AddMaple vs Q Research Software: A Modern Replacement for Most Teams

You've been using Q for years. It's powerful. Your organization has invested in training, scripts, and institutional knowledge. But today, you're facing a client deadline in 48 hours. You need crosstabs for four banners, text theme analysis, statistical validation, and a shareable dashboard.

In Q, this means hours of table building, manual text tagging (or hoping your scripts still work), exporting to PowerPoint, and dealing with client access. By the time you're done, you'll have answered their questions—but you'll have spent the whole day doing it.

There's a faster way. AddMaple handles the 80% of your Q workflows that teams actually run daily. It's not a replacement for every Q use case. But for most client projects, exploratory analysis, and quick iterations, it's dramatically faster. And it's built from the ground up for survey data, which means fewer workarounds.

What Q Does (And Why It's Heavy)

Q is incredible if you have time and complexity. You can:

Build elaborate banner frameworks with nested cuts and custom logic
Write QScripts to automate recurring analyses across surveys
Handle complex weighting and data transformations
Build bespoke table logic that persists year after year

But that power comes at a cost: setup time. Before you answer your first question, you're building tables, configuring banners, testing logic. For a one-off project, you spend 2–3 hours setting up before you can even explore. For recurring trackers, Q's investment pays off. For ad-hoc projects, it's overkill.

And there's a secondary cost: sharing. Q exports static tables and charts to PowerPoint, Excel, or PDF. Your client or stakeholder can't explore. They can't re-run the analysis with different filters. They just see what you chose to show them.

The Same Project: Two Different Paths

Imagine you just got a product satisfaction tracker exported from a Q study. You have 500 responses with:

Respondents by: Region (5 levels), Product Tier (3 levels), Tenure (3 levels), Usage Intensity (3 levels)
Key outcomes: Overall satisfaction (1–5), NPS (0–10), Purchase intent (1–5)
Multi-response question: "Which features matter most?" (Checkboxes)
Open-ended: "What should we improve?"

You need:

Crosstabs of satisfaction & NPS by major segments
Multi-response summary with shares (not double-counted)
Theme analysis of open-ended feedback
Significance tests on key comparisons
A client-ready dashboard showing the story
Turnaround: 48 hours

The Q Path

Step 1: Import and set up (~45 min) Export from your source system as CSV or .sav. Import into Q. Set up variable definitions, recode any dirty data, assign weights if needed. Confirm your missing codes and levels. Q's interface is powerful but dense—you're clicking through menus to define each element.

Step 2: Build banner framework (~60 min) You want crosstabs by: Region, Product Tier, Region × Product Tier (nested), Usage Intensity. In Q, you build this by defining a banner—specifying the cuts and nesting. You might write QScript to automate this if you do it regularly. But one-off? You're clicking.

Step 3: Create base tables (~45 min) For each outcome (satisfaction, NPS, purchase intent), you create a table: rows are your questions, columns are your banner. You might have 3–4 base tables. In Q, you build each one, check the logic, export to the workspace.

Step 4: Multi-response handling (~30 min) Your "features matter most" question is multi-response. You set it up so Q counts respondents, not selections. You create a separate table showing feature shares. Q handles this correctly, but you need to remember the right settings.

Step 5: Manual text analysis (~2+ hours) You have 500 open-ended responses. Q doesn't have built-in AI text clustering. Your options:

Read and manually code all 500 (not happening in 48 hours)
Use an external tool (Qualtrics TextIQ, Reltio, or another add-on) and import results back
Run a QScript that clusters based on keywords (fragile)
Hire a research assistant

Most teams either use an external tool (add-on cost, integration headache) or pick a representative 50 responses and manually code. Either way, it's time.

Step 6: Statistical testing (~30 min) You run chi-square tests on your key crosstabs. Q shows you p-values. You calculate effect sizes manually or export to SPSS/R. You make decisions about which differences to highlight.

Step 7: Export and assemble (~60 min) Export your tables and charts to PowerPoint. Clean up formatting. Add commentary. Build a narrative. The client gets a 20-slide deck, but they can't re-run the analysis themselves.

Step 8: Iterate (Uh oh) The client asks: "Can you break this out by just the West region?" You go back into Q, create a new banner with one region, rebuild tables, re-export, re-format. This takes another 30–45 minutes per request.

Total time: 5–7 hours. If the client makes changes, add another 1–2 hours per iteration.

The AddMaple Path

Step 1: Import and type check (~5 min) Export the Q data as CSV. Upload to AddMaple. AddMaple auto-detects types: recognizes your 1–5 scales, spots the multi-response, flags your text column. You confirm the detections (usually all correct) and you're ready. No variable setup, no recoding UI, no dialog boxes.

Step 2: Explore core segments (~5 min) Create your first pivot: Satisfaction (rows) by Region (columns). You see the distribution instantly. No banner building, no table configuration. Just one click and you have numbers. You can add Product Tier as a filter or a second dimension just as fast.

Step 3: Build your key crosstabs (~10 min) Satisfaction × Region. NPS × Product Tier. Purchase Intent × Region × Product Tier. Each cross-tab is one pivot. AddMaple shows counts and percentages automatically. Sample sizes are visible. No manual configuration.

Step 4: Multi-response done right (~2 min) Create a pivot: Product Tier × Features (multi-response). AddMaple automatically applies Multi-Select logic. Each feature shows as a percentage of respondents, not a raw count. If 280 of your 500 respondents chose "Mobile," it shows 56%. Clean. Correct. No formula fiddling.

Step 5: Text analysis with AI (~10 min) Click on your open-ended column. Click ✨AI Coding. You can guide the AI with custom instructions ("focus on feature requests, not praise") or let it generate themes automatically. AddMaple clusters all 500 responses and proposes: "Performance Issues," "Mobile Experience," "Pricing Concerns," "Integration Gaps," "Onboarding Help," etc.

Each theme comes with descriptions and representative quotes grounded in real user language. You verify, rename themes to match your language, merge overlapping ones, or refine manually by highlighting specific text. If you discover a new theme while reviewing, AddMaple automatically applies it to all remaining responses.

Compare theme frequency by region in one pivot: instant. See which regions mention which pain points.

Step 6: Statistical validation (~5 min) Toggle on Significance Testing in your Satisfaction × Region cross-tab. AddMaple color-codes cells: warm (above expected), cool (below expected). Hover any cell: z-score, p-value, Cohen's h effect size. No export, no manual calculation. Decision made.

Step 7: Build and share a dashboard (~10 min) Pin your top 5–6 insights: satisfaction by tier (with significance), NPS by region, feature shares, top 3 themes with quotes, and a key finding. Add one-sentence notes on each: "Enterprise customers show +18pp higher 5-star rating (p=0.002, Cohen's h=0.28). They prioritize performance and advanced features."

AddMaple's Story Dashboards support multiple pages, text sections, images, and videos for richer storytelling. Create one dashboard for executives, another for the operations team.

Click Publish. AddMaple generates a read-only link (optionally password-protected). Client clicks the link. They see your Story Dashboard. They can filter by region. They can click on themes to see all quotes. They can't edit or see raw data. It's polished and safe.

Step 8: Client asks for West region only (~1 min) They ask: "Just show me the West region data." You don't rebuild anything. The dashboard link already has filtering built in. They click "West" and all charts update. If you want a separate dashboard just for West, you pin the same charts with the filter applied: 2 minutes.

Total time: 1 hour. Iterating for client requests: 1–2 minutes per filter/segment.

Specific Features That Close Gaps

Let me zoom into areas where AddMaple either matches or exceeds Q for typical survey work:

Correct multi-response handling: AddMaple's Multi-Select logic treats checkboxes as share-of-respondents by default. No setup. No formulas. This alone saves hours for surveys with "select all that apply" questions.

Weighting: Apply a weight column in AddMaple's project settings. All pivots, cross-tabs, statistical tests, and dashboard charts automatically use weighted calculations. You see weighted and unweighted bases in the status line. See Weighting.

Complex banners: Build nested cuts visually in AddMaple. "Region × Product Tier" is a two-click pivot. Export formatted crosstabs to Excel with your choice of banners and questions. See Export Crosstabs (Excel). For the 80% of use cases (standard demographics cross-tabs), AddMaple is faster than Q's banner framework.

AI text analysis: AI coding clusters open-ended responses into themes with descriptions and real examples from your data. You can:

Provide custom instructions to guide the AI
Generate themes automatically or supply your own codebook
Verify, refine, and merge themes before applying to all responses
Manually edit codes and highlight exact text matches
Add new codes and have AI find them across all remaining records

Compare theme frequency across segments. This replaces hours of manual coding or expensive external tools.

Significance testing: Significance Testing runs chi-square on every cross-tab cell and color-codes results. Hover to see z-scores, p-values, effect sizes. This is faster than Q's test export → Excel workflow.

Grouping Likert questions: If you have multiple items on the same scale (e.g., three satisfaction questions), Group them so they appear together in charts. This is cleaner than Q's item handling for grid questions.

Dashboards and sharing: Publish interactive dashboards where stakeholders can filter and explore without seeing raw data or formulas. No client licenses needed. No PowerPoint exports. Just a secure link.

Key drivers and clustering: For deeper analysis, AddMaple includes Key Driver Analysis (random forest + regression) and Clustering to segment mixed survey data. Q has this too, but AddMaple's interface is more intuitive.

When You Still Need Q

AddMaple covers 80% of survey workflows. The 20% it doesn't:

Complex QScripts and automation: If you run large trackers driven by automated QScripts that reshape data, run custom logic, and output hundreds of tables to a workflow, you need Q. AddMaple doesn't support scripting.

Highly bespoke table frameworks: If your organization has invested years in a custom table setup (specific nesting, custom calculations, legacy banner structures), moving to AddMaple means rebuilding. Q's persistence wins here.

Deep SPSS/R integration: If your pipeline includes statistical modeling in SPSS or R that feeds back into Q, keep Q for that part. Use AddMaple for exploration and dashboards.

Rigid multi-wave tracker logic: If you're managing a large tracker with fixed table logic that repeats month-to-month, Q's setup saves you time if you do it once. AddMaple shines for ad-hoc projects, not rigid trackers.

The Migration: From Q to AddMaple

You don't need to replace Q overnight. Most teams do a hybrid approach:

For new projects: Export labeled CSV from your source system (or from Q). Upload to AddMaple. Run your analysis in 1–2 hours instead of 5–7 in Q. Use AddMaple for exploration, text theming, stats, and dashboards.

For existing trackers: If you have a large Q tracker, keep it running. Export the labeled data and explore in AddMaple for faster client sharing and iterative analysis. When it's time to refresh the tracker, consider rebuilding in AddMaple (you'll find it simpler and faster).

For SPSS data: AddMaple reads .sav files directly. Upload, confirm types, and start analyzing. All your value labels carry over.

Real-World Example: The 48-Hour Client Project

You get a request: "We need a summary of our product satisfaction survey by region, product tier, and tenure. Show me what's driving satisfaction differences. Include themes from open feedback. Turnaround: 48 hours."

In Q: You'd spend the first 18–20 hours setting up tables, banners, text tagging, and formatting. You'd have 4–6 hours left for actual insights. You'd miss the deadline or deliver a rough cut.

In AddMaple: You spend 1 hour uploading data, building pivots, analyzing themes, and toggling on significance testing. You spend 2 hours writing narrative and tweaking the dashboard. You have 21 hours left to sleep and do other work. You deliver polished, interactive results 36 hours early.

That's the difference.

FAQ

Does AddMaple replace Q entirely? For most survey analysis, yes. AddMaple covers 80% of what teams actually do day-to-day. Keep Q for complex scripting, automation, or legacy workflows.

Can I import Q exports into AddMaple? Yes. Export labeled CSV or XLSX from Q. Upload to AddMaple. Your variable names and value labels carry over. You're analyzing in minutes.

What about weighting in AddMaple? Apply a weight column in AddMaple's project settings. All analyses use weighted bases. You see "Weighted n=X" in the status line. See Weighting.

Can I use both Q and AddMaple together? Absolutely. Use Q for complex trackers and scripted automation. Use AddMaple for exploration, client dashboards, and faster iteration. Export labeled data from Q; import to AddMaple.

What about statistical tests AddMaple doesn't have? AddMaple covers chi-square, t-tests, ANOVA, correlations, key drivers, regression, and clustering. For niche tests, run them in Q or R and bring results into an AddMaple dashboard.

How do I handle SPSS files? Upload .sav files directly to AddMaple. Your labels and types are preserved. If your file has complex structures (multiple response sets, weighted data), export to CSV first, then upload.

Does AddMaple support banners like Q? AddMaple lets you build complex banners visually: Region, Product Tier, Region × Product Tier (nested). For the vast majority of crosstab needs, this is faster than Q's banner framework. Export formatted crosstabs to Excel. See Export Crosstabs (Excel).

The Choice

You have a survey. Q is on your desk. You can open it and spend 5 hours building tables. Or you can upload to AddMaple and have answers in 1 hour.

Q is powerful and persistent. Use it for complex automation and large recurring trackers where the setup cost is amortized over time.

AddMaple is fast and intuitive. Use it for everything else: exploration, client projects, quick iterations, text analysis, and shareable dashboards.

Most organizations end up using both. And that's fine. Different tools for different jobs.

Ready to try the faster path? Upload your first survey and see how quickly you can answer questions.