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    Research Engineer makes applied research and statistics easier

    Chi-square Database Management Diagnostic Testing Effect Size Epidemiology Evidence-based Medicine Kruskal-Wallis Kurtosis Mann-Whitney U Non-parametric Statistics Normality Odds Ratio With 95% CI Psychometrics Relative Risk Research Research Design Research Question Sample Size Skewness Statistical Assumptions Statistical Power Analysis Statistics Survey Variables Wilcoxon

    Research Engineer is designed to get you to the correct research question, research design, sample size, database, and statistical test

    Based on your decisions to the questions presented, you will get to right place

    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
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    Research Engineer is the world's first online decision tree for applied research and statistics

    Database Management Diagnostic Testing Education Epidemiology Evidence-based Medicine Psychometric Tests Research Design Research Engineer Research Question Statistical-power-test Statistics Survey Variables

    Fully automated and freely accessible to researchers around the world

    The first interactive decision tree that integrates statistical assumptions and post hoc analyses

    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!
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    Dichotomous variables in SPSS

    Categorical Variables

    Analyze dichotomous variables in SPSS

    Choose reference categories or dummy code variables

    Here is a really quick tip for making the statistics and outputs of SPSS much easier to interpret when using dichotomous predictor and outcome variables. Whatever "level" of the dichotomy that you are most interested in should be codified as a "1." If a participant has the characteristic or outcome of interest, codify those observations as "1" and the absence of the characteristic or outcome of interest as "0."  

    SPSS has a default that always makes the highest numerical category be the reference group. However, most times, researchers want to know the odds of something occurring versus not occurring, NOT the odds of something not occurring versus the odds of it occurring. Therefore, it is important when running bivariate associations between dichotomous categorical variables to always use the codification scheme above so that the statistical outputs can be interpreted properly.

    When conducting multivariate analyses, SPSS still uses the same reference default for the highest number category. The "point and click" interface for multivariate statistics in SPSS gives you the option to click on a "Categorical" button. Always do this and make sure that you set the category to "first" when running these types of statistics.  
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    Measurement at continuous levels

    Categorical Continuous Ordinal Parametric Statistics Variables

    Measure variables at the highest level possible

    Don't discount your continuous variables!

    There is a tendency for researchers to take continuous variables and recode them into ordinal or categorical variables. For example, researchers may ask participants to answer if they are 20-30 years old, 31-40 years old, 41-50 years old, 51-60 years old, or 60+ years old. Or, they may set an arbitrary "cut-off" of values above or below a certain value (People who are 55 years and older versus everyone younger than 55 years).

    Researchers lose valuable precision and accuracy in measurement when continuous variables are demoted to ordinal or categorical levels. It is ALWAYS better to take an actual numerical value with a "true zero" and analyze it using parametric statistics. If there is a theoretical, conceptual, or empirical basis for pairing down continuous measures into lower levels of measurement, then and only then should it be done. If you were a researcher and wanted to know the most precise and accurate measure possible of my age, which of the following is the best way to ask?

    1. How many years old are you?  (continuous)

    2. How old are you? (circle one)  20-30    31-40    41-50    51-60   60+  (ordinal)

    3. Are you above or below the age of 55?  (categorical)

    The continuous method will give you a stronger measure of age, which can then be broken down into separate ordinal or categorical levels, AT YOUR DISCRETION. So, always measure at the continuous level if at all possible.

    With this being said, PLEASE realize that while we can go from continuous to ordinal and continuous levels of measurement, it is IMPOSSIBLE to change categorical and ordinal variable into a continuous level of measurement.

    Let's use a basic example:

    Gender - 0 = male and 1 = female

    Is there any way to convert this into a continuous variable? No.

    Here is another example:

    How old are you? (circle one)  20-30    31-40    41-50    51-60   60+

    Can you convert this into a continuous variable? No, again.

    In conclusion, ALWAYS try to measure your variables at a continuous level, if at all possible or feasible. They can be broken down into ordinal and categorical variables as needed. Also, REALIZE that once you have decided to measure something at a categorical or ordinal level, it cannot be converted to continuous.