- Research is a process
- The better the quality of the earlier stages, the better the quality
of the results section

- The better the quality of the earlier stages, the better the quality
of the results section
- General discussion of what needs to happen before you start writing
- Careful planning of each stage of the research
- Power analysis (or a detailed analysis plan)
- What, how and how long

- The goal is to reduce panic
- Use grad school as an opportunity to learn new statistical techniques and software packages
- How long ago was your last stats class?
- Real data can be real messy

- Four issues that are often problematic – 1. Missing data
- Learn pros and cons of various techniques
- No hard-and-fast rules
- Package to do the imputation
- Package to analyze the imputed data set(s)

- Four issues that are often problematic – 2. Small sample sizes
- Common stat techniques are often not appropriate
- Model may not run for numerical reasons
- Assumptions may not be met
- "Fall back" to a simpler technique
- Issues of fair and accurate reporting

- Four issues that are often problematic – 3. Survey data
- Not like data from experiments
- Different commands or a different stat package

- Four issues that are often problematic – 4. Correlated data
- Patients or doctors (nested) in hospitals, etc.
- Several possible ways to analyze correlated data
- May have to analyze the data in multiple ways

- The results section is an extension of the methods section
- Clear and precise analysis plan
- Changing analyses
- Need to understand the stat techniques and their assumptions just as you need to understand your substantive area
- A lot of work between getting the appropriate output and being ready to explain the results to others (AKA writing the results section)
- ANOVA example

- Specifics
- Set up the research question clearly and precisely
- Establish the reader’s expectations
- What to leave in and what to leave out? Tell a story
- No relationship between the amount of time something took and how much space in the write-up it gets
- Careful balance between enough detail to replicate the experiment
and space limitations imposed by the journal

- Describing your data
- Avoid including p-values in the description of the data
- Purpose: description, not hypothesis testing
- alpha inflation
- With 10 tests, the nominal alpha level is .40, not .05

- Planning, fair and accurate reporting
- Reproducibility

- Avoid including p-values in the description of the data
- Analyses
- Order your hypotheses (and hence, analyses) from most to least important
- Confusion about the meaning of a specific p-value
- False precision
- Statistical significance versus clinical relevance
- Report effect sizes

- Where to find examples
- Articles that report similar analyses
- Write-ups can vary widely between disciplines
- DAE pages, annotated outputs, Long and Freese (2006)

- Examples
- Coding dichotomous variables – meaning of coefficient and interaction term
- Categorical predictor variables
- Logistic regression
- Confidence intervals
- Interaction terms
- Bivariate tests

- Watch words
- chance

- odds
- risk

- probability

- significance (statistical or clinical, parameter or model)

- likelihood

- standardized (variable, coefficient, test scores)

- normal

- controlling for (this is an idea that is in the analyst’s head, not the
program analyzing the data)

- covariates

- robust (regression, standard errors, findings)

- nested, hierarchical (models, data)

- random (variables, intercepts, slopes, effects)

- chance
- Tables and graphs
- Can be more difficult than it seems to create clear and useful tables and graphs
- Clarity is paramount; less is more
- Several useful references are available
- The Visual Display of Quantitative Information by Edward R. Tufte,
- Visual and Statistical Thinking: Displays of Evidence for Making Decisions by Edward R. Tufte,
- Visual Explanations by Edward R. Tufte,
- Visualizing Data by William S. Cleveland,
- Displaying Your Findings: A Practical Guide for Creating Figures, Posters and Presentations by Adelheid A. M. Nicol and Penny M. Pexman
- Presenting Your Findings: A Practical Guide for Creating Tables by Adelheid A. M. Nicol and Penny M. Pexman.
- Please note that all of the these books are available for loan from our Statistics Books for Loan

- Distinction between what goes in the results section and what goes in
the discussion section (discipline specific)

- Things to avoid
- "more significant" (use effect size or omega-squared instead)
- "almost significant"
- Don’t bother speculating about why non-significant results are non-significant
- Just say no to post-hoc power analyses

- Future trends – Increasing sophistication of statistical analyses
- In times past, theory and/or software was not available
- A "fancier" model is not necessarily better
- Good match between the data and the analysis technique
- Missing data example

- Future trends – The use of the web
- Researchers or journals posting data and syntax on web site
- Use syntax (instead of point-and-click)
- Useful if you need to replicate or modify an analysis for an R&R
- Document data transformations, analyses and thought process
- Honesty about the number of significance tests run on the data

- Resources mentioned in this presentation
- ATS Statistical Computing

Data Analysis Examples

Annotated output - Annotated Output
- Online Seminars including
- Power Analysis
- Statistics Books for Loan
- Applied Statistics Courses Offered at UCLA
- Statistical Consulting Schedule
- Statistical Consulting Services

The main page for this seminar is here .

- ATS Statistical Computing