Hello everyone! I have been away for awhile and am glad to be back. I have been cranking out 93 pages worth of a non-provisional patent for Research Engineer. I filed provisionally last year and after over 1.3 million hits in the first year, I decided the time, effort, and money was worth it!

Thank you for your interest and use of Research Engineer over the past year! I have a laundry list of things to add to the website and will be posting more regularly now that this patent application is completed! Wish me luck!

Check out the site!

-Dr. H

The Intraclass Correlation Coefficient (ICC) is a measure of inter-rater reliability that is used when two or more raters give ratings at a continuous level.  There are two factors that dictate what type of ICC model should be used in a given study.

1.  Will the raters given ratings for all observations?

2.  Are the raters a sample from the overall population or are the raters the only people in the population?

When raters do not give ratings on all observations (i.e. three ratings are given from a random sampling of three raters out of a possible six independent raters), then the One-Way Random model is used.

When raters give ratings for all observations (i.e. three ratings are given from three raters from the overall population for each observation), then the Two-Way Random model is used.

When raters give ratings for all observations and the raters are the only valid members of the population (i.e. three ratings are given from the most esteemed scholars in an area), then the Two-Way Mixed model is used.

Between-subjects statistics are used when comparing independent groups on an outcome. Independent groups means that the groups are "different" or "independent" from each other according to some characteristic. With between-subjects designs, participants can only be part of one group (independence) and only observed once (independence of observations, IOO).

One chooses a between-subjects statistical test based on the following:

1. Number of independent groups being compared (one group, two groups, or three or more groups)

2. Scale of measurement of the outcome (categorical, ordinal, or continuous)

3. Meeting statistical assumptions (independence of observations, normality, and homogeneity of variance)

Here is a list of between-subjects statistical tests and when they are utilized in applied quantitative research:

1. Chi-square Goodness-of-fit - One group, categorical outcome, a priori hypothesis for dispersal of outcome

2. One-sample median test - One group, ordinal outcome, a priori hypothesis for median value

3. One-sample t-test - One group, continuous outcome, meet the assumption of IOO and normality, a priori hypothesis for mean value

4. Chi-square - Two independent groups, categorical outcome, and chi-square assumption (more than five observations in each cell)

5. Fisher's Exact test - Two independent groups, categorical outcome, and when the chi-square assumption is not met

6. Mann-Whitney U - Two independent groups, ordinal outcome, and when the assumption of homogeneity of variance for independent samples t-test is violated

7. Independent samples t-test - Two independent groups, continuous outcome, meet the assumption of IOO, normality (skewness and kurtosis statistics), and homogeneity of variance (also known as homoscedasticity, tested with Levene's test)

8. Unadjusted odds ratio - Three or more independent groups, categorical outcome, chi-square assumption, choose a reference category and compare each independent group to the reference

9. Kruskal-Wallis - Three or more independent groups, ordinal outcome, and when the assumption of homogeneity of variance is violated

10. ANOVA - Three or more independent groups, continuous outcome, meet the assumption of IOO, normality, and homogeneity of variance

Check out the following new pages in Research Engineer!
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Mean
Median
Mode
Variance
Standard deviation
Interquartile range
Simple random sampling
Stratified random sampling
Clustered random sampling
Purposive sampling
Convenience sampling
Nominal variables
Interval variables
Ratio variables
Count variables
Hierarchical regression

As promised, a new non-inferiority trial calculator has been published in Research Engineer. Go the Non-Inferiority Trial or Calculators page to download it for free. Thank you!

Also, check out the flyers that we are going to start giving out! Thoughts?

A new Equivalency Trial Calculator for both categorical and continuous outcomes is now available in Research Engineer! Check it out on both the Equivalency Trial page and the Calculators page. Non-inferiority calculator to come!  

A new Sitemap is available for navigating the 334 webpages of Research Engineer.

A new Search page has been implemented as well.

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-EH

Surveys and the outcomes they generate are oftentimes not able to meet the assumption of normality, as per skewness and kurtosis statistics.  Also, some types of variables are just naturally skewed (i.e. income, length of stay at a hospital), and thus require the use of non-parametric statistics.

Spearman's rho correlation is considered non-parametric because it is the correlational test used when finding the association between two variables measured at an ordinal level.  Ordinal level measurement does not possess a "true zero" and therefore cannot possess the precision and accuracy of continuous variables.

Pearson's r is used when correlating two continuous variables.  However, one MUST check for the assumption of normality and identify and make decisions about any outliers (observations more than 3.29 standard deviations away from the mean).  This is of PARAMOUNT IMPORTANCE because correlations are highly influenced by outlying observations.  Just ONE outlier can artifically skew a correlation positively or negatively, and in a statistically significant fashion!

Going back to the introduction, remember to use Spearman's rho on interval and ordinal variables as well as with variables that are naturally skewed.  Statistics, in and of itself as a science, is very flawed.  Not everything you come across in existence will fit the normal curve.  Luckily, we have non-parametric statistics that are robust to these common violations of inferential statistical tests.

Hello dear friends and patrons! Thank you so much for your interest in the website! We now have a copyright for Research Engineer! It feels so great to type and highlight "c" and hold down "alt-0-1-6-9" for the site! Check it out in the footer below!

Fighting out of Knoxville, TN,

EH

Sensitivity and specificity are important diagnostic measures that provide evidence of a diagnostic test's ability to either "detect disease" or "identify the healthy," depending upon the clinical context.

In order to conduct diagnostic testing, the results of the diagnostic test of interest have to be compared to the results of existing "gold standard" method of diagnosis in a defined population. The results of both the diagnostic test and "gold standard" have to be quantified as a dichotomous categorical variable (positive or negative, "+" or "-").   

Sensitivity is the ability of a diagnostic test to detect disease. It is the percentage of people that tested positive or "+" with both the diagnostic test and the "gold standard." A diagnostic test with high sensitivity is good at picking up cases of a given disease state. It is also able to "rule out" disease states.

Specificity is the ability of a diagnostic test to identify the healthy. It is the percentage of people that tested negative or "-" with both the diagnostic test and the "gold standard." A diagnostic test with high specificity is good at detecting cases that do not require more intensive treatment. It is also able to "rule in" disease states.

It is optimal to have a diagnostic test that can both detect disease (sensitivity) AND identify the healthy (specificity). There is an absolute inverse relationship between sensitivity and specificity. As sensitivity goes up, specificity will go down. Higher specificity will lead to lower sensitivity. A well-accepted criterion for a balanced diagnostic test is 80% for both sensitivity and specificity. However, given the clinical context, a certain type of diagnostic test with either higher sensitivity or specificity may be warranted.  

If the diagnostic test results are measured along a numerical continuum, then receiver operator characteristic (ROC) curves can be plotted to detect what value maximizes both sensitivity and specificity. ROC curves can also be used to compare the diagnostic efficacy of several tests concurrently and comparing area under the curve (AUC).