## FAQs

**Who Utilizes the Services of a Dissertation Consultant?**

Students from many disciplines will often use a dissertation consultant. Unless you are a student majoring in statistics, trying to figure out how to apply all those concepts from past stats and research classes can be overwhelming. Determining sample size, conducting power analyses, and choosing the right tests are all very important steps. It doesn’t start there, though; it begins with a great research design.

## Will you help me with my Thesis, too?

Absolutely! Often, a thesis is required as a prerequisite to your dissertation. We are happy to assist you in this process as well.

**Who Chooses to be a Dissertation Consultant?**

Consultants usually have a master’s or PhD in statistics or in the social and behavioral sciences, since these tend to have the research methodology training and experience needed. Those who have a track record of successfully teaching these subjects and mentoring others in the process tend to be the most effective when it comes to dissertation consulting. A dissertation consultant can help you with the following:

**Choosing a topic and having it approved by your committee**

Is it interesting?

Is it researchable?

Is it feasible?

**Developing a research question**

All research begins with a question. What exactly is it you wish to know about your topic and why? This is different from formulating a hypothesis; that will come later. Be sure to be very specific. For example, you cannot be too broad or general when you ask a question. Once you know what you want to know, start your initial search of the literature and see what you find.

**Nailing down your hypothesis**

Once you have seen what the literature says on your topic, you can intelligently develop your hypothesis. A hypothesis is not a question, but a statement you make concerning the outcome of your experiment or study.

There are important differences between a research question and a hypothesis, however, and until you have these clearly defined you should not begin working on your project in earnest. Hopefully, some explanation will clarify why I say this:

A **research question** is the very question (or questions) you are trying to answer by conducting your study. For example, you might be wondering if stress levels decline as a person ages, or whether there are male/female differences in the way college students experience stress. This is very different from a **research hypothesis**, which is not a question at all, but a bold, clear, concise statement about the theory driving your research. The way you ask questions and state hypotheses is extremely important, as they determine how you will conduct your literature review, your methodology, and how the data will be collected and analyzed.

**Effectively planning out your research (this is where the design comes in)**

Below are a few of the study design types typically used in dissertation research:

• Descriptive or survey research design: attempts to describe and explain conditions of the present by using many subjects and questionnaires to fully describe a phenomenon. This is one of the most popular for dissertation research.

• Experimental design: attempts to explore cause and affect relationships. Because of the requirement of random assignment, and tight control, this design can be difficult to execute in the non-laboratory setting, however.

• Quasi Experimental Research Design: approximates the experimental design but does not have a control group.

• Correlational study: attempts to explore relationships between two or more variables

## What is a power analysis?

Power is the probability of correctly rejecting the null hypothesis when it is, in fact, false. In other words, we are trying to avoid making a Type II error. We conduct an a priori power analysis in order to determine how many subjects or participants we will need in our sample so that we can be reasonably confident that, when we say we find a statistically significant difference, that difference is real. Ideally, an a priori power analysis should be conducted for each hypothesis.

**Choosing the correct tests when analyzing your results**

What I always emphasize to my students is this: the data drives the analysis! In other words, the type of data collected determines the test(s) that must be used in the analyses. It is important, therefore, to choose your instruments carefully, to understand the type of data you have available to you, and which tests are appropriate when it comes to your research hypothesis.

**Reporting your results**

Depending upon your discipline, you must report (write up) your findings in a certain way and use a discipline specific style of citing resources. In the social sciences, for example, APA is the required style, while in the health science, AMA is used.

The type of study you are conducting:

What type of study design will you use in your study? Will you be conducting an experiment or collecting self-report data from surveys?

Who your population of interest is:

Does your study design require a random sample?

What is the cost underestimating it if your sample is not representative?

## What is statistical significance?

The probability value, or p-value, you get from your test is compared to a critical value, or alpha level, that you determine ahead of time. These values range from zero to 1.0, and the lower your resulting p-value the more likely it is that any differences you are finding are not by chance. Since we typically set alpha to .05, a p-value of less than .05 would be considered statistically significant.

## What is effect size?

Even if your test results are significant, they may not be meaningful. This is where effect size comes in. Effect size tells us about the magnitude of the difference between groups. It is simply calculated by taking the mean of the two groups you are comparing and dividing it by the pooled standard deviation of one of the groups. Generally, the breakdown (Cohen, 1988) is as follows (though it can vary somewhat):

0.2 = small effect

0.5 = moderate effect

0.8 = large difference effect

**Note:** Some of these topics are discussed in more detail on other pages, while others are still being added.