Guidelines for Statistical Methods for JNEB

The following are guidelines for authors and reviewers concerning statistical methods appropriate for Research Articles and Research Briefs.

Participants
Quantitative data

  • How did the authors decide number upon the number of participants? Power analysis is the strongest rationale for determining the number of participants. If there was no power analysis another rationale for participant recruitment should be provided.
Qualitative data
  • How did the authors decide number upon the number of participants? Recruitment until responses were saturated is the preferred method of determining the number of participants. If this was not the rationale, a rationale should be provided.
Surveys
Was reliability of the survey tested?
  • Either Cronbach α for multiple items [questions] relating to a similar idea or construct; or
  • Kappa if inter-rater reliability being used; or
  • Citation of survey validated in similar population.
Data presentation
Were the following addressed in Methods and Results?
  • Determination & treatment of outliers.
  • Treatment of missing data.
  • Means and SD if data have a normal distribution; IQR and median if not normally distributed.
  • SEM used only if multiple samples gathered.
Decisions on data analysis
Did the authors provide a rationale for deciding to use parametric vs nonparametric analyses? Authors should:
  • Tell how they decided by testing the distribution of the data for normalcy; how did they test to determine if data were normally distributed [p-p plots, q-q plots, skewness and kurtosis,Wilk-Shapiro, Kolmogorov-Smirnov, or Lilliefors].
  • If normally distributed: Comparing means [t-tests] or variances [ANOVA].
  • If not normal then Chi square, Mann-Whitney U, Kolmogorov-Smirnov Z.
  • If categorical with 2 categories, binomial.
When presenting correlations
  • Outliers should be removed; check for normal distribution or if not normally distributed use log-transformation.
When using regression
  • Dependent and independent variables should be identified.
  • The variation explained by the model should be ≤ .05.
  • R value should be reported to show strength of relationship.
  • R2 should be reported to show how much of the variance is accounted for by the model.
  • Weight of the variables should be presented in a table.


Updated September 2011