In the following we will work on crafting a successful research question. At this point, don't be committed to a methodology, and beware that you are not writing a question that unconsciously leans to a particular methodology.
The process you follow is critical, not the methodology, at least not yet.
In the following, you will:
Let's get started.
Learning to write good research questions is a skill that takes practice. Developing a research question is a developmental process. As you read the literature and gain a greater understanding about your research problem, you will rework your research question until you are able to focus more specifically on what you want to explore and learn about during the formal research process. Keep in mind that a question typically goes through several iterations. So don't worry if your first attempts may not be the final product. This is normal.
Let's get oriented first. The research question is what the researcher seeks to answer by collecting data. It is the foundation of the entire study, because the question embodies the method by which the research problem—called for by the existing literature—will be solved. It's simple: You are going to solve your research problem by collecting information, and you collect that information by asking about a specific question or set of questions.
Simply by reading a well-formed research question, you can usually tell:
As we proceed you will need to have knowledge of a variety of research terms. If you don't recognize and understand these terms, there are handouts in the Dissertation Research Seminar Courseroom that you can refer to. We strongly encourage you to take notes as you work your way through this document, and make sure that you understand each section.
Let's work on quantitative questions first.
Quantitative research questions address the:
They also clearly identify the sample that will be questioned. Most importantly, they use the same language for these elements as the research problem used. Your research question needs to cover all three items.
Quantitative questions that are appropriate for a dissertation are worded to seek verification of a hypothesis, that is, a prediction. These predictions are based on the existing literature, and should be entirely consistent with the research problem (which comes from the literature).
Quantitative questions are written in two formats: conceptual and operational.
An example of Conceptual vs. Operational Questions
Let's take a subject like the relationship between depression and alcoholism as our example. First, here is a simple conceptual version:
What is the relationship between depression and alcoholism?
Okay. Now here is an example of an operational version of the same question:
Is there a statistically significant correlation between levels of depression and degree of alcoholism?
Notice three changes: between the conceptual and the operational versions of the question:
Because quantitative questions seek verification, a critical piece of the analysis will be to discover whether or not the results (e.g., the correlation between two variables) are due to chance or whether they can be considered real. Therefore, operational quantitative questions will always contain some way of asking about "statistical significance." They will not ask, "Is there a correlation between A and B?" They will ask, "Is there a statistically significant correlation …?"
Quantitative studies usually pose more than one question. Indeed, all quantitative questions will have a set of subquestions. But some studies require additional main questions (and their subquestions).
Here's a conceptual question as an example:
Which psychological and organizational factors associated with employee burnout are most predictive of reduced productivity?
If it was found that a gap exists in the current literature as to what psychological and organizational factors are associated with employee burnout, then in that question there are really two questions:
You'll note that the first question asks for a correlation, and the second one asks for a prediction. You can consider these to be two main questions, then.
Now, each quantitative main question requires subquestions. In qualitative methodology, you only have the main question and there is almost never any reason for more than one main question. But we're talking about quantitative here, so let's look at examples of subquestions. Take this conceptual main question:
Is being managed by an authoritarian manager a better predictor of employee burnout than being managed by a transformational manager?
Now we can transform that into an operational main question:
Is there a statistically significant difference in levels of burnout in employees managed by authoritarian managers compared with employees managed by transformational managers?
Before we can answer the main question, we need information on all the variables. First, we need to measure management style so we can create groups based on that concept:
Answering those two questions will allow us to group employees in either the authoritarian or the transformational group.
Next, we need to measure the levels of burnout in our two groups of employees:
Once we get the answers to those subquestions, we can proceed to the statistical analysis that will answer the main question about which management style better predicts employee burnout.
You will notice that in our examples, each subquestion describes a variable. Subquestions are almost always descriptive questions (and nearly all qualitative questions are descriptive). Very complex main questions asking for quite complicated statistical tests might require correlational subquestions to support the main analysis, but in general, subquestions typically are descriptive. Main questions, on the other hand, must, for a Capella dissertation, be at least correlational or predictive.
Let's see what these types of questions look like.
DESCRIPTIVE Quantitative Questions
Descriptive questions ask what a single measure is. For example:
What are the reading scores of 3rd graders receiving special education assistance in rural Minnesota schools?
Here, there is only one variable being described: reading scores. The most common subquestions are descriptive, obtaining the measures of each of the main question's variables.
Notice too, that a descriptive question can be framed conceptually or operationally:
CORRELATIONAL Quantitative Questions
The word "quantitative" in the title of this section is actually redundant, because qualitative questions never ask for relationships between variables, or correlations. Correlational questions ask for a calculation of a relationship between two or more variables and its statistical significance. For example:
Is there a statistically significant correlation between time spent in the school bus each day and the reading scores of rural special education 3rd graders?
Usually, a dissertation will not be this simple. Successful dissertations ask somewhat more complicated questions, either asking about multiple variables or asking about a predictive or causal relationship. However, in a complex causal question, there may need to be some correlational subquestions in order to compare groups, for example. And of course, for each correlation, there will need to be two or more descriptive subquestions.
Here too, the correlational question can be framed either conceptually or operationally:
DIFFERENCE (or PREDICTIVE) Questions
Difference questions form a set of questions that look for causal relationships between two or more conditions. Depending on the type of relationship being examined, different words are used. The general conceptual framework is:
Does A cause B?
The word "cause" has different meanings, and capturing those meanings will express a more precise question. For example,
What is the influence of A on B?
Other words indicate specific kinds of causal relationships, such as:
When the question asks for "effects," it is asking for a cause-effect relationship between or among variables.
The conceptual version of the causal or predictive questions is straightforward:
What is the effect of A on B?
Or you might ask, what is the influence of A on B? To what extent is B affected by A?
Does A predict B?
The operational version of the question needs to be carefully framed to detect precisely the kind of causal relationship you're interested in.
Is there a statistically significant difference between mean reading scores of rural special education 3rd graders who spend more than 60 minutes a day on the school bus as compared with those who spend fewer than 30 minutes a day on the school bus?
This operational question would be asked when a cause-effect relationship is suspected. If a statistically significant difference is found and if that difference is reasonably strong, the conclusion might be that time on the school bus affects, influences, impacts, predicts, perhaps even causes differences in the reading scores.
Very simply, quantitative questions measure variables. We have found that a large number, perhaps a majority, of doctoral learners do not really understand variables, and this lack of understanding causes them significant time loss when writing their proposals. Remember, the research question is the "driver" of your methodology and design, and if you do not understand your variables, your question will be off-track and the design may be wrong.
If you have any confusion about any of these terms please take a minute and study the handout on variables available in the courseroom or refer to the discussions of variables in your statistics and research methods texts.
We have one more element to consider regarding quantitative research questions, namely, hypotheses.
Again, you may be familiar with hypotheses, but if you are not, please review your research methods and statistical textbooks to refresh your understanding. Discussing why hypotheses are necessary in quantitative research is beyond the scope of this particular document.
First, here are the characteristics of hypotheses.
There will be a statistically significant correlation between time spent in the school bus each day and the reading scores of rural special education 3rd graders.
Examples of the Hypotheses
The Null hypothes is states the research question in the negative. Using our 3rd grade reading example, the null hypothesis would be:
H0: There will be no statistically significant correlation between time spent in the school bus each day and the reading scores of rural special education 3rd graders.
The Alternate hypothesis states the research question in the positive. For example,
H1: There will be a statistically significant correlation between time spent in the school bus each day and the reading scores of rural special education 3rd graders.
Note how the two symbols for the null and the alternate are constructed. H0 always indicates the null, and H1 always indicates the alternate. H of course stands for hypothesis.
Having considered how to construct quantitative research questions, let's turn our attention to qualitative research questions.
Qualitative research seeks discovery. Qualitative research questions often are chosen because the research problem indicates that little is known about the topic. Perhaps a great deal of statistical knowledge exists, but no one yet has inquired into how the people involved experience the topic. There are cases in which the topic is meaningful but no one yet has begun to investigate it. In both instances, the researcher decides to go into the field and discover how people describe their experience of the phenomenon.
In other words, qualitative research questions are always descriptive in some way or another and are written so that they obtain descriptive verbal information from participants.
Just like quantitative questions, the way the qualitative question is written suggests the specific qualitative design. For example:
All qualitative questions inquire into descriptions, observations, and interpretations. They do not inquire into relationships between variables or seek causal explanations.
Qualitative research questions seek to discover the:
In only one qualitative design, namely Grounded theory, the researcher seeks to explain a process by relying on verbal descriptions of that process by participants who have undergone it. This is the one kind of qualitative design that goes beyond simple description.
Qualitative questions also identify the sample that will be questioned. Most importantly, they use the same language for these elements as were used in the research problem.
Qualitative questions do not:
As a result, qualitative questions also do not:
On a side note, many qualitative studies use interviews to collect their data, and of course, interviews will require that questions be asked. But these data collection questions are not our topic here. Here, we're concerned with how to craft the main research question only. So let's talk about that next.
Here are some examples of good qualitative questions, with the probable research design that would spring from them.
If one wished to learn how monks describe the everyday experience of being lonely, a good research question might be:
How do religious monks describe the lived experience of monastic loneliness?
This would lead to a phenomenological study.
Suppose the research problem is that it is not known how persons suffering from early-onset dementia come to terms with having the disorder. A research question for this might be:
How do persons diagnosed with early-onset Alzheimer's disease describe their processes of coming to terms with the disease?
Because this asks about a process, that is, a movement from one condition, being diagnosed, to another condition, coming to terms with the diagnosis, and because it asks for evidence from those who have experienced the process, it would trigger a Grounded theory study.
What if the research problem is that although much is known statistically about best treatments for a given psychological problem, too little is known about what the experience of receiving that treatment is like. For this, a good research question might be:
What can be learned from patients with dissociative identity disorder, their caregivers, and their families about the various aspects of the experience of inpatient treatment at a specialized large urban treatment facility?
Because this question asks for verbal information from a number of sources (patients, caregivers, and families), clearly identifies a "bounded system" (a specific facility for treating dissociative identity disorder), and asks for "lessons learned," it clearly specifies qualitative case study for its design.
One might be interested in what factors shape and inform the careers of successful business leaders. A good qualitative research question for this topic could be:
How do successful business leaders describe the forces, experiences, and influences that shaped and informed their careers?
Because the subject matter is about external factors—forces, experiences, and influences—and because the question does not flow naturally to the other designs, this would be an excellent generic qualitative inquiry question.
Here is a list of words that are commonly used in qualitative questions:
All these share the common characteristics: that they inquire about subjective experience and require verbal answers.
Now you will have the opportunity to practice the working with the information from this document by actually craft your own research questions, one qualitative, one quantitative.
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