Statistical Analysis
Statistical analysis turns collected data into findings you can defend, including significance testing, regression, group comparisons, and the modeling a study design calls for. Praxia Insights analyzes data from research we ran and from data you already have, and explains the results in language your audience will follow.
Plenty of organizations are sitting on data they've never analyzed properly. A survey ran and somebody looked at the averages, a program collected outcomes for three years and nobody's compared them, or an analysis got done and nobody could explain what it meant to the board.
Analysis is where a study either becomes useful or doesn't. Run it badly and you'll report differences that aren't real, or miss ones that are, and either way somebody makes a decision on it.
We'll analyze what you have, tell you what it supports, and write it up so the people who need to act on it can follow the reasoning without a statistics background.
Discuss your analysis needs
What We Analyze
Survey data: descriptive results, group comparisons, significance testing, and analysis of open-ended responses.
Program and outcome data: whether outcomes changed, for whom, and whether the change holds up statistically.
Experimental and quasi-experimental data: the analysis a trial or comparison design requires, including handling attrition and baseline differences.
Assessment and instrument data: reliability, item performance, factor structure, and validation analysis.
Longitudinal data: change over time, growth patterns, and how much of it is real.
Administrative and operational data: whatever your organization already collects and has never had analyzed.
Methods
Descriptive analysis
Distributions, central tendency, and variation, presented so people can see the shape of the data instead of only its average, because an average on its own hides most of what's interesting.
Multilevel and clustered analysis
When people are nested inside classrooms, sites, or organizations, the analysis has to account for it. Studies that ignore clustering routinely report effects that aren't there.
Group comparisons and significance testing
Testing whether the differences between groups are real or the kind of variation you'd expect by chance, using the test that fits the data you have.
Psychometric analysis
Reliability, item analysis, and factor structure for assessments and instruments, so you know whether a measure is behaving the way it's supposed to.
Regression and modeling
Examining how outcomes relate to multiple factors at once, including which relationships hold up when you account for the others.
Effect sizes and practical significance
Whether a result is large enough to matter in practice, since a statistically significant difference can still be too small to change anything anybody does.
Explaining the Results
Analysis that nobody can interpret has no value, and this is where a lot of statistical work falls down. A report full of coefficients and p-values gets forwarded around and quietly ignored.
We write results for the people who have to use them. That means saying what was found in plain language, what it means for the decision at hand, and how confident anybody should be about it, with the technical detail available in an appendix for whoever wants to check it.
We'll also tell you when the analysis doesn't support the claim somebody was hoping to make. That conversation is easier before a report goes to a funder than after.
Project Examples
A NIH-funded study examining neuropsychological predictors of adaptation after trauma.
A five-year randomized control trial (RCT), funded by the U.S. Department of Ed, to study individualized math instruction.
Led a national survey study in partnership with Berklee BEATL & Adobe to study AI use in the music industry.
How We Work
1. Initial consultation and plan
We meet to discuss your specific needs and confirm this is the right approach for the decision you're facing.
2. Project scoping and budget
You receive a custom scope of work with tasks, due dates, and budget before any work begins. Fixed scope, no hourly surprises.
3. Project execution
You receive updates on progress throughout, including recruitment status and fielding milestones.
4. A true partnership approach
We share insights with you as they come up throughout the project, so your team can start acting on what we learn long before the final report is finished.
5. Project completion
We deliver final insights in the format your team will actually use, whether that's a written report, an executive presentation, or a working session.
Frequently Asked Questions
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Yes, and it's one of our more common requests. Send us what you have, whether that's survey exports, program records, or a partly finished analysis. We'll tell you what it can support, and we'll be honest if the way the data was collected limits what you can conclude from it.
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We work in SPSS, R, and Python depending on what the analysis needs, and we'll deliver results in whatever format works for you. If your organization standardizes on a particular tool, tell us and we'll work in it.
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Two to five weeks for most projects, depending on how clean the data is and how complex the analysis needs to be. Data cleaning often takes longer than the analysis itself, especially with data pulled from several systems.
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It depends on the volume and state of the data, the complexity of the analysis, and how the results need to be presented. Analysis of a clean single dataset costs the least, and multilevel modeling across a messy multi-site dataset costs the most. We'll give you a fixed-scope quote after an initial consultation.
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It's normal, and it's usually workable. Inconsistent categories, duplicate records, missing values, and mismatched identifiers across systems are all things we handle. We'll tell you upfront how much cleaning is involved so it's in the scope from the start.
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It means a result is unlikely to have come about by chance alone, given the sample you have. What it doesn't mean is that the result is large or important. A tiny difference can be statistically significant in a big sample, so we always report effect sizes alongside significance.
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Yes. We write findings for the audience that has to use them, and we'll present to a board or a funder if that helps. Statistical results that only a statistician can read tend not to change anything.
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That depends on how the data was collected. If there's a comparison group and the design supports a causal claim, then yes. If you only have outcomes for participants, we can tell you what changed but not whether your program caused it, and we'll state that distinction plainly in the reporting.
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We report what we found. Overstating results is a real liability with funders and boards, who are generally good at spotting it, and it costs you credibility on every report afterward. Mixed results are also more useful than they feel, because they tell you what to change.
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Yes, and you should ask. Deciding the analysis in advance is how you make sure you collect what you'll need, and it prevents the situation where a study finishes and the question can't be answered with what was gathered.
Our Experience
More than 60 studies designed, including case study, quasi-experimental, RCT, and mixed-method designs
More than 100 focus groups and in-depth interviews conducted
More than $4 million in grant-funded research managed
Clients spanning Fortune 500 companies, startups, nonprofits, universities, and federally funded programs
Our researchers hold doctorates in educational neuroscience, psychology, and related fields
Have data that needs analyzing?
Tell us what you have and what you're trying to find out, and we'll advise on what it can support and what it would take.