Read-only sample study. A complete walkthrough — from problem statement to a validated report — using concept_test.csv (300 rows × 14 cols). Sign in to run your own.
Sample study · correlational design
New Product Concept Test
Research information
Statement of the problem
This consumer-research study evaluates three new product concepts (A, B, C) on purchase intent, price fairness, and a 6-item brand-fit scale, with 300 respondents randomly assigned across concepts and asked which they would buy overall.
Objectives
- To describe purchase_intent, price_fairness, brand_fit_score, and demographics across the three concepts.
- To assess the reliability of the brand-fit items brand_fit_1, brand_fit_2, brand_fit_3, brand_fit_4, brand_fit_5, and brand_fit_6.
- To determine whether purchase_intent differs across the three concepts.
- To determine whether price_fairness differs across the three concepts.
- To determine the association between preferred_overall and past_brand_user.
- To predict purchase_intent from brand_fit_score and price_fairness.
Dataset
concept_test.csv300 rows × 14 cols
| Column | Kind | Missing | Distinct |
|---|---|---|---|
| respondent_id | text | 0 | 300 |
| concept | categorical | 0 | 3 |
| age | numeric | 0 | 47 |
| past_brand_user | categorical | 0 | 2 |
| purchase_intent | numeric | 0 | 6 |
| price_fairness | numeric | 0 | 6 |
| brand_fit_1 | numeric | 0 | 7 |
| brand_fit_2 | numeric | 0 | 7 |
| brand_fit_3 | numeric | 0 | 7 |
| brand_fit_4 | numeric | 0 | 7 |
| brand_fit_5 | numeric | 0 | 7 |
| brand_fit_6 | numeric | 0 | 7 |
| brand_fit_score | numeric | 0 | 32 |
| preferred_overall | categorical | 0 | 3 |
Feasibility & estimateFree
✓ Feasibledesign: correlational · via heuristic
Detected a correlational design with 13 analyzable variable(s) and 6 analytical question(s). Recommended 7 statistical test(s). Estimated cost: 76 credits.
Recommended analysis
To describe purchase_intent, price_fairness, brand_fit_score, and demographics across the three concepts.
Descriptive Statistics(brand_fit_score)
Frequency & Percentage Distribution(concept, purchase_intent, price_fairness)
Weighted Mean with Verbal Interpretation(purchase_intent, price_fairness)
To assess the reliability of the brand-fit items brand_fit_1, brand_fit_2, brand_fit_3, brand_fit_4, brand_fit_5, and brand_fit_6.
Cronbach's Alpha(brand_fit_1, brand_fit_2, brand_fit_3, brand_fit_4, brand_fit_5, brand_fit_6)
McDonald's Omega(brand_fit_1, brand_fit_2, brand_fit_3, brand_fit_4, brand_fit_5, brand_fit_6)
To determine whether purchase_intent differs across the three concepts.
Chi-Square Test of Independence(concept, purchase_intent)
To determine whether price_fairness differs across the three concepts.
Chi-Square Test of Independence(concept, price_fairness)
To determine the association between preferred_overall and past_brand_user.
Chi-Square Test of Independence(past_brand_user, preferred_overall)
To predict purchase_intent from brand_fit_score and price_fairness.
Ordinal Logistic Regression(purchase_intent, price_fairness, brand_fit_score)
Dataset Complexity19
Statistical Tests26
AI Interpretation18
Visualizations8
Report Generation5
Total to run76 credits
Estimated runtime: ~1-2 minutes
ResultsComputed
9 tests76 credits
Descriptive Statistics (brand_fit_score)
| n | max | min | std | mean | mode | range | median | variable | variance |
|---|---|---|---|---|---|---|---|---|---|
| 300 | 39 | 7 | 6.006 | 24.573 | 22 | 32 | 24.500 | brand_fit_score | 36.072 |
Frequency & Percentage Distribution (concept, purchase_intent, price_fairness)
Weighted Mean with Verbal Interpretation (purchase_intent, price_fairness)
N300
| n | sd | item | rank | median | weighted mean | interpretation |
|---|---|---|---|---|---|---|
| 300 | 1.082 | purchase_intent | 1 | 4 | 4.443 | Somewhat Agree |
| 300 | 1.074 | price_fairness | 2 | 4 | 4.387 | Neutral |
Overall weighted mean = 4.42, interpreted as “Neutral”.
The interpretation bands assume a 1-7 scale, inferred from responses running 2-7. State the instrument's scale if it differs. · Weighted means treat the rating scale as equal-interval, which is the standard convention for summarising Likert items but is a descriptive summary, not a statistical test. The median is reported beside each mean as the measure that does not require that assumption.
Cronbach's Alpha (brand_fit_1, brand_fit_2, brand_fit_3, brand_fit_4, brand_fit_5, brand_fit_6)
α0.946N300
Internal consistency is excellent (alpha = 0.946).
McDonald's Omega (brand_fit_1, brand_fit_2, brand_fit_3, brand_fit_4, brand_fit_5, brand_fit_6)
N300
| item | loading |
|---|---|
| brand_fit_1 | 0.844 |
| brand_fit_2 | 0.894 |
| brand_fit_3 | 0.864 |
| brand_fit_4 | 0.851 |
| brand_fit_5 | 0.868 |
| brand_fit_6 | 0.862 |
Internal consistency (McDonald's omega) is excellent (omega = 0.946).
Chi-Square Test of Independence (concept, purchase_intent)
significantχ²58.02df10N300p< .001
Cramér's V0.311
medium
| 2 | 3 | 4 | 5 | 6 | 7 | row |
|---|---|---|---|---|---|---|
| 3 | 19 | 39 | 32 | 7 | 0 | A |
| 0 | 5 | 21 | 43 | 23 | 8 | B |
| 6 | 23 | 40 | 25 | 4 | 2 | C |
The result is statistically significant (p = 0.0000 < 0.05).
33% of expected cell counts are < 5; Chi-Square may be unreliable.
Chi-Square Test of Independence (concept, price_fairness)
significantχ²42.15df10N300p< .001
Cramér's V0.265
small
| 2 | 3 | 4 | 5 | 6 | 7 | row |
|---|---|---|---|---|---|---|
| 6 | 27 | 44 | 17 | 5 | 1 | A |
| 1 | 9 | 34 | 42 | 13 | 1 | B |
| 3 | 15 | 23 | 37 | 17 | 5 | C |
The result is statistically significant (p = 0.0000 < 0.05).
33% of expected cell counts are < 5; Chi-Square may be unreliable.
Chi-Square Test of Independence (past_brand_user, preferred_overall)
n.s.χ²3.579df2N300p.167
Cramér's V0.109
small
| A | B | C | row |
|---|---|---|---|
| 65 | 87 | 42 | no |
| 26 | 59 | 21 | yes |
The result is not statistically significant (p = 0.1671 >= 0.05).
Ordinal Logistic Regression (purchase_intent, price_fairness, brand_fit_score)
significantN300p.016
McFadden pseudo-R²0.009
negligible
| coef | term | p value | std err | odds ratio |
|---|---|---|---|---|
| 0.075 | price_fairness | 0.442 | 0.097 | 1.077 |
| 0.047 | brand_fit_score | 0.006 | 0.017 | 1.048 |
Ordinal logistic model of 'purchase_intent'. The result is statistically significant (p = 0.0158 < 0.05).
Report
Publication-ready report
Tests used · Results · Discussion — generated from the computed statistics.
Every claim checked · 40 numbers verified · 4 findings cross-checked · prose: llm:gemini
Reproducible code
Download the exact Jupyter notebook or Python script that reproduces these results with widely-used libraries (scipy, statsmodels, lifelines).
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