How surveys are weighted, adjusted, and read, with step-by-step analysis methods for SPSS, Python, and the browser.
Posts cover the full path from raw responses to a defensible result: base weights, nonresponse adjustment, raking, design effects, and the checks that prove the work.
Latest in Survey Research and Analysis How-To
First Post: Wednesday, November 18, 2026
The first Survey Research and Analysis How-To post arrives on Wednesday, November 18, 2026. New posts appear here as they publish.
Guides on the Shelf
Analysis Studio: The Complete How-To Guide
How to use Analysis Studio from first load to finished result: setup, survey designs, licensing, and every one of the twenty modules step by step.
Read the GuideHow to Weight a Survey, Step by Step
The full weighting workflow in order: base weights, nonresponse adjustment, raking and post-stratification, trimming, and the diagnostics that prove the work.
Read the GuideWhat Is a Design Effect?
The design effect prices complex samples and weighting in one number. How it is computed, what drives it, and how it converts a nominal sample into an effective one.
Read the GuideHow Many Responses Do You Need?
Sample size for surveys and studies, worked out in the open: margins of error, group comparisons, design effects, and response rates, each with the arithmetic shown.
Read the GuideStatistical Power, Explained with Worked Numbers
What power is, what moves it, and what an underpowered study costs, with the numbers worked: power curves, detectable effects, and the four levers.
Read the GuideEffect Sizes: Reporting How Much, Not Just Whether
Cohen d, odds ratios, Cramér V, eta squared, and hazard ratios, each with its interval and a worked figure, plus the bands and their limits.
Read the GuideWhich Statistical Test Should You Use?
A decision guide across the Analysis Studio roster: by outcome type, grouping structure, pairing, and survey design, to the procedure that answers the question honestly.
Read the GuideReading A/B Test Results
The difference is the headline, the interval is the story, and the sample ratio mismatch check decides whether either can be trusted. A worked test, read properly.
Read the GuidePropensity Scores for Nonprobability Samples
How pseudo-weighting an opt-in sample against a reference probability sample works, what it corrects, what it cannot correct, and how to report it.
Read the GuideSurvey Trend Analysis Across Waves
Testing change over time in repeated cross-sectional surveys: per-wave estimates, polynomial trend contrasts on real time values, and two-wave change tests done honestly.
Read the GuideCronbach's Alpha: Interpretation and Reporting
Reading alpha honestly: the bands, the item-count effect, alpha if item deleted, why high alpha is not unidimensionality, and a reporting paragraph that holds up.
Read the GuideReading a Kaplan-Meier Curve
Censoring marks, the number at risk, median survival, and the log-rank test, taught on one small curve computed step by step.
Read the GuideAnalysis Studio Module Guides
A guided tour of every Analysis Studio module: what it computes, what it needs, and how to read its results.
Browse the ModulesGo Deeper
More Pillars
Statistics for Investors
The statistics behind investing, worked on real data: averages that mislead, risk measures and their limits, and what backtests can and cannot show.
Browse the PillarValuation
Company valuation with the arithmetic shown: discounted cash flow, multiples, and the assumptions that move the answer.
Browse the PillarSmall Area Estimation
Estimates for counties, towns, and other small places, where direct survey numbers run too thin to stand on their own.
Browse the PillarMaine by the Numbers
Maine in data: population, work, housing, and health, county by county, from public sources.
Browse the Pillar