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

The advent of the Patient Protection and Affordable Care Act (PPACA) will lead to drastic changes in the way that research is conducted to better understand healthcare outcomes of comparable treatments. The PPACA created the Patient-Centered Outcomes Research Institute (PCORI) for the purposes of bolstering and supplementing the ability of researchers to conduct Comparative Effectiveness Research (CER).

Comparative Effectiveness Research is defined in the PPACA as "research evaluation and comparing health outcomes and clinical effectiveness, risks, and benefits of two or more medical treatments, services, and items." Treatment, services, and items were defined as "healthcare interventions; protocols for treatment, care management, and delivery; procedures; medical devices; diagnostic tools; pharmaceuticals; integrative health practices; any other strategies or items being used in the treatment, management, and diagnosis, or prevention of, illness or injury in individuals."

The PPACA further set forth that the PCORI should focus on Comparative Effectiveness Research in existing clusters or subgroups of the population that are underserved or unrepresented in the current clinical literature. These clusters or subgroups tend to relate to ethnicity, gender, age, and comorbidities. These are called sensitivity analysis in that the analysis focuses strictly on these subgroups within a population.

The research designs associated with Comparative Effectiveness Research under the guise of the PPACA include randomized trials and observational designs such as prospective cohort and retrospective cohort. Randomized trials are more feasible than randomized controlled trials because the randomization occurs at the intervention level rather than the patient level. PCORI further stipulated that observational designs should meet certain benchmark criteria before valid generalizations can be made: Valid research questions, explicitly defined inclusion and exclusion criteria for participation, comparable interventions or treatments, sound secondary data sources, and transparency of treatment and analysis protocols.

Federal legislation has approved the use of observational research designs like the retrospective cohort design where data and outcomes already exist, making research much more feasible and user-friendly to conduct. 

The methodology or research design used in a study is employed to answer the research question. Without a research question, there is no reason to have a methodological approach. Observational research designs like cases series, case-control, cross-sectional, retrospective cohort, and prospective cohort are research questions related to associations between variables.  Experimental research designs are used to answer research questions related to causal effects.

When choosing a research methodology, one word should always come to mind, feasible.  The feasibility of what you can and cannot do given time, money, resources, and collaborators must be taken into consideration before conducting a study.  Researchers that have limited amounts of the aforementioned may be better served by retrospective observational designs where data on predictors and outcomes already exists.  Prospective and experimental designs require much more time and effort to conduct.  A significantly larger amount of empirical complexity and experience is needed to conduct these types of designs.  There must also have to be sufficient time to follow-up on the outcomes of interest.

The PICO (population, intervention, comparator, and outcome) mnemonic is an excellent tool for defining important parts of a research methodology.  The population should be defined in regards to inclusion and exclusion criteria.  In order for studies and experiments to be replicated, the intervention or treatment must be explicitly described.  If the goal of a research study is to show evidence of a treatment effect, then a comparison, control, or comparator group is necessary. Comparator participants should possess similar demographic and clinical characteristics to treatment participants to truly understand any associations or effects.  Finally, the primary outcome should be measured at the current "gold standard" level to increase the precision and accuracy of research findings.  The "gold standard" outcome is also more generalizable and understood by clinicians because it is part of their lexicon and cognitive schema.

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!

Research questions (and answering them) are always the primary focus of anything and everything empirical, methodological, epidemiological, and statistical. Without a research question, there is no reason to conduct a study or run statistics.

The following are DIRECTLY derived from research questions:

1. Null and alternative hypotheses (hypothesis testing and inferential statistics)
2. Research design (observation or experimental)
3. Population of interest (inclusion and exclusion criteria) 
4. Sampling method (non-probability or probability)
5. Intervention or independent variable (categorical, ordinal, or continuous)
6. Confounding or control variables (secondary, tertiary, and ancillary research questions)
7. Comparator or control treatment (categorical, ordinal, or continuous)
8. Outcome or dependent variable (categorical, ordinal, or continuous)
9. Outcome and design for an a priori power analysis to calculate sample size
10. Structure of the database (between-subjects, within-subjects, or multivariate) and code book
11. Statistical tests used (descriptive, between-subjects, within-subjects, correlations, survival, or multivariate)

Researchers must take the appropriate amount of time to fully formulate and refine research questions. SO MUCH is dependent upon it for their study. Luckily, this task is made easier with the use of two prevalent mnemonics: FINER (feasible, interesting, novel, ethical, relevant) and PICO (population, intervention, comparator, outcome).

FINER is a more of a philosophy for writing research questions. The arguments for the "F," "I," "N," "E," and "R" are all and informed upon by the empirical literature in the area of empirical or clinical interest. Researchers especially have to be well vested in the most current literature in order to make sound arguments for interesting, novel, and relevant questions.

PICO is employed to explicitly and operationally define the population of interest, the intervention, the comparator, and the outcome in a research question. It is also more readily applicable in busy clinical and empirical environments and when writing literature search queries.  

These two mnemonics compliment each other very well in applied empirical and clinical environments. The post-positivist philosophy of social and medical sciences lends itself well to FINER. Measurement of observable constructs and the application of experimental designs through the PICO mnemonic is also strongly reflective of a post-positivist philosophical orientation. Together, the "why" and "what" questions associated with conducting research can be argued in an evidence-based, objective, and logically sound fashion.

I have conducted thousands of statistical consultations over the years and have worked with many novice resident researchers over that time. One cannot help but admire the spirit, energy, and motivation of young people wanting to make an impact on medicine through research. I enjoy the zeal and drive of bright people wanting to be physicians and researchers. This is a good thing!

That being said, I spend a lot of my time with novice researchers using deductive reasoning to hone down their research questions into something tangible and feasible. They come into the office with an idea that will change medicine forever and we will be cruising around the Caribbean in a year! This has never been researched before!  No one has ever done this before! Trust me, I want all of these proclamations to be true and I also want to change the face of medicine. Yet, most times it just not feasible to do so given the time, resources, participants, competencies and environment associated with the study.

I focus on a few primary areas when it comes to feasible research questions with my consultees:

1. Participant pool - Are there enough participants available in the immediate clinical or empirical environment to achieve adequate statistical power for inferential analyses? How will you recruit the participants? What are your inclusion and exclusion criteria? Inclusion and exclusion criteria may need to be modified to increase sample size.

2. Effect size - Small effect sizes require large sample sizes.    

3. Research design - Retrospective designs are always more feasible because the data already exists.

4. Communication - Research never occurs in isolation. Researchers should communicate and collaborate with their peers regarding their research projects. Attendings and academic physicians can give you ideas on how to feasibly conduct your research.

5. Time - What is the time frame for the study from inception to publication? How much time do you have to set aside for the research study? Does the completion of your research coincide with abstract deadlines of interest?

6. Power analysis - Conduct an a priori power anlaysis based on an evidence-based measure of effect to see if the study is feasible in regards to sample size needed to achieve power.

In my experience, statistics is a cognitive dissonance-inducing mathematical science and no one tends to recall their personal and professional statistical experiences with much zeal. It's as if there is an automatic recoil when the topic of statistics enters the discussion and planning of a research study. The literature has posited that statistics are intimidating and nebulous because many people do not possess the necessary competencies and experience with statistics and also people do not understand the lexicon of the science.

The most important thing to remember about applied statistics, despite their prevalence, relevance, and utility in everyday life, is that they are tools that human beings use to communicate the results of data analysis. Hypothesis testing is employed in empirical research so that researchers can present their findings in a relative context that is interpretable and applicable in other research and applied environments.

Statistics are useful ONLY when they are used to answer useful, appropriate, answerable, relevant, and valid research questions that are grounded in the empirical literature.

As a biostatistical consultant at an academic regional medical campus, I am supposed to spend 80% of my time working with residents, fellows, faculty, clinicians, researchers, nurses, pharmacists, and hospital staff to formulate and refine their research question. THAT is how important it is to any research study. 

A research question is cultivated through researchers' efforts to know the existing literature, their clinical expertise and interests, their collaboration with peers, and their intrinsic motivation towards scientific discovery and innovation. Answerable, appropriate, meaningful, and purposeful research questions make valid and needed contributions to the literature.

Deductive reasoning should be used when formulating a research question. Oftentimes, researchers will want to answer EVERY possible question and collect data on EVERY single variable that they can in hopes of finding SOMETHING SIGNIFICANT. This is not the way that REAL science works. A focused and refined research question is the basis for constructing and executing research. This does not mean that researchers cannot ask secondary, tertiary, and ancillary research questions as demographic, clinical, and confounding variables are yielded from literature reviews! Of course, these are important questions to ask and often lead to great discoveries! (Example:  Viagra) However, having ONE research question that serves as the foundation for a study is extremely important and should not be overlooked!

Many novice researchers will plan an entire study around a type of research design or a statistic that they read in an article. REMEMBER, research designs and statistical tests are chosen to answer researcher questions, NOT the inverse.

All of this being said, there are two existing frameworks that greatly assist in formulating (FINER) and refining (PICO) research questions. FINER stands for feasible, interesting, novel, interesting, and relevant. PICO stands for population, intervention, comparator, and outcome.