Sep 2020 – Page 2

ARTICLE 45: Research Methods for Ph. D. Studies: Critical Race Theory

16th Sep 2020 No Comments

Written by Dr. Hannes Nel

Introduction

What is the truth?

Just think about it – 29 paradigms, 29 different ways in which the truth can be perceived.

And there are many more that I will not discuss because some of them are not suitable for research purposes. Some are concepts, others are value systems, a third group focus more on applications rather than research.

I mentioned in my previous post that paradigms should help us to achieve meaningful patterns and theories and that we should guard against subjectivity.

Accepting more than twenty-nine ways in which to perceive the truth is already gambling with objectivity, authenticity, validity and accuracy in our research.

If we, moreover, ignore our responsibility to do research in an ethical manner, our theses or dissertations will end up being fictitious novels.

I will discuss critical race theory and how it should be approached in this post.

Critical Race Theory

Critical race theory focuses on the application of critical theory in terms of race.

It objects to the perception of racial power, especially where it is overtly or covertly supported by legislation, which would render it institutionalized.

Institutionalized racism is the structures, legislation, policies, practices and norms resulting in differential access and opportunities between racial groups.

It can manifest itself in any situation where needs exist.

Such needs can be material, psychological, political, technological, social, economic or power needs.

In critical race theory intentional discrimination is resisted on all terrains where people are involved.

For example, universities, schools, employment in the private and public sectors, sport, etc.

Critical race theory favours an aggressive, race-conscious, approach to transformation.

Although the starting point is often simple racial inequality, political and legislative transformation can be even more important objectives.

Critical race theory is often used to combat racial discrimination, facilitate the upliftment and growth of disadvantaged communities, redress historical racial discrimination, etc.

Critical race theory focuses on discrimination of one race against another.

It is not the reserve of any one race, and the victims of discrimination can be a minority or majority racial group.

Critical race theory mostly investigates the achievement of racial emancipation and equality.

It can be addressed in any field of study, although social studies embrace the paradigm the most.

Historical and current incidents of racial discrimination are often used as evidence in support of a research problem, question or hypothesis.

Critical race theory is supported by structuralism.

For example, by investigating how legislation and cultural influences impact on the demography of a community.

In this respect, micro-aggression is often an element of research making use of a critical race theory perception.

Micro-aggression can be found in any community where a certain group might feel anger and frustration because of the way the perceived or real privileged elite threaten them or because of one or more privileges that they have at the expense of the discriminated or that the discriminated are denied.

This can erupt into riots, crime, or any other form of violence.

And, of course, such micro-aggression can become the topic of research.

Critical race theory can also be linked to critical theory, neoliberalism, feminism, romanticism, humanism, post-colonialism and post-structuralism.

Liberalism is in opposition with some of the values of critical race theory because of the former’s favouritism towards the elite, the rich and the noble.

Critical race theory and structuralism are also in opposition because structuralism promotes positions of power, which can have a detrimental effect on human relationships.

Positivism is also in opposition with critical race theory because positivism favours quantitative research while supporters of critical race theory feel that the analysis of numbers strip human interaction of its affective values.

Critical race theory is not always structured.

Although it often investigates legislation and cultural influences, the process can be aggressive and unstructured.

It can even include riotous advocacy campaigns.

Hidden motives can also be present.

Critical race theory is, unfortunately, sometimes misused to achieve political agendas and to oppress minority or even majority groups that are vulnerable.

Summary

Critical race theory investigates race-related issues.

It objects to institutionalized racial discrimination.

It often studies situations where needs exist because of the unfair treatment of a racial group.

An aggressive, race-conscious approach to transformation can be favoured.

Any field of study making use of any research method can investigate racial discrimination.

However, social studies predominate.

Micro-aggression is often an element of the research making use of critical race theory.

Critical race theory is supported by structuralism, critical theory, neoliberalism, feminism, romanticism, humanism, post-colonialism and post-structuralism.

Critical race theory is opposed by liberalism, structuralism and positivism.

Criticism against critical race theory include that:

It is not always structured.
Hidden motives can be present.
It is sometimes misused.

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ARTICLE 42: Research Methods for Ph. D. and Master’s Degree Studies: Paradigmatic Approaches

11th Sep 2020 No Comments

Written by Dr Hannes Nel

Introduction

Do you believe that something can be only true or false, right or wrong?

Do you turn your back on people with whom you disagree?

Do you agree with the notion that the truth is often the perception of an individual?

And do you accept that not all people see the truth the same as you do?

I hope my posts on paradigms will convince you that an argument or premise can be true for some but not for others, sometimes true but not always, only partially true, and true in one context but not in a different one.

An introduction to research paradigms

Most paradigms can also be regarded as research methods.

And what we call research methods are often data collection methods.

There are many paradigms, but not all of them can be used as the foundation for research.

Because some paradigms are only concepts that are too dependent on a specific context for the discovery of generalizations.

But even this is not a general rule.

Because your research, not just the paradigm, will sometimes be dependent on a specific context.

Relativism is an example of a paradigm that always applies to a certain context.

Some paradigms are modifications of classical paradigms.

Research paradigms are sometimes also called:

Philosophical perspectives.
Philosophical epochs.
Epistemological approaches.
Discipline matrixes.
Theoretical frameworks.

They represent certain assumptions and perceptions with respect to the nature of the world and how we know it.

A paradigm is a philosophy that includes certain patterns, structures and frameworks or systems.

It is a system of interrelated ontological, epistemological and methodological assumptions.

It includes scientific ideas and values that a group of researchers have in common regarding the nature of research and how it should be conducted.

The paradigm or paradigms that you use will add a philosophical perception to the clinical academic meaning of your research.

It also determines the spirit in which you will do your research.

Different groups of researchers see research differently.

That is why there are many different paradigms to choose from.

You should decide which paradigmatic approach you will follow in your research.

It is possible to adopt more than one paradigm.

You can even follow one paradigmatic approach in one section of your thesis or dissertation and a different one in a different section.

You can even use a paradigm as the foundation of just one argument in your thesis or dissertation.

Just as long as they don’t contradict each other.

And you need to be careful of not damaging the consistency of your arguments by making use of too many paradigms.

This can easily happen if you forget your arguments and stance in an earlier section of your report.

Your philosophical stance informs the research method that you will use and the way in which you will interpret the data that you collect.

By choosing a paradigmatic approach, you commit yourself to a particular stance while rejecting a good number of other possibilities.

This need not be a problem – you can always change your stance later while doing your research.

It can easily happen that you need to change your paradigmatic approach, because your knowledge and understanding will grow as you collect and analyze data.

That is great, because you need to be objective and flexible when you embark on doctoral or master’s degree studies.

Always keep an open mind and be prepared to admit it when you are wrong.

Fortunately, you have a computer that allows you to return to and review previous work as many times as might be necessary.

And you can change your mind without other people knowing it.

This applies to natural science as well as social science.

And obviously then also to quantitative and quantitative research.

You should choose your research paradigm with the research problem, question or hypothesis in mind.

Research paradigms allow for a variety of research methods that can be used.

The choice is not so much about the research method that you will use, but rather about your ontological and epistemological assumptions.

The challenge is to select a paradigm or combination of paradigms that are most suited for solving a research problem, question or hypothesis.

The choice of a research paradigm or paradigms should be made in the context of many and often competing influences.

Your personal preferences and many external variables will also play a role.

Even so, don’t get bogged down in too much soul searching and uncertainty about which paradigm to choose.

Study the paradigms carefully and select one to four that look like they fit in well with what you have in mind.

If you do not decide on a paradigm to follow, you will inevitably follow one that fits in with your personal preferences.

And you will not even know that you are following a paradigm if you don’t know them.

The danger of this is that you might switch around between different paradigms too often, with the result that your arguments might be confusing and perhaps even contradict one another.

This is especially true when you investigate a complex research question or hypothesis.

Consistency, structure and logic are critically important in writing a thesis or dissertation.

You run the risk of destroying those requirements if you don’t follow one or a few paradigms that articulates with your research question or hypothesis.

Using more than one paradigm improves the possibility that the knowledge that you develop will be comprehensive and generalizable.

You should choose your paradigm or paradigms early.

That is, when you structure your research approach and methods.

You can even specify your choice in your research proposal if it is doctoral studies that you are embarking on.

It will show your intent, motivation and expectations for your research.

You will need to make some philosophical assumptions when you decide upon a paradigm or paradigms because it will also impact om the focus of your research.

I need to emphasise, be careful of combining paradigms that are in opposition with one another.

This is necessary because opposing paradigms are often based on different ontological and epistemological assumptions.

They, furthermore, do not share a common vocabulary with shared meanings.

And there is no neutral ground from which to adjudicate the merit of the paradigms or their consequences.

I will point out such possible clashes when we discuss the paradigms individually.

In brief – technicist paradigms are often in opposition with interpretive paradigms while critical paradigms fit in somewhere between the two groups.

Being “in opposition with”; “challenged by”; rejected by”; “associated with”; or “disagree with” does not mean that different paradigms completely differ or disagree.

But rather that they agree or disagree in terms of certain characteristics and elements.

You need to be fully aware of the paradigmatic assumptions that you make.

And you need to consistently move from description to explanation in terms of your findings and conclusions without deviating from your paradigmatic assumptions.

Summary

A paradigm is made up of:

A philosophy.
A system of interrelated ontological, epistemological and methodological assumptions and perceptions.
Scientific ideas and values.

The paradigm or paradigms that you choose for your research:

Determines the spirit in which you will do your research.
Informs the research and data collection methods that you will use.
Adds a philosophical perception to the academic meaning of your research, and
Lends consistency, structure and logic to your thesis or dissertation.

The paradigm that you choose will probably apply to qualitative and social research or quantitative and natural research.

A mixed research approach is also possible.

You can change your paradigmatic approach at any stage during your research.

When choosing a paradigm or paradigms for your research, you should consider:

Your research problem or hypothesis.
The ontological and epistemological assumptions of your research.
The context in which you will conduct your research.
Your personal influences and preferences.
Many external variables that will be relevant to your research topic.

You can achieve coherence in your research process by articulating your research question or hypothesis and your research method to the paradigm or paradigms of your choice.

Don’t spend too much time and effort on trying to find the perfect paradigm for your research.

Close

In closing, it would be almost impossible to discuss all paradigms that you can find in the literature.

Academics do not agree which paradigms should be accepted as such.
Many paradigms overlap and echo the nature and elements of other paradigms.
Not all paradigms can be used as the foundation for research.

If everything goes according to plan, I will discuss the following paradigms separately in the twenty-nine posts following on this one:

Behaviourism.	11. Interpretivism.	21. Pragmatism.
Constructivism.	12. Liberalism.	22. Pre-modernism.
Critical race theory.	13. Modernism.	23. Radicalism.
Critical theory.	14. Neoliberalism.	24. Rationalism.
Empiricism.	15. Phenomenology.	25. Relativism.
Ethnomethodology.	16. Positivism.	26. Romanticism.
Feminism.	17. Post-colonialism.	27. Scientism.
Functionalism.	18. Post-modernism.	28. Structuralism.
Hermeneutics.	19. Post-positivism.	29. Symbolic interactionism.
Humanism.	20. Post-structuralism.

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ARTICLE 43: Research Methods for Ph. D. Studies: Behaviorism

11th Sep 2020 No Comments

Introduction

Many Baby Boomers will remember the teachers at school who would not allow you to verbally respond to their scolding and reprimands.

“Listen to me and don’t talk back!”, they would say.

The consequence of this was that sometimes, when you had a good reason for behaving in a manner that they did not approve of, you just kept quiet and took your punishment with a stiff upper lip.

Just to set the record straight – I am deeply thankful to every teacher that taught me at school.

They did what they thought was right and they always had the interests of their pupils at heart.

Of course, there were also the difficult teachers, but I was fortunate not to have such a teacher ever.

All right – almost never.

Teachers in those days adopted an exaggerated behavioristic approach towards pupils.

They reacted to what they saw and did not care to think about the reasons why children behaved the way they did.

Hello, my name is Hannes Nel and I will discuss the nature and elements of behaviorism in this article.

Behaviorism

Behaviorism is a set of doctrines that argue that human and animal behavior can be explained in terms of external stimuli, responses, learner history and reinforcement.

Behaviorists argue that the human mind cannot be known.

Therefore, it cannot be shown that human thinking has an effect in the individual’s behavior.

All mental states, including beliefs, values, motives and reasons can only be described, defined and explained in terms of observable behavior.

Any data of a mental kind should be regarded as unscientific.

Reinforcement can increase or decrease the desired behavior.

Thus, reinforcement of behavior can be positive or negative.

All human behavior can be understood in terms of cause and effect.

Therefore, research should focus on that which is determined by and is the product of the environment.

This implies that research should focus on observable behavior which can be objectively measured rather than on cognitive processes which can only be inferred.

Intentionality and purposiveness are excluded or regarded as less important.

Behaviorism is related to positivism because positivism also believes that understanding human behavior can be gained through observation and reason.

Behaviorism can also be associated with empiricism because both make use of experimentation, specifically experimentation with experience and the simulation of experience in research.

Symbolic interactionism is also related to behaviorism because both believe that learning takes place through the interaction between human beings, that is, external stimuli.

Both behaviorism and symbolic interactionism depend on language to convey and share research findings.

Consequently, the accuracy and validity of findings through both behaviorism and symbolic interactionism depend on the ability of the researcher to use language.

So, you might have noticed that behaviorism, positivism, empiricism and symbolic interactionism gain comprehension through the observation of cause and effect.

Here we have the possibility of using different types of paradigms together, because:

Behaviorism, empiricism and symbolic interactionism are predominantly interpretivist paradigms.
Positivism is a predominantly technicist paradigm.

Behaviorism disagrees with phenomenology because phenomenology considers experience through direct interaction while behaviorism takes external stimuli into consideration.

Behaviorism disagrees with constructivism because constructivism claims that understanding is gained through experience and reflection while behaviorism largely neglects the cognitive processes, especially reflection.

The same applies to pragmatism because pragmatism postulates that knowledge is gained through observation and interpretation.

Again, the difference is vested in cognitive processes.

The problem with behaviorism as a research paradigm is that changes in behavior without taking cognitive processes into consideration are often only temporary.

Consequently, it does not deal with subjective, but lasting, human meaning-making.

Some behaviorists, however, do recognize the fact that cognitive thinking and the accompanying emotions can influence behavior.

This would be called radical behaviorism.

A second criticism against behaviorism is that the causes of changes in behavior are not always scientifically corroborated.

Summary

Behaviorism argues that behavior can be explained in terms of external stimuli, responses, learner history and reinforcement.

The human mind cannot be known.

Cognitive processes can only be inferred.

Therefore, all mental states can only be described, defined and explained in terms of observable behavior.

Behavior can be improved or suppressed.

All human behavior can be understood in terms of cause and effect.

Behaviorism is related to positivism, empiricism, and symbolic interactionism.

Behaviorism disagrees with constructivism and pragmatism.

Criticism against behaviorism includes that change without cognitive processes will probably be temporary.

And that the causes of changes in behavior are not always scientifically corroborated.

Close

In closing, There are four ground rules for research that one should meet regardless of which paradigm or paradigms you use.

Firstly, you should not ignore cognitive processes.

Secondly, you should always try to integrate and systemize your findings into a meaningful pattern and theory.

Thirdly, keep in mind that text constructed by human beings is fallible.

Therefore, you must always corroborate your data and findings.

Fourthly, personal preferences can damage the accuracy of your data collection, analysis, conclusions, finding and recommendations.

Despite paradigms not always supporting all four these ground rules, I strongly recommend that you keep them in mind.

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ARTICLE 41: Research Methods for Ph. D. and Master’s Degree Studies: Transformative Research

10th Sep 2020 No Comments

Written by Dr. Hannes Nel

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, it is during times of crisis that people often perform at their best.

War, financial depression, a pandemic seem to stimulate the innovative skills of people.

When caught with their back against the wall, people discover the most remarkable solutions.

The jet engine, electronics, vaccines are examples of such inventions.

Transformative research deals primarily with research on and for change.

I discuss transformative research in this article.

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 unforeseen 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 for transformative research.

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

And 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.

You 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 a concept usually relies on persistence and mental discipline.

It is often claimed that revelations and 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.

Master’s degree studies can create an awareness of the need for change.

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.

Change mostly requires persistence and hard work.

Although sudden and unexpected change can happen.

However, observations and findings are often only approximations.

But it is the next step that is needed to trigger change.

Ontologically transformative research evolved from a paradigm to a full-fledged research method.

Paradigms that can be used in association with transformative research include functionalism, liberalism, pragmatism and radicalism.

Because of its focus on everyday life, ethnomethodology opposes transformative research.

Modernism is too bureaucratic, prescriptive, procedural and structured to be used with transformative research.

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.

Paradigms that focus on change and survival fit in well with transformative research.

Paradigms that focus strongly on structure, bureaucracy, prescriptions and procedures do not fit in well with transformative research.

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ARTICLE 40: Research Methods for Ph. D. and Master’s Degree Studies: Statistical Research Methods Part 2 of 2

8th Sep 2020 No Comments

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 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 article:

Determining validity from statistical data.
Calculating statistical significance.
Analyzing statistics.
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.

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 papers that students studying towards a degree in accounting must write, then the exam papers 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 more subjective than content validity.

For example, if you were to do research on the exam papers 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 papers 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 attributed 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:

Construct validity.
Content validity.
Face validity.
Conclusion validity.
Criterion validity.
Internal validity.
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.

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ARTICLE 39: Research Methods for Ph. D. and Master’s Degree Studies: Statistical Research Methods Part 1 of 2.

6th Sep 2020 No Comments

Written by Dr Hannes Nel

Introduction

Technicist scientists are of the opinion that natural science is the most reliable path to the truth.

They, furthermore, claim that the truth can only be known through scientific proof.

Truth is based on logic, they say.

They believe in the singularity of meaning.

Something is either true or false – there is no in-between.

That is why they often base their research on hypotheses rather than a research question or statement.

They also believe that the truth can be proven and expressed numerically.

And they believe that the truth is not dependent on context or time.

Social scientists do not agree.

They feel that experience and reflection should not be neglected.

Individual and group perceptions can often be the truth.

The truth can be different in different contexts and at differ times.

Logic is simply common sense.

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

Investigating a statistical hypothesis.
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

Your 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.

It is possible that 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 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 two 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.

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ARTICLE 38: Research Methods for Ph. D. and Master’s Degree Studies: Sampling Part 6 of 6

5th Sep 2020 No Comments

Written by Dr. Hannes 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 rates due to COVID-19 in some countries are much higher than the reported figures.

Perhaps it is true that findings and statistics are sometimes deliberately manipulated.

Then again, imagine how near impossible it must be to conduct research on an issue that affects the entire world.

Researchers often have no other choice than to make use of sampling.

And sampling facilitates efficiency and viability, not validity and accuracy.

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 die COVID-19 virus, or has been affected markedly less that 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 research 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.

This is my last video on sampling, Methods. Therefore, I guess it would be fitting if I were to summarise the links between the different types of sampling. Here it is:

Cluster sampling is a form of stage sampling.
Convenience sampling is a form of opportunistic sampling.
Convenience sampling, dimensional sampling, quota sampling, purposive sampling and snowball sampling can be conducted as non-probability sampling.
Extreme-case sampling is used in conjunction with most other types of sampling.
Matched sampling and stratified sampling use systematic sampling.
Multi-phase sampling is a form of stage sampling.
Purposive sampling is a variant of boosted sampling.
Reputational sampling and unique-case sampling are variants of extreme-case sampling.
Snowball sampling is a form of volunteer sampling.
Systematic sampling is a modified form of random sampling.
Theoretical sampling is a form of criterion sampling and dimensional sampling.
Time sampling includes instantaneous sampling, interval sampling and duration sampling.
Typical-case sampling is related to case-study sampling.
Typical-case sampling is the opposite of critical-case sampling.

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ARTICLE 37: Research Methods for Ph. D. and Master’s degree Studies: Sampling Part 5 of 6

4th Sep 2020 No Comments

Written by Dr. Hannes 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:

Stratified sampling.
Systematic sampling.
Theoretical sampling.
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.

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ARTICLE 36: Research Methods for Ph. D. and Master’s Degree Studies: Sampling Part 4 of 6

2nd Sep 2020 No Comments

Written by Dr. Hannes Nel

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 the entire population is 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 sampling methods in this article:

Probability sampling.
Purposive sampling.
Quota sampling.
Random sampling.
Reputational-case sampling.
Snowball sampling.
Stage sampling.

Probability sampling

A probability sample is drawn randomly from the wider population.

Therefore, it will be useful if you need to generalize.

Or if you seek representativeness of the wider population.

The name already reveals that it should be used with quantitative research.

Obviously, statistical calculations will form part of the research process.

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.

To develop a purposive sample that will sufficiently represent the population:

You will need to know the profile of your target group.
And you will select items for the sample according to a list of important characteristics.

You can select purposive samples 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 and literature study.

You will need to find certain types of individuals displaying attributes that are needed for your research.

In horticultural research you will probably use categories such as resistance to plant diseases, flower colour, size and shape, etc.

In social research categories such as age, gender, status, role or function in organizations, stated philosophy or ideology, may serve as starting points.

For study in-depth, you will need to select information-rich cases.

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

Quota sampling

For quota sampling you will need to choose representative individuals from a specific subgroup.

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.

The research question and the focus of your study will determine which attributes you should select for your sample.

You should use a nonprobability method to fill the 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.

You can, for example, group the people in your sample as 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, although it will often be best to have the same number of individuals in each 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.

A random sample is mostly used when each element of the population has the same 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 dart board and throwing darts at them to choose sample items, the roll of a 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 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 come 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 people who suffer from arachnophobia, you need to find only one person who is intensely scared of spiders and would be willing to put you in touch with others who suffer from arachnophobia 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.

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ARTICLE 35: Research Methods for Ph. D. Studies: Sampling Part 3 of 6

1st Sep 2020 No Comments

Written by Dr. Hannes Nel

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

Of all the types of sampling, event sampling is the most a research method rather than a data collection method.

You would typically select a small group of individuals to investigate.

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.

You can use your laptop computer to count the words for you.

Any word processor on computer has the facility to count words.

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 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 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 for 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 criterion might be region.

The second stage criterion can be students who excel in numeracy skills.

The third stage criterion 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.

In non-probability sampling you, as the researcher, select the sample based on your own subjective judgement.

Non-probability infers that randomness ins not required, and 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.

The more expertise you have of the field of study, the better will you be able to select your sample.

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 my 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 focus 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.

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.

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