RESEARCH DISCIPLINE
Statistics Support for Marketing Experiments
With Several Stimuli
Between-subjects marketing experiments analyzed as they were designed: randomization verified, manipulation checks reported, exclusions fixed in advance, and differences between conditions stated as estimates with intervals, not as significance alone.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
How Is a Scenario-Based Marketing Experiment Analyzed?
Statistics support for marketing experiments covers the analysis of randomized studies of ads, prices, messages and scenarios, including designs with crossed factors and several stimuli per condition. Experimental marketing research analysis starts from the design: participants are randomized to one version, so a difference between conditions can support a causal reading of the manipulated factor. Scenario-based experiment analysis checks first that the scenario was perceived as intended and then estimates how large the effect is, keeping each contrast tied to the stimulus participants saw and reporting it with an interval. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, plans and analyzes such studies with its in-house PhD-level statisticians. What an effect means for a brand or a manager is the researcher’s judgment.
WHEN TO ENGAGE
Statistical Input Across a Marketing Experiment
Most of what decides an experiment's credibility is fixed with the stimuli and the plan, long before the first response arrives.
Conditions Being Designed Scenarios or stimuli are being drafted, and the number of conditions, the outcome measures and the participants per condition are chosen so the key contrast is estimated with useful precision.
Pilot Data in Hand A pretest has returned, and the manipulation check, the spread of the outcome and the dropout by condition show whether the main study can go ahead as designed.
Main Study Collected Responses from the full sample are in, and the exclusion rules, the check on randomization and the primary contrast are applied as set out in the plan, before outcomes are inspected.
Effect Questioned in Review A reviewer asks whether the effect holds across stimuli, whether a mediator can carry a causal claim or why several outcomes were tested, and the answer has to come from the data.
CONDITIONS AND CONTRASTS
Analyzing Between-Subjects Marketing Experiments
The sequence mirrors the experiment: confirm that randomization and the manipulation worked, state the primary contrast, then turn to moderators and mechanisms in that order.
Randomization Check
Condition sizes and pre-treatment characteristics are described by condition, and any departure from the planned allocation, such as a quota that filled unevenly on the panel, is reported before outcomes are compared.
Manipulation Checks
Whether participants perceived the scenario as intended is estimated condition by condition, so a null result can be told apart from a manipulation that did not take.
The Primary Contrast
The effect of the manipulated factor is the difference in means or proportions, with an interval and a standardized effect size, using the method from Comparative & Hypothesis Testing that suits the outcome.
Crossed Factors
When two factors are crossed, the interaction is examined first, and the effect of one factor at each level of the other is reported with its interval and not only as a significance flag.
Mechanisms
A mediator measured after the manipulation is not randomized, so an indirect effect estimated in Structural Equation Modeling (SEM) assumes no unmeasured cause of both mediator and outcome, and the causal wording stays within that assumption.
Tables for Marketing Journals
Tables and wording follow the conventions of Marketing research within Business, Management & Economics, with the participants per condition shown for every estimate.
THREATS TO THE CONTRAST
Threats to Validity in Scenario Experiments
Randomization makes the conditions comparable, but the stimuli, the order of the measures and the reading of the results can still undo what it achieved.
One Ad Standing for a Category Each condition is represented by a single ad or scenario, so the manipulated feature is confounded with everything else about that stimulus, and the effect cannot be generalized beyond it without several stimuli per condition.
Check Asked Before the Outcome A manipulation check placed before the outcome measure draws attention to the manipulated feature, so responses to the outcome may reflect what participants guessed the study was about.
Wording Tuned on the Same Data Stimulus wording is revised after a first wave shows which version gives a significant result, and the same responses then count as confirmation, so the p-value no longer means what it says.
Covariates Measured Afterwards An attitude measured after the manipulation is added as a covariate, though the manipulation may have changed it, so adjusting for it can bias the contrast being estimated.
Convenience Panel Read as Market A volunteer online sample is described as the consumer population, so the effect is generalized to shoppers whom the study never reached.
Subgroup Difference Inferred An effect is significant in one subgroup and not in another, and the two are called different without a test of whether the effect itself differs between them.
DELIVERABLES
Experiment Records and Condition Tables
The records separate what was planned from what was found, so a reviewer can see which rules were set before the outcomes were inspected.
Plan for the Experiment The primary outcome, the contrast, the exclusion rules and the treatment of secondary outcomes, fixed before outcomes are examined and kept as a dated record.
Randomization and Flow Table Participants entering, completing and excluded in each condition, with the reason, and the comparison of baseline characteristics across conditions.
Manipulation Check Results The check for each manipulated factor with its estimate and 95% confidence interval by condition, and a note on what it implies for interpretation.
Condition Contrast Tables Means or proportions by condition, the contrast with its 95% confidence interval and standardized effect size, and the participants behind each.
Stimulus Record The final wording, images and order of every scenario as participants saw them, so a reader can see exactly what differed between conditions.
Experiment Analysis Script One script that applies the exclusion rules, computes the checks and contrasts and produces every table from the exported survey platform file.
ILLUSTRATIVE EXAMPLE
A Two-by-Two Design With Three Products per Category
An illustration of how a crossed-factor experiment is analyzed when each category is represented by several stimuli and not by one.
ILLUSTRATIVE EXAMPLE · SAVINGS FRAMING EXPERIMENT RESEARCH · UNITED STATES
A doctoral researcher in marketing randomized online participants to a saving shown as a percentage or as an amount off, for a product from a practical category or from an indulgent one, with three different products in each category, and measured perceived value of the offer on a multi-item scale.
The plan named the interaction of framing and category as the primary question and fixed the exclusion rules before data arrived. The interaction was examined first, and the effect of framing was then reported within each category with its interval. The complication was the stimuli: in the indulgent category, one of the three products accounted for most of the framing difference. Because three products per category are too few to estimate how much products vary, they were not treated as a sample of all products. Instead the effect was shown separately for each product and again with each product left out, and the conclusion was worded for these six products, with the note that a claim about each category as a whole would need more products per category.
Outcome: The researcher received the analysis plan, the participant flow table, the interaction estimate with its 95% confidence interval, the framing effect within each category with its interval, the estimates for each product and with each product left out, and the script.
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
Fitting Contrasts and Mediators From a Platform Export
Experiments usually arrive as an export from a survey platform, and the contrasts, interactions and mediators planned for them can be fitted in any of these four.
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
Scenario Design and Brand Implications Are Research Choices
The stimuli, the theory they test and what an effect implies for a brand are research decisions, while the checks, contrasts and tables are delivered as agreed.
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 or journal manuscript remains yours.
Your Research Decisions The research question, the design of scenarios and measures, the interpretation of effects for brands or consumers, 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
Cell Sizes, Stated Intentions and Multiple Outcomes
Planning enough participants per cell, reading stated intention against real buying and handling several outcome measures decide how far a marketing experiment's claim can reach.
What falls within the analysis of a marketing experiment?
Statistics support for marketing experiments covers the planning and analysis of randomized studies of ads, prices, messages and scenarios: the sample per condition, randomization and manipulation checks, exclusion rules, the primary contrast with its interval, and models for moderators and mechanisms. The effect is stated in the units of the outcome, so its practical size can be judged.
How many participants does each condition need?
There is no fixed number per cell. It depends on the smallest effect worth detecting, the variability of the outcome and the planned analysis, and an interaction needs more participants than a main effect of the same size, because it is a difference between differences with a larger standard error. A pilot supplies the variability, and the plan aims for an interval on the key contrast narrow enough to be useful.
Does a scenario experiment show that consumers would actually buy?
It shows the effect of the manipulated feature on the response that was measured, such as stated intention or a choice among hypothetical options. Stated intention and real purchase can diverge, so a claim about buying needs a behavioral measure or a field test, and the wording of the conclusion follows what was measured.
How should several outcome measures be reported?
One primary outcome is declared before data are examined, and the others are reported as secondary, each with an estimate and an interval. If several outcomes serve one hypothesis, a composite or a joint test is considered, or the error rate is adjusted, so that the outcome that happens to be significant is not presented as the finding.
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 experimental design, the exported data file, the condition codes and any preregistration, 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
Isolating the Feature
Your Scenario Manipulates
A difference between conditions can be credited to the manipulated feature only when the stimuli differ in that feature alone, so the design of the scenarios sets the limits of every claim the analysis supports.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014