Within-subjects designs increase statistical power. because participants serve as their own control. Between-subjects designs necessitate more observations of the outcome to be able to effectively compare independent groups on an outcome. Multivariate analyses further decrease statistical power in that many more observations of the outcome to detect significant effects. At least 20 -40 more observations of the outcome have to collected per variable entered into a simultaneous of hierarchial regression model in order to meet statistical power when trying to account for demographic, etiological, clinical, and confounding effects.

Within-subjects designs, when coupled with with continuous outcomes, large effect sizes, limited variance in the outcome and a large sample size, greatly increase statistical power. Small effect sizes are also easier to detect using within-subjects statistics because participants serve as their own control. Within-subjects design also provide more statistical power when small sample sizes are used.    

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.

In medicine, there is an important metric that signifies efficiency and quality in healthcare, length of stay (LOS) in the hospital. When thinking about the distribution of a variable such as LOS, you have to put it into a relative context. The vast majority of people will have an LOS of between 0-3 days given the type of treatment or injury that brought them to hospital. VERY FEW individuals will stay at the hospital one month, six months, or a year. Therefore, the distribution looks nothing like the normal curve and is extremely positively skewed.  

As a researcher, you may want to predict for a continuous variable that has a natural and logical skewness to its distribution in the population. Yet, the assumption of normality is a central tenet of running statistical analyses. What is one to do in this situation?

The answer is to first, run skewnessand kurtosis statistics to assess the normality of your continuous outcome.  If the either statistic is above an absolute value of 2.0, then the distribution is non-normal. Check for outliers in the distribution that are more than 3.29 standard deviations away from the mean. Make sure that the outlying observations were entered correctly.

You now have a choice:

1. You can delete the outlying observations in a listwise fashion. This should be done only if the number of outlying variables is less than 10% of the overall distribution. This is the least preferable choice.

2. You can conduct a logarithmic transformation on the outcome variable. Doing this will normalize the distribution so that you can run the analysis using parametric statistics. The unstandardized beta coefficients, standard errors, and standardized beta coefficients are not interpretable, but the significance of the associations between the predictor variables and the transformed outcome can yield some inferential evidence.

3. You can recode the continuous outcome variable into a lower level scale of measurement such as ordinal or categorical and run non-parametric statistics to seek out any associations. Of course, you are losing the precision and accuracy of continuous-level measurement and introducing measurement error into the outcome variable, but you will still be able to run inferential statistics.

4. You can use non-parametric statistics without changing the skewed variable at all. That is one of the primary benefits of non-parametric statistics: They are robust to violations of normality and homogeneity of variance. Instead of interpreting means and standard deviations, you will interpret medians and interquartile ranges with non-parametric statistics. 

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