.

Obviously, the sample should display behaviour, phenomena, events, etc. that would be typical for the entire population.

That means that the research findings based on the sample should be generalizable.

This, however, is not always the case.

Researchers sometimes limit their research to the sample without even trying to achieve generalizability.

In effect, what is supposed to be a sample becomes the population.

I will discuss the following sample methods in this article:

1. Probability sampling.
2. Purposive sampling.
3. Quota sampling.
4. Random sampling.
5. Reputational-case sampling.
6. Snowball sampling, and.
7. Stage sampling in this post

Probability sampling

A probability sample is drawn randomly from the wider population.

Therefore, it will be useful if you need to make generalizations.

Or if you seek representativeness of the wider population.

The name already reveals that it should be used with quantitative research, where statistical calculation will be made.

Probability or representativeness is calculated with a wonted measure of accuracy probability.

For example, 97% (giving an error factor of 3%).

The risk of bias is less than if you were to rely on your gut feeling or subjective judgement.

However, all samples, regardless of whether they are based on statistical calculation or your judgement, are hampered by a measure of sample error.

Purposive sampling

Purposive sampling is also sometimes called purposeful sampling or judgmental sampling.

In purposive sampling the researcher collects data according to the aims of the research.

Sample items are selected according to a list of important characteristics.

You will need to know the profile of your target group to develop a purposive sample that will sufficiently represent the population.

Purposive samples can be selected after you have studied the bigger population from which to select the sample.

This might require fieldwork or studying written profiles, such as curriculum vitae.

It can also require fieldwork as well as literature study.

You will be on the lookout for certain types of individuals displaying certain attributes that are needed for the research.

Categories such as age, gender, status, role or function in organizations, stated philosophy or ideology, may serve as starting points.

Information-rich cases are selected for study in depth.

As your research progresses, new categories may be discovered which would lead you to more sampling in that particular dimension.

Quota sampling

Representative individuals from a specific subgroup are chosen in quota sampling.

For example, numbers in accordance with the demographic composition of the total population.

You can simplify the process by first preparing a matrix table that creates cells or strata.

You may wish to use gender, age, education, or any other attributes to create and label each stratum or cell in the table.

Which attributes are selected will have to do with the research question and study focus.

The quota sampling process then uses a nonprobability method to fill these cells.

Each category in the overall sample must be filled using the same recruitment procedure for the resulting groups to be comparable.

Next, you need to determine the proportion of each attribute in the full study population.

Let’s say you want to study perceptions of violence among people over age 50.

The research could create various age groups.  

For example, younger than 30; 30 to 39; 40 to 49; 50 to 59 and 60 and older.

You will need to determine the proportion of people in each age group.

The groups younger than 50 are included for comparison purposes.

Random sampling

In random sampling, each possible sample has an equal probability of being chosen as the sample.

The sample needs to be large enough to deliver findings that will apply to the entire target group with a reasonable accuracy.

This depends on the size of the population and the measure of heterogeneity.

The larger the population, the larger the sample drawn should be.

For populations of equal size – the greater the heterogeneity on a particular variable, the larger sample will be needed.

A random sample is mostly used when each element of the population has the same and equal chance of being selected to be part of the sample.

This is sometimes called ‘simple random sampling’.

The procedure used to draw the sample needs to ensure that as little bias as possible is present.

Drawing names from a hat, putting the names on a dartboard and throwing darts at them to choose sample items, the roll of dice are examples of how a random sample can be composed.

Some dedicated computer software can also select a random sample from a list of names.

Reputational-case sampling

Reputational-case sampling is a variant of extreme-case and unique-case sampling.

Like in the case of the extreme-case and unique-case sampling, you will choose a sample recommended by an expert in the field.

For example, you might be looking for individuals who have particular expertise that is most likely to advance your research interests.

Snowball sampling

Snowball sampling is sometimes also called chain referral sampling or respondent-driven sampling.

Snowball sampling is an effective way to locate subjects with certain attributes or characteristics necessary in the study.

Snowball samples are particularly popular among researchers who are interested in studying various classes of deviance.

Sensitive topics are often studied, and the sample often comes from difficult-to-reach populations.

In snowball sampling, you would identify a small number of individuals who have the characteristics in which you are interested.

These people are then asked to identify and put you in touch with others who also have the wonted characteristics.

For example, if you are doing research on prostitutes you need to find only one prostitute who would be willing to put you in touch with other prostitutes to get the snowball rolling.

It so happens that this sampling method is especially useful for sampling a population where access and obtaining co-operation are difficult.

The basic procedure of snowball sampling begins by identifying one or more people with relevant characteristics and then interviewing them or asking them to complete a questionnaire.

After interviewing the subjects or after they have completed a questionnaire, you would ask them for the names of other people who possess the same attributes as them.

In this manner, a chain of subjects driven by the referral of one respondent to another is formed.

Stage sampling

Stage sampling is an extension of cluster sampling.

It involves selecting the sample in stages, i.e. taking samples of samples.

Doing research on the value systems of universities, you can first select a cluster of universities as your sample, then certain faculties in each university as a sub-sample, then a number of students from each faculty as your sub-sub sample.

Summary

A probability sample is a random sample for which the measure of probable accuracy is calculated.

In purposive sampling, you would collect data based on the aim of your research. The characteristics of the population from which you will draw a sample will be taken into consideration.

A quota sample is drawn from a population that represents the profile of your research target. The profile is based on your research question and the focus of your study.

In random sampling, each possible sample has an equal chance of being chosen for your research.

In reputational-case sampling, you will choose a sample recommended by an expert in the field.

In snowball sampling, you would identify a small sample or perhaps just one individual who meets the requirements for your research. This individual or small group can recommend other individuals or groups that meet your requirements.

Stage sampling is the selection of a sample in stages, that is taking samples from samples.

Introduction

I will discuss event sampling, extreme-case sampling, matched sampling, multi-phase sampling, non-probability sampling and opportunistic sampling in this article.

Event sampling

Event sampling is a form of research more than a data collection method.

You would typically select a small group of individuals to do the research on or about.

It requires counting the number of times that an occurrence is observed or a statement, word, etc. is found in a data source, for example a document.

Counting words, statements, etc. in a document is easy if it is available in electronic format.

An example of this is to count the number of times that the words “he” and “she” respectively are used in a book to determine if there is gender discrimination against one or the other.

When your research requires you to observe certain repetitive actions, you will need to develop statements or criteria for the action.

This can be compared to the cricket scorebooks that some clubs still use, although computer programmes for even that have been developed.

The task becomes more difficult if it is not only necessary to count the number of times a number of actions are repeated, but also the chronological order in which the actions took place.

An example of this is where you need to determine what kind of response a certain action invites, such as how people respond to a smile, compliment or insult.

Extreme-case sampling

Extreme-case sampling is used when information about unusual cases that may be particularly troublesome, or enlightening are sought.

Such a sample might represent the purest instance of a phenomenon that you wish to investigate.

Obviously, such cases are likely to be exceptions to the rule.

You might identify and investigate extreme cases in order to develop a richer, more in-depth understanding of a phenomenon.

You can also use extreme-case sampling to lend credibility to your research project.

Extreme-case sampling is often used in conjunction with other sampling methods.

In such events, the extreme-case will be investigated in the context of the other sampling method.

The extreme-case can be used as an outlier, that is an observation that takes on an extremely high or extremely low value, thereby strengthening the norm.

An extreme-case can also be investigated on its own.

For example, where you do research on a specific individual who is extremely gifted, rich, strong, etc.

Matched sampling

In matched sampling one member of a control group is matched to a member of an experimental group.

Matching is done randomly, but the independent variables that are considered important for the research are taken into consideration.

Pairs of participants are matched in terms of the independent variables.

For example, age, gender, academic qualifications, etc. and only then assigned to the control or experimental group.

Matching pairs are used mostly when experimental research methods are used.

This implies that it is more valuable to quantitative research than to qualitative research.

However, experimental research methods can be used with qualitative research methodology.

Therefore, matching pairs can also be used to compose samples for such research.

Multi-phase sampling

Multi-phase sampling is a form of stage sampling.

The difference between multi-phase sampling and stage sampling is this:

In the case of stage sampling, the criteria on each level are pretty much the same for each level of sampling in a particular region or field.

You will take samples of samples.

For example, your initial sample can be 1000 students randomly selected at a university.

Your next level sample can be 500 students who enrolled in psychology.

Your third level can be the 100 students who achieve the highest marks in the final exam.

In multi-phase sampling, the criteria change on each ensuing level.

For example, the first stage might be region, the second stage criteria can be students who excel in numeracy skills,

The third stage criteria can be students with blue eyes.

Non-probability sampling

Convenience sampling, quota sampling, dimensional sampling, purposive sampling and snowball sampling can all be conducted in a non-probability sampling manner.

Non-probability infers that the accuracy of the research findings need not be accurate to a good measure of probability, for example 90%.

In non-probability sampling, you will do research on a specific group to obtain knowledge about the group.

You will not claim general applicability of your findings.

The group, therefore, is important rather than any wider population.

This is often the case with small-scale research.

For example, just two or three faculties in a university,

Or just the lecturers in a small number of faculties.

Small-scale research often uses non-probability samples.

Meaning that not all available individuals have an equal chance of being selected for the sample.

You can select your sample based on your subjective judgement.

Such samples are far less complicated to set up than a randomly selected sample.

However, keep in mind that they probably do not represent a substantive population.

Then again, they are less expensive than random samples and can achieve the purpose of the research if the general applicability of the findings is not important.

Small-scale research can also be used to pilot data collection tools, such as questionnaires before they are used in actual research.

Opportunistic sampling

Opportunistic sampling means taking advantage of unanticipated events, leads, ideas, hints, and issues.

Accidental sampling is similar, but slightly different, as we saw in a previous post on convenience sampling.

The sample is composed of items upon which you stumbled by chance.

People who are available at the time of your study can be included in the sample.

They will need to provide valuable information and they must fit the criteria that you need.

A friend, who unknowingly shares valuable information with you at a party can be included in your sample.

Opportunistic sampling can be risky because the data that you collect can often not be generalized.

It might, furthermore, be difficult to corroborate the information.

Your source might not be reputable and might not have any credentials or qualifications making him or her an authoritative or expert source.

Summary

Event sampling is where you would do research on one or a small number of people, occurrences, or phenomena.

Extreme-case sampling also focuses on just one or a small number of people, occurrences, or phenomena. Only here you will look for an unusual case or cases.

In matched sampling members of a control group are compared in terms of independent variables to members of an experimental group, which would be the dependent variable.

In multi-phase sampling the sample is broken down in terms of different criteria for each phase or level.

Non-probability sampling is selecting a sample that does not necessarily represent the population.

The result of this would be that your research findings cannot be generalized.

Opportunistic sampling means using any data sources that you can find without deliberately selecting them.

Introduction

I discussed an introduction to sampling in my previous article.

I also discussed the first two types of sampling, namely boosted sampling and case study sampling.

I will discuss cluster sampling, convenience sampling, critical-case sampling and dimensional sampling in this post.

Cluster sampling

A large and widely dispersed population brings with it administrative problems.

Let’s say you want to investigate the impact of video games on teenagers.

It would be difficult to select and investigate a sample of, say, 1000 teenagers across the entire country.

You can reduce the time and work by selecting a smaller geographical area within the country.

This might have a negative effect on the generalizability of your findings.

It depends on your purpose with your research if this would be a problem or not.

But at least your research topic will now be viable.

This is an example of cluster sampling.

You can go one or more steps further by subdividing the geographical region into towns and towns into suburbs.

This is called ‘multi-stage cluster sampling’.

The obvious risk with cluster sampling is that you can build in bias by excluding some possibilities.

Limitations can be:

• Younger teenagers will probably not be affected the same as older ones.
• Teenagers in rural areas will not be affected the same as children living in urban areas.
• There are many other possible variables that can have an impact on the results of the research.
• For example, single-parent versus both parents in the family, personality traits, home conditions, etc.

The type and purpose of the research will determine if cluster sampling should be used or not.

Convenience sampling

Convenience sampling means selecting inputs and data from whoever happens to be available.

It is, thus, a rather opportunistic type of sampling.

It can also be accidental in nature, although there is a subtle difference between convenience sampling and opportunistic or accidental sampling.

Convenience sampling means making use of sources of information that you know of and that are easy to obtain.

Opportunistic or accidental samples are sources of information that you are not aware of in advance and that ‘falls into your lap’.

Convenience sampling is not a scientific way in which to collect data or to compose a sample.

It involves choosing the nearest individuals to serve as respondents.

It also means continuing the process until the required sample size has been reached.

Captive audiences such as students in a class are sometimes asked to discuss issues or complete questionnaires.

Convenience sampling is unlikely to deliver generally applicable findings.

Therefore, you will only use convenience sampling if achieving generalizable findings is not important to you and your research.

You must acknowledge that your findings are not applicable to a larger population than the small sample that you selected for your research.

It is even possible that your findings apply to your sample only.

Critical-case sampling

Critical-case sampling is sampling where it is important to obtain maximum applicability.

If the information and findings hold true for a critical case, it is likely to hold true for other cases and communities as well.

Critical cases are those that are likely to yield the most or most important information that will prove a hypothesis or solve a problem and that will have the greatest impact on the development of knowledge.

When selecting a critical-case sample, you would be looking for a ‘decisive’ case that would help you decide about which of several different explanations for a phenomenon, event or behavior is most plausible or most widely regarded as representing a general profile of a community.

For example, if one or a small number of people living in a jungle somewhere proves to have an immunity to the venom of a snake that can be found in that part of the world, one can probably deduce that there are people who have developed an immunity to the venom of that snake.

It does not necessarily mean that the tribe from which the sample was taken are immune to the venom of that snake.

This is a rather dramatic example and one will probably need to do more research and tests before you can claim with certainty that all people belonging to that tribe are immune to the venom.

It would also be interesting to do additional research on why people are immune to the venom.

But please keep in mind that this example is a figment of my imagination – I don’t think there is such a tribe or snake.

Critical-case sampling is an efficient way in which to conduct research.

It need not be expensive because only one or a few items should be enough to prove your hypothesis or solve your research problem statement or question.

Dimensional sampling

Dimensional sampling a refinement of quota sampling.

It can be used to reduce the sample size.

Dimensional sampling means selecting participants in the sample group in terms of a combination of criteria that you feel they should meet.

For example, when doing research on the correlation between perseverance and cognitive thinking ability, you can draw up a table with criteria for perseverance at the top (as column headings) and cognitive thinking ability down the side (as row headings).

A table like the one in the example can easily be converted into graphs of different types.

Summary

Cluster sampling is mostly used where the population is widely dispersed.

You will need to choose a cluster in the population that is representative of the population and that can realistically be investigated.

Convenience sampling means accepting any relevant data sources that you can easily find for your sample.

Convenience samples can be found by accident or coincidently.

Critical-case sampling focuses strongly on the purpose of your research.

The sample will often be too small to deliver generalizable findings.

Dimensional sampling is sampling where different characteristics of the sample are compared in the hope that the intersections of comparative data will deliver significant findings.