A few words on what I'm doing on here. I am a biostatistician, methodologist, psychometrician, and counselor. Everyday, the incredibly intelligent people I work with including physicians, residents, fellows, staff, and faculty feel anxiety when it comes to statistics and research. Research has shown that statistics can induce cognitive dissonance in an individual due to limited experiences and competencies. The collective unconscious has sequestered statistics and research into a dark corner and that's scary.

Research and statistics are the methods by which we, as scientists, analyze, synthesize, and evaluate our research findings in a manner that can be generalized to the appropriate audience. If our methods for communicating research findings causes cognitive dissonance, just because it relates to research and statistics, then how can one ever really be able to generalize the clinical literature and integrate it into clinical practice?

After seven years of being the one to induce cognitive dissonance in others related to research and statistics, I decided to make a useful tool for students and researchers that could alleviate some of the feelings of anxiety associated with research and statistics. I built Research Engineer.

Research Engineer is designed to get you to the correct research question, research design, sample size, database, statistical test, evidence-based medicine intervention, diagnostic calculation, epidemiological calculation, variables, surveys, psychometrics, and educational framework to answer your current question (and future questions). 

I am trying to bring research and statistics out of the collective unconscious and into the conscious mind where it can be effectively communicated among researchers, scientists, and students by creating this decision engine. It is easy to get to the correct research or statistical component, just answer the questions that I present you in the webpages and click on the buttons with your answer in them. Also, the step-by-step methods for conducting and interpreting each statistical test in SPSS are presented on their respective webpages. 

You can also contact me via phone, social media, and email at any time in you have questions. If you need some help conducting statistics for a research project, I have eight years of experience across thousands of individual projects and I would love to help you on your study.  We can negotiate prices if you are an undergraduate or graduate researcher. 

In conclusion, Research Engineer makes choosing research methods and statistical tests MUCH EASIER. Just answer the questions embedded in the various decision engines and get to the correct method or test, EVERY TIME.

Thanks for your continued support, dear friends and colleagues. And many thanks and salutations to the individuals that use Research Engineer. I am honored and humbled to have this great opportunity to create a very useful and unique website. You all are the ones that make it shine!

​Sincerely,

R. Eric Heidel, Ph.D.
Assistant Professor of Biostatistics
​Affiliate Professor of Biomedical Engineering
Department of Surgery
Office of Medical Education, Research, and Development
University of Tennessee Graduate School of Medicine
Owner and Operator, Scale, LLC

Research Engineer is going to be presented for the first time in a public forum next Tuesday. I'm pretty excited to let all of my colleagues know what I've been up to these past five months. I realized earlier today that Research Engineer has completely changed my life for the better. And I'm so thankful to all of those that have supported me along the way.

And to visitors of this website, I extend my most gracious and humble thanks for your patronage. The website will continue to grow and help you in all of your future empirical endeavors.

I have built the world's first online decision engine for research questions, research designs, statistics, statistical power, databases, evidence-based medicine, survey design, psychometrics, epidemiology, diagnostic testing, variables, and education. I look forward to the future!

When it comes to writing survey items that use Likert scales as response sets, use 5-point Likert scales with increasing order. The 5-point scale is preferable to a 4-point, 3-point, or dichotomous scales because there is more chance for variance with a 5-point scale and there is a "neutral" rating.

Variance in the responses is needed in order to properly assess the diversity that may exist in a population. Increased variance is also important for the underlying mathematics associated with reliability analysis, exploratory factor analysis, validity analysis, and confirmatory factor analysis.

The use of 5-point Likert scales also works well in an aesthetic fashion for structuring a survey. Space and time can be saved in survey administration when items from similar content areas use the same 5-point Likert response set. These questions can be formatted into a matrix.

Finally, increasing order should be used when using a Likert scale, going from left to right.  

For example:

Strongly Disagree, Disagree, Neither Agree Nor Disagree, Agree, Strongly Agree
Never, Rarely, Sometimes, Often, Always
Very Poor, Poor, Moderate, Good, Very Good

Coming from a social science background, I understand that social scientists can spend the vast majority of their time just trying to measure for the construct or behavior they are interested in. I spent a year of my life constructing a survey instrument to measure for the construct of isomorphism in clinical supervision. It is an exciting and yet daunting task to create something from nothing, and I commend social scientists that try to capture variance in human beings.

Surveys can be used to answer "unique" research questions. And by unique, things like isomorphism that exist at a very abstract or unconscious level are perceived in any number of ways to any number of people. Also remember, these types of "unique" constructs often exact a reaction of "cognitive dissonance" in your peers because they are "unknown," "different," or "weird."

All of that being said, the VERY FIRST thing you should do when conducting a survey research study is create a construct specification related to the construct you are measuring for in the proposed survey.

Remember, the survey should be written to represent just ONE construct. It is important to give an operational definition to the ONE construct. Define it in objective and measurable terms if at all possible, and use that definition as the basis for building subsequent components, content areas, and "factors." The construct specification serves as a springboard for showing how your construct exists or is theorized to exist in the context of the empirical literature. You are essentially making an argument, based on the literature in the area, that the construct can be, should be, or has not been properly assessed.

Creating a construct specification also constitutes seeking out existing survey instruments that measure something theoretically, conceptually, or empirically linked to your construct of interest. Find the "gold standard" survey instruments with the most validity evidence and seek out permission for their use in your study (if needed).  

Explicitly describe the population of interest associated with your survey. What are the inclusion and exclusion criteria for being a potential participant in your survey study? How will you go about recruiting participants? Will you use incentives?  How will you administer the survey?  Will you be able to meet sample size requirements of 150-300 for a pilot study and 300-1,000 for a validation study?

The next section of a construct specification operationalizes the content areas of your construct. Each content area should have an operational definition. Then, each component (or item) that makes up the content area should be defined and described in regards to its relevance to the construct. Lastly, give a citation from the empirical literature area to back up the argument for relevance. Do this for each component (or item) for each content area of the constuct. This can be a tedious process for more "abstract" constructs, but it is essential to provide an empirical framework/argument so that your audience can the proper frame of reference for perceiving the construct.

The last section of the construct specification is the "Table of specifications" where you given numerical designations of the percentage of the survey allocated to each content area of the construct. The number of items and content areas and their coverage within the survey must be equivalent to the make-up of the second section of the construct specification. If your construct is theorized to be composed of three content areas and one of the content areas represents 60% of the literature, then that content area should represent 60% of the items in your survey.

Going through this process is an excellent opportunity to become vested in the empirical literature and become an expert in the field. It is a time-consuming process to build a strong construct specification, but it provides a much higher quality end product.  

I designed and tested a survey instrument for purposes of my dissertation. I published the survey to an online survey administration site.

Then, I went to every website for every Counselor Education graduate program in the United States and Canada and got as many emails of students and faculty that I could find. All in all, I spent about two months of my life putting together a list of over 3.200 emails.  

After seeking out the help of the IT department, I learned how to send out mass emails to potential participants WITHOUT the emails arriving as junk or spam. Here are the steps:

1. Type every email address in ONE column of an Excel database.

2. Open up a "New Message" email and put your own email address into the To: box.

3. Click on the Cc: button to open up the menu.

4. Highlight the column of email addresses, right click your mouse, click Copy.

5. Paste the emails into the Bcc: box in the email heading.

6. Type your email out (with informed consent) and embed the link to the online version of your survey into the email.

7.  lick Send.

It's that easy!

The term operationalization is very near and dear to my heart since I conducted my dissertation on operationalizing and validating the construct of isomorphism in supervision. Operationalization essentially means defining observable and measurable components of a given construct or behavior.

The term is used often in the social sciences because scientists in that field have to spend so much time creating and validating their constructs of interest, just to be able to measure for them. From an empirical standpoint, they have to operationalize the construct as it exists within the perception, context, experience, and environment of members of a population.

Many social scientists use survey methodologies (cross-sectional) to operationalize an abstract, new, or unique construct or behavior. They master the content area related to the construct, create a survey, and then administer it to a sample from a targeted population to see what content areas or items account for the most variance. Principal components analysis and confirmatory factor analysis are used to establish the construct validity of survey instruments.

Medical professionals use cross-sectional research designs to establish the prevalence of disease states. Operationalization within physiology deals more with using "gold standard" techniques and concrete measures such as lab values.  Treatment protocols are another form of operationalization within medicine.  Certain procedures like a central line insertion require 20+ sequential steps to be conducted by surgical team members, every time.  With the advent of the Affordable Care Act and upcoming clinical pathways, operationalization will play an even larger role in building economical, efficient, and effective standards of care.