Tags

  • Published on

    Research and statistics in the collective unconscious

    Cognitive Dissonance Research Research Engineer Statistics

    Research Engineer will always be free

    Fighting the good fight for statistics and research

    It behooves me to make Research Engineer free to the world.  Statistics and research often cause a "knee-jerk" reaction of anxiety and cognitive dissonance.  I am reminded of this on a daily basis, because I live it on a daily basis.  

    It seems to me that statistics and research, in the most basic applied sense, have acquired a negative connotation within the collective unconscious.  That is scary because researchers communicate the findings of their research using statistics! Other researchers and clinicians are supposed to be able to interpret and generalize the research methods and statistical findings of the studies, denoting possession of the necessary competencies and experience to do so.  

    Research Engineer will always be free, because research and statistics belong to everyone.  Names like Aristotle, al-Haytham, Bacon, Descartes, Bayes, Gauss, Pearson, Fisher, Spearman, Bonferroni, Tukey, Cox, and Cronbach have come before me and have invented the empirical and statistical methods presented in this website.  All I did was take their methods and build THE FIRST decision engine designed to avert the aforementioned anxiety and cognitive dissonance. The brilliant men and women that came before me deserve to be recognized and their methods, which we use in everyday life to make important decisions, should be held in a higher esteem within the collective unconscious.

    I tell my clients that when they feel cognitive dissonance associated with research or statistics, I will be standing there in their unconscious mind, ready to help.  It always gets a reaction, but they always come back and they always receive my best efforts.

    Now, when anyone around the world feels that "knee-jerk" reaction to research and statistics and they want to retreat into their unconscious minds, Research Engineer will be waiting, always free and always able to help.  

    Click on the button below to continue.
  • Published on

    Establishing causal effects

    Blinding Causal Effects Experimental Designs Intention-to-treat Random Assignment Random Selection Randomized Controlled Trial Systematic Review

    Necessary conditions for establishing causal effects

    Experiments require random selection and random assignemnt

    Everyone conducting research is looking for associations between predictor, confounding, and outcome variables. People will often talk about wanting to know the correlation, relationship, or effects between variables.  These types of research questions are answered using observational research designs.

    However, when researchers are attempting to explain causal effects, there are certain criteria that have to be met.  

    1. An experimental design is the only method by which a causal effect can be inferred. This means that study participants are selected at random and randomly assigned to treatment groups. Any differences at baseline are thought to occur by chance with random selection and random assignment (whereas with observational designs they are due to selection and observation biases).  

    2. Analyses have to be conducted in an "intention-to-treat" fashion. This means that all study participants are analyzed in the original groups that they were randomly assigned to at the beginning of the study.

    3. Blinding can bolster the validity of causal effects, but is not necessary for establishing causal effects. Blinding can reduce observational biases that can occur in experiments where human beings are studied.    

    4. As much as possible, all pertinent demographic, clinical, and confounding variables related to the association between the primary predictor and primary outcome variable should be accounted for in experiments. True causal effects (in reality and practice) are always multivariate in nature. Clinicians do not make treatment decisions based on one form of evidence, they want as many forms of evidence as possible. The statistics that establish the efficacy of treatments should reflect the multivariate nature of treatment decisions.

    5. There has to be a sufficient amount of time or follow-up for an outcome. True causal effects are found when the temporal aspects (etiological, prognostic, time) of developing an outcome are accounted for methodologically and statistically.

    6. One clinical trial that shows valid evidence of a causal effect and meets all of the aforementioned criteria is STILL NOT ENOUGH! The results of the most methodologically sound randomized controlled trials or true experiments need to be aggregated in systematic reviews, syntheses, and synopses of syntheses. Pooled effects account for more variance in the general population and strengthen the "causal" understanding of causal effects.