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  Belen 9:11 am on September 26, 2025 Permalink  

  Guide users through text input like a pro 

  You’ve seen it happen: someone stares at an empty text box for 10 seconds, types something, deletes it, then types something completely different.

  “What format do they want?” “How detailed should this be?” “What’s a good example?”

  Empty text boxes can be intimidating. But with the right guidance, people fill them out confidently and give you exactly what you were asking for.

  GuidedTrack’s *placeholder and *tip features solve this by putting helpful instructions exactly where users need them – right at the point of input.

  Two Ways to Help Users

  *placeholder – Gentle Hints Inside the Box

  Placeholder text appears inside the text box and disappears as soon as the user starts typing. Perfect for showing format examples or quick hints:

  

  The user sees the example format right in the text box, but it vanishes the moment they start typing their actual number.

  *question: What's your phone number?
  	*placeholder: e.g., +1234567890
  	*save: phone_number
  

  Use *placeholder for:

  • Format examples (phone numbers, dates, email addresses)
  • Quick hints that don’t need to be remembered
  • Simple guidance that fits in one line

  *tip – Persistent Instructions Above the Box

  The *tip keyword displays helpful information below the question but above the input area, and it stays visible while users type:

  *question: What was your most recent strange dream like?
  	*tip: For instance, you might say "I dreamt I turned into a toaster."
  	*save: dream_description

  The tip stays put, so users can reference it while they’re writing their response.

  

  Use *tip for:

  • Complex instructions that are hard to memorize
  • Examples that users might want to reference while typing
  • Longer guidance/explanations

  Why This Improves Your Data Quality

  📐 More consistent formats: Placeholder examples show exactly how you want information formatted.

  💡 Richer detail: Tips that explain why you’re asking encourage more thoughtful responses.

  🔄 Less back-and-forth: Clear guidance upfront prevents the need to follow up for clarification.
  

  Getting Started

  Both features work with any *question that accepts text input:

  *question: Your question here
  	*placeholder: Format hint that disappears when typing starts
  	*tip: Persistent guidance that stays visible while typing
  	*save: variable_name

  Small Details, Big Impact

  These aren’t flashy features, but they make a real difference in how people experience your forms.

  The person filling out your survey shouldn’t have to guess what you want. A simple placeholder or tip can turn confusion into confidence, blank responses into useful data.

  Your users will appreciate the guidance, and you’ll appreciate the better responses.

   
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  Belen 7:34 am on July 24, 2025 Permalink  

  Survey data that makes sense 

  Imagine you’re running a course evaluation in which students select which topics they studied, then rate their confidence in each one.

  A common issue with most survey platforms that generate questions dynamically is that, when you download the data, it looks like this:

  StudentID	Q3_Text	Q4_Response
  001	Algebra, Statistics	4|3
  002	Geometry, Algebra	2|5
  003	Statistics, Geometry	3|5

  Which rating goes with which school subject? You’ll need to do some work to figure that out.

  A Different Approach

  With GuidedTrack’s data::store, you can create columns based on what students actually tell you:

  *question: Which topics did you study this semester?
  	*save: topics_studied
  	*multiple
  
  *for: topic in topics_studied
  	*question: How confident do you feel about {topic}? (1-5 scale)
  		*answers: [1,2,3,4,5]
  		*type: slider
  		*save: confidence_level
  	>> data::store("{topic}_confidence", confidence_level)

  This will generate columns that tell you exactly what each number means and your data will look something like this:

  StudentID	Algebra_confidence	Statistics_confidence	Geometry_Confidence
  001	4	3
  002	5		2
  003		4	5

  Key advantages of using this feature

  • Clear column names: Instead of remembering what Q4_Response means, you see “Algebra_confidence.”
  • No predefined lists: If a student studies “Linear Algebra” or “AP Statistics,” those become column names automatically. You don’t need to guess every possibility upfront.
  • Flexible data structure: Your columns match what people actually studied, not what you thought they might study.

  Common use cases

  • Product feedback where customers choose features to rate
  • Event surveys where attendees select sessions to evaluate
  • Employee surveys where people rate specific departments
  • Complex academic studies where different participants answer different questions or see different stimuli
  • MaxDiff and Best Worst studies where participants see different sets of options and you need separate columns for each combination

  In short, this feature is helpful when your GuidedTrack program asks users to choose or enter a list of items, and you want to follow up with a question about each one they selected.

  How to use *data::store

  The syntax is:

  >> data::store(columnName, value)

  Just replace columnName for the name you want to see in your csv file for that column, and value for the variable containing this column’s value.

   
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  Belen 1:20 pm on June 24, 2025 Permalink  

  Tip: Use *experiment to run better studies with balanced groups 

  Picture this: You just finished running a study with 100 participants, each randomly assigned to one of three interventions, to compare how effective they are. You’re excited to analyze the results, but when you open your CSV file, your heart sinks. Group A has 45 people, Group B has 32, and Group C has only 23.

  Your beautiful research design just got a lot more complicated.

  If this sounds familiar, you’re not alone. Unbalanced groups are a common headache in research, and they can seriously mess with your statistical analysis. The good news? GuidedTrack’s *experiment feature solves this problem automatically.

  What makes *experiment special?

  In most survey platforms and research tools, getting balanced groups can be a nightmare. You either have to manually track participant counts, use complex external randomization services, or just cross your fingers and hope for the best.

  GuidedTrack’s *experiment feature handles all of this automatically by ensuring that group sizes never differ by more than 1 person. Here’s the basic concept of how it works. Suppose you have two groups in your study. Then, *experiment makes sure that the first two participants end up in different groups from each other (selected at random). The next two people also end up in different groups from each other. And so on. Each pair is randomized to one of the two groups, keeping the size of each group balanced.

  Real examples of *experiment in action

  🧠 Testing Different Meditation Techniques

  Let’s say you want to compare three approaches to stress reduction:
  

  *experiment: Meditation_Study
  	*group: Visualization
  		For the next 10 seconds, visualize yourself in a warm, happy place.
  		*wait: 10.seconds
  
  	*group: Writing
  		For the next 10 seconds, write about a warm, happy place.
  		*question: My warm happy place is...
  	
  	*group: Control
  		For the next 10 seconds, just sit quietly and breathe normally.
  		*wait: 10.seconds
  

  With 90 participants, you’ll end up with exactly 30 people in each group. No more, no less.
  

  📱 A/B Testing App Interfaces

  Product teams can benefit from using *experiment to test different user interfaces:
  

  *experiment: Button_Color_Test
  	*group: Red_Button
  		--[...Set the button color as red...]
  	
  	*group: Blue_Button
  		--[...Set the button color as blue...]
  	
  	*group: Purple_Button
  		--[...Set the button color as purple...]
  

  Whether you get 47 users or 150 users, each version will be seen by roughly the same number of people.

  Advantages of using *experiment

  Statistical Power: Balanced groups give you the best chance of detecting real differences between your conditions. Unbalanced groups can hide important findings or make you think you found something when you didn’t.

  Easier Analysis: Most statistical tests work best with equal group sizes. You won’t need to worry about weighted analyses or explaining why your groups are different sizes.

  Professional Results: When you present your findings, balanced groups look intentional and well-planned (because they are).

  When to use *experiment

  Use *experiment when:

  • You’re comparing different treatments or conditions
  • You plan to analyze differences between groups
  • You want balanced sample sizes
  • You’re running A/B tests

  You might not need it when:

  • You just want variety in your content
  • You’re showing random examples or scenarios or you have a very complex randomization scheme (use *randomize instead)
  • Balance isn’t important for your goals

  Syntax of *experiment

  *experiment: Your_Experiment_Name
  	*group: Group_A
  		-- Content that only participants assigned to Group A will see
  
  	*group: Group_B
  		-- Content that only participants assigned to Group B will see

  When you examine the CSV file, you’ll see a column labeled with your experiment’s name in the header row. Each row beneath it indicates the group assigned to that particular run.

  Tips for creating studies with *experiment

  1. Name your experiments clearly: “Onboarding_Flow_Test” is much better than “Experiment_1” when you’re looking at data six months later.
  2. Keep groups meaningful: Don’t create 12 tiny groups when 3 larger ones would give you better statistical power.
  3. Document your hypotheses: Before you launch, write down what you expect to find. Your future self will thank you.