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1. Failure to create well-defined, clearly-articulated research questions
The first step when planning a RCR is to formulate a series of research questions that are to be answered based on results of the study. Research questions should be logical, flowing from that which is known or believed to be true to that which is unknown and requires validation . Research questions form the initial structure of the RCR and guide the study design and data analysis. It is important to spend adequate time carefully scripting and revising the research questions for the study.
There is no shortage of published advice on developing and refining research questions . We have selected one framework for the design and articulation of research questions to present here which we have found to be particularly useful. Though not mentioned elsewhere in this manuscript, we recommend Morgan and Harmon  to the reader as an additional reference in properly framing research questions. The framework presented here is a typology of research questions. Such questions generally fall into one of three categories: questions of description, questions of relationship, or questions of comparison.
Questions of description are common to RCRs. These questions describe what is going on or what exists . Incidence and prevalence research are descriptive. For example, we might formulate the research question, “What is the incidence rate for seasonal influenza among the elderly population in Belgium for the year 2009?” The answer to this question might be expressed in terms of a percentage. Other examples might include questions comparing characteristics and outcomes of patients with community-acquired pneumonia who were admitted to the Intensive Care Unit (ICU) with those patients managed on the ward  or characterizing hip joint pain referral patterns . Results to descriptive questions are often reported as proportions, percentages, frequency counts, measures of central tendency (mean, median, mode), measures of variability (standard deviation, range), or various charts, graphs, and tables.
Questions of relationship ask how phenomena are related to one another . As an example, we might pose the question, “What is the relationship between occupational burnout and suicide ideation among medical residents in the Northeast United States?” To answer this question, we would likely gather burnout and suicide ideation scores from the population of interest and then calculate a correlation coefficient to quantify this relationship. Other examples of this type might include examining the relationship between levels of community-reported infectious diseases and rate of neural tube defects  or assessing the relationship between the use of antipseudomonal drugs and the development of a resistance to Pseudomonas aeruginosa . The answers to these types of questions are often provided in the form of a correlation coefficient. There are many such coefficients, and the proper choice of the coefficient is dictated by the nature of the data, including data level (nominal, ordinal, interval, or ratio) and the underlying distribution.
Questions of comparison ask about group or sub-group differences on a variable (or variables) of interest. The groups discussed in the above definition represent levels of the independent variable, whereas the variable examined across groups is known as the dependent variable. Questions of comparison are often used in randomized clinical trials. In a simple example, a group of patients with a particular disorder are randomly assigned to either a treatment or to a control group. The treatment group receives the intervention while the control group does not. At the end of the trial, the two groups are compared to assess the efficacy of the treatment. While questions of comparison may seek to establish cause-effect relationships, such is not always the case. We might pose the research question, “Are there differences between males and females on life satisfaction following a spinal cord injury?” In this example, the independent variable cannot be randomly assigned since gender is a pre-determined characteristic. This question still lends itself to comparison however. Other examples might include comparing the effect of fluid resuscitation with albumin or saline on mortality among ICU patients  or comparing four weight loss diets from low to high carbohydrate intake for effects on weight loss . These types of questions are often answered by statistically comparing measures of central tendency across groups.