RESEARCH DISCIPLINE
Social Science Statistics Support
for Binary, Ordinal and Count Outcomes
Statistics for social sciences in practice: choosing the regression model for sociology, political science, education and neighboring fields, from logit vs probit models for a yes-or-no outcome to ordinal and count models, with every estimate reported with its interval.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Which Model Fits a Binary, Ordinal or Count Outcome?
Social science statistics support covers the choice and fitting of the regression model when the outcome is not continuous: a yes-or-no measure, a rating on an ordered scale or a count of events. The model is chosen for the scale of the outcome. Among binary outcome models in social research, logit yields odds ratios and probit assumes a normally distributed latent error; both are reported as predicted probabilities or marginal effects, since raw coefficients from different models are hard to compare. When the data come from a complex survey, the weights and design are handled as described under General Social Survey Analysis Support. OnlineSPSS is a specialist statistical consultancy for academic, clinical and professional research, and its in-house PhD-level statisticians apply these methods and record each choice. Reading the estimates through social theory falls to the researcher.
WHEN TO ENGAGE
Social Survey and Panel Data at a Decision Point
What the work involves depends on whether a model has already been chosen or the outcome is still waiting to be modeled.
A Yes-or-No Outcome The outcome is employed or not, voted or not, and the choice between logit, probit and a linear probability model has to be made and the effect reported in a form readers can interpret.
A Rating Scale Outcome The dependent variable is an agreement or frequency scale, and the choice between an ordinal model, a binary split and a continuous treatment has to be made and justified.
Subgroups at the Center Differences between regions, cohorts or demographic groups are central, so interactions and marginal effects must be estimated and interpreted instead of reading coefficients across separate models.
A Count With Many Zeros The outcome counts arrests, visits or children, with the variance above the mean or zeros piled up, and Poisson, negative binomial or a zero-inflated model must be chosen and defended.
MODEL CHOICES
Regression Choices for Non-Continuous Outcomes
In the social sciences the outcome scale decides the model, and the effect measure decides how the result is read by someone outside the analysis.
Outcome Scale
Binary, ordinal, count and continuous outcomes each take their own model, chosen from the scale of measurement and the effect measure the question needs, within Regression & Predictive Modeling.
Comparable Effects
Logit vs probit models usually give similar predicted probabilities for a binary outcome, but their coefficients sit on different scales and are not directly comparable across models or groups, so average marginal effects or predicted probabilities carry the comparison.
Interactions
Whether an effect differs by gender, cohort or region is tested with an interaction and shown on the scale of interest, using a prespecified list of moderators instead of a search for differences.
Count Outcomes
Counts are fitted with Poisson or negative binomial models, checked for overdispersion and excess zeros, with rate ratios or predicted counts reported and exposure time included where it differs.
Respondents Within Places
Respondents nested in schools, neighborhoods or countries are analyzed with Linear Mixed Models (Multilevel Modeling), and with few countries the group-level effects are read with caution.
Reporting by Field
Tables and wording follow the conventions of Education & Social Sciences and Sociology, with the number analyzed shown in every model.
TYPICAL FLAWS
Model-Choice Errors Reviewers Catch
These errors seldom stop a model from running, so they survive until a reviewer reads the methods section.
Logit Coefficients Compared Across Models Coefficients from nested models or different groups are compared directly, although unobserved variation rescales them, so a change may reflect the scale of the model and not a real difference.
Counts Fitted as Continuous A count with many zeros is fitted by least squares, or by a Poisson model whose overdispersion is never checked, so standard errors are too small and predictions can fall below zero.
Ordinal Scale Treated Casually A five-point attitude item is analyzed as continuous by habit, or as ordinal with the proportional odds assumption never examined.
Controls Added Until It Works Covariates are added or dropped until the key coefficient reaches significance, which makes the reported interval too optimistic and the specification impossible to reproduce.
Thin Subgroup Cells Estimates for small subgroups are reported from very few respondents, so the intervals are wide and the point estimates swing with a handful of cases.
Few Countries, Large Claims A comparison of a small number of countries is read as a finding about countries in general, although the data carry little information on between-country variation.
DELIVERABLES
Model Files, Choice Memo and Tables
Recodes and model choices are recorded where a reviewer would look for them, beside the estimates they affect.
Model Choice Memo A note giving the outcome scale, the model chosen and the alternatives considered, with the reason for the choice and the diagnostics that support it.
Variable Construction File Recodes, scale scores and the handling of don't-know and refusal codes, with the counts affected, so the analysis sample can be rebuilt from the raw file.
Nested Model Table Nested models side by side on the same respondents, with the change in fit and average marginal effects reported beside the coefficients, so the comparison does not rest on the logit scale.
Predicted Probability Figures Figures showing predicted outcomes across the range of key predictors and groups, with confidence bands, ready for a results section.
Interaction Results Tests of the planned interactions with the effect in each group on the outcome scale, and a statement of which ones were prespecified.
Replication Program A single script that rebuilds the analysis sample and refits every model in order, with the specifications stated at the top, so a reviewer can rerun the results.
ILLUSTRATIVE EXAMPLE
Second-Year Enrollment and Working Hours
An illustration of how a binary outcome, nested models and an interaction were handled when coefficients could not be compared directly.
ILLUSTRATIVE EXAMPLE · UNIVERSITY ENROLLMENT RESEARCH · UNITED STATES
A higher education researcher used institutional records to ask whether first-generation undergraduates were less likely to enroll for a second year, and whether weekly paid work changed that difference after accounting for prior attainment.
The outcome was binary, so logit and probit fits were compared and gave very similar predicted probabilities; logit was kept for its odds ratios. The complication was that the coefficient for first-generation status changed when work hours entered the model, and part of that change came from the rescaling of logit coefficients, not only from the share of the gap that work hours accounted for. Average marginal effects were therefore reported for each nested model on the same undergraduates. The interaction was shown as the difference in predicted probabilities between groups across work hours, with intervals, and a linear probability model served as a robustness check. Results were worded as associations.
Outcome: The researcher received the average marginal effects and predicted probabilities with 95% confidence intervals, a model choice memo, the nested model table, the figures and the replication program.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
SOFTWARE
Models for Categorical and Count Outcomes
The package depends on the models required and on what your institution and readers use, and each analysis is checked so that the specification fitted is the one reported.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
RESEARCH RESPONSIBILITY
Social Theory Guides the Reading of Each Estimate
A gap between groups or regions takes its meaning from theory and from the policy setting, so interpreting it, and deciding what it implies, is work the researcher keeps.
Agreed Statistical Support OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, defined in a scope that is confirmed before any work begins.
No Intellectual Contribution OnlineSPSS does not claim any intellectual contribution to your research. Authorship of the dissertation, the journal article or the policy report remains yours.
Your Research Decisions The theoretical framing, the choice of populations and comparisons, the interpretation of the findings and the conclusions remain yours, with overall responsibility.
Confidential Handling Research data and project details are handled confidentially, project information is not published, and an NDA is available where required.
FREQUENTLY ASKED QUESTIONS
Logit, Probit, Counts and Interactions
Choosing between logit and probit, modeling counts with many zeros and reading a logit interaction on the probability scale.
What does social science statistics support cover?
Social science statistics support covers the regression models and group comparisons used to analyze survey, panel and administrative data about people and institutions, with a focus on binary, ordinal and count outcomes. The work includes choosing a model for the outcome scale and reporting effects on a scale a reader can interpret. The theoretical argument and the substantive conclusions belong to the researcher.
Should I use logit or probit for a binary outcome?
In most social research the two give very similar predicted probabilities, so the choice rarely changes the conclusions. Logit is more common because its coefficients convert to odds ratios. Logit coefficients are roughly 1.6 times the size of probit coefficients, because the two models fix the variance of the latent error at different values, so a logit and a probit coefficient are never compared with each other. Average marginal effects or predicted probabilities carry the comparison, and a linear probability model is a reasonable check when the probabilities are not close to 0 or 1.
Why can an interaction look different on the probability scale?
In a logit model, a coefficient for the product term describes the interaction on the log-odds scale, and an effect that is constant there still varies on the probability scale. The interaction is therefore reported on the scale the question concerns, usually as differences in predicted probabilities between groups with intervals, and the scale is named in the text. Probit model interpretation follows the same logic with a normal link: the product-term coefficient sits on the latent index scale, so the interaction is likewise reported as differences in predicted probabilities.
How do I model a count outcome with many zeros?
Start with the distribution. Poisson regression assumes that the variance equals the mean, which social data seldom satisfy, so overdispersion is checked and negative binomial regression, or Poisson with robust standard errors, is used. Excess zeros raise a substantive question: whether zeros come from a separate process, as in zero-inflated and hurdle models, and a fit statistic alone does not settle that. Results are reported as rate ratios or predicted counts with intervals.
How do you protect my research data and confidentiality?
Your data and project details are handled confidentially, and project information is not published. Researcher names and institutions are not used publicly without permission. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, such as the codebook, the outcome variables and the dataset, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
The Scale Behind Each Effect
in Your Results
A coefficient, an odds ratio and a predicted probability answer different questions, and deciding which of them a paper reports belongs in the statistical plan.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014