# Survey design and live results: a tourism services example

## About this vignette

This vignette follows a single instrument through the complete surveyframe workflow: design the questionnaire, export it as a hosted survey with a Google Sheets backend, collect responses, clean and score them, run the analysis plan, and render a results report. The results section uses 60 simulated responses so the vignette builds offline. Replacing the simulated data with a call to `read_sheet_responses()` connects the same workflow to live responses that grow with each submission.

The questionnaire and concept are adopted from:

> Sharafuddin, M. A., Madhavan, M., & Wangtueai, S. (2024). Assessing the Effectiveness of Digital Marketing in Enhancing Tourist Experiences and Satisfaction: A Study of Thailand’s Tourism Services. Administrative Sciences, 14(11), 273. https://doi.org/10.3390/admsci14110273

The instrument covers five research areas applicable to any tourism services context: digital marketing effectiveness (relevance, accessibility, ease of use, and perceived value), destination service quality (accommodation and local transport), destination sustainability quality, tourist satisfaction, and behavioural intention. The item wording is reproduced from the original questionnaire. Researchers studying other destinations can adapt the scales and analysis plan to their own context.

## The questionnaire

### Shared Likert scale

All rated items use a five-point agreement scale.

    likert5 <- sf_choices(
      "likert5",
      values = 1:5,
      labels = c("Strongly disagree", "Disagree",
                 "Neither agree nor disagree", "Agree", "Strongly agree")
    )

### Helper functions

`stem_items()` builds items that share a common sentence stem. Each item is the stem followed by a unique completion. `solo_items()` builds standalone items. Neither function is exported, and both use only `sf_item()`.

    stem_items <- function(ids, stem, completions, scale_id) {
      Map(
        function(id, comp)
          sf_item(id, paste(stem, comp),
                  type = "likert", required = TRUE,
                  choice_set = "likert5", scale_id = scale_id),
        ids, completions
      )
    }

    solo_items <- function(ids, labels, scale_id) {
      Map(
        function(id, lab)
          sf_item(id, lab,
                  type = "likert", required = TRUE,
                  choice_set = "likert5", scale_id = scale_id),
        ids, labels
      )
    }

### Digital marketing items

Relevance and engagement (DMRE, 5 items)

    dmre_stem <- paste(
      "The digital marketing contents I encountered during my",
      "trip planning and booking phases were"
    )
    dmre_completions <- c(
      "relevant to my interests.",
      "engaging.",
      "customisable.",
      "flexible and I was able to make real-time adjustments for my demand.",
      "able to cater to my specific needs and preferences."
    )
    dmre_items <- stem_items(
      paste0("dmre_", 1:5), dmre_stem, dmre_completions, "DMRE"
    )

Accessibility and usefulness (DMAU, 5 items)

    dmau_stem <- "The contents, communication, and booking services were"
    dmau_completions <- c(
      "easy to find so I could contact service providers and book my trip directly.",
      "fast.",
      "efficient.",
      "of good quality.",
      "user friendly."
    )
    dmau_items <- stem_items(
      paste0("dmau_", 1:5), dmau_stem, dmau_completions, "DMAU"
    )

Ease of use (DMEU, 5 items)

    dmeu_stem <- paste(
      "The digital marketing contents and procedures regarding",
      "trip planning and booking were"
    )
    dmeu_completions <- c(
      "easy to learn.",
      "understandable and required little mental effort.",
      "neat and simple.",
      "easy to follow.",
      "mobile friendly."
    )
    dmeu_items <- stem_items(
      paste0("dmeu_", 1:5), dmeu_stem, dmeu_completions, "DMEU"
    )

Perceived value (DMPV, 5 items)

    dmpv_stem <- paste(
      "In terms of value, both the commercial and",
      "user-generated contents are"
    )
    dmpv_completions <- c(
      "sufficient to support eco-friendly practices.",
      "appropriate.",
      "trustworthy and credible.",
      "consistent across all digital platforms.",
      "value for money."
    )
    dmpv_items <- stem_items(
      paste0("dmpv_", 1:5), dmpv_stem, dmpv_completions, "DMPV"
    )

### Destination service quality items

Accommodation (DSQA, 4 items)

    dsqa_stem <- "The accommodation related services met my expectations for"
    dsqa_completions <- c(
      "check-in and check-out services.",
      "room cleanliness.",
      "staff attitude.",
      "safety and security."
    )
    dsqa_items <- stem_items(
      paste0("dsqa_", 1:4), dsqa_stem, dsqa_completions, "DSQA"
    )

Local transport (DSQT, 5 items)

    dsqt_stem <- "The local transport related services met my expectations for"
    dsqt_completions <- c(
      "frequency.",
      "connectivity.",
      "comfort.",
      "staff attitude.",
      "ride safety."
    )
    dsqt_items <- stem_items(
      paste0("dsqt_", 1:5), dsqt_stem, dsqt_completions, "DSQT"
    )

### Destination sustainability quality (DSUQ, 11 items)

    dsuq_labels <- c(
      "I can easily find and purchase locally-made handicrafts and souvenir products.",
      "The livelihoods of local vendors and artisans are respected, and fair prices are paid for their products.",
      "The size of food portions sold is adequate, reducing waste and leftovers.",
      "Awareness programmes are adequate in encouraging me to reduce water consumption.",
      "There are enough local guides with in-depth knowledge to enhance my travel experience.",
      "Sustainable transport options such as bikes, walking routes, and public transport are adequate.",
      "Reusable bags are adequately available for purchase.",
      "Digital infrastructure is adequate so that I can avoid printing and use digital copies.",
      "There are adequate choices of sustainable seafood in the destination.",
      "There are enough litter bins throughout the destination.",
      "There are adequate awareness signs about endangered marine species, plants, and animals."
    )
    dsuq_items <- solo_items(paste0("dsuq_", 1:11), dsuq_labels, "DSUQ")

### Tourist satisfaction (TS, 3 items) and behavioural intention (BI, 3 items)

    ts_items <- solo_items(
      paste0("ts_", 1:3),
      c(
        "The destination met or exceeded my expectations.",
        "Overall, my travel experience with the destination was good.",
        "Overall, I felt comfortable in the destination."
      ),
      "TS"
    )

    bi_items <- solo_items(
      paste0("bi_", 1:3),
      c(
        "I will recommend others to use online platforms for planning and booking their trips.",
        "I will share my experience online.",
        "I intend to revisit the destination."
      ),
      "BI"
    )

### Demographic items

    gender_cs    <- sf_choices("gender",
      c("male", "female", "transgender"), c("Male", "Female", "Transgender"))

    age_cs       <- sf_choices("age",
      c("18_25", "26_40", "41_50", "51_60", "60_plus"),
      c("18-25", "26-40", "41-50", "51-60", "60 and above"))

    visitor_cs   <- sf_choices("visitor",
      c("first_time", "repeat"),
      c("First-time visitor", "Repeated visitor"))

    freq_cs      <- sf_choices("freq_visit",
      c("lt_1", "once", "2_3", "gt_3"),
      c("Less than 1 time in a year", "Once in a year",
        "2-3 times in a year", "More than 3 times in a year"))

    nationality_cs <- sf_choices("nationality",
      c("thai", "chinese", "japanese", "korean", "indian", "australian",
        "british", "german", "american", "other"),
      c("Thai", "Chinese", "Japanese", "Korean", "Indian", "Australian",
        "British", "German", "American", "Other"))

    education_cs <- sf_choices("education",
      c("high_school", "diploma", "undergraduate", "post_graduate"),
      c("High school", "Diploma", "Undergraduate", "Post graduate"))

    profession_cs <- sf_choices("profession",
      c("student", "business", "salaried", "freelancer", "not_working"),
      c("Student", "Business", "Salaried and working", "Freelancer",
        "Not working (housewife or retired)"))

    companion_cs <- sf_choices("companion",
      c("friends", "family", "other"),
      c("Friends", "Family members", "Other"))

    group_cs     <- sf_choices("group_size",
      c("lt_3", "3_5", "gt_5"),
      c("Less than 3", "3-5", "More than 5"))

    demo_items <- list(
      sf_item("gender",        "Gender",                type = "single_choice",
              required = TRUE, choice_set = "gender"),
      sf_item("age_band",      "Age",                   type = "single_choice",
              required = TRUE, choice_set = "age"),
      sf_item("visitor_type",  "I am",                  type = "single_choice",
              required = TRUE, choice_set = "visitor"),
      sf_item("freq_visit",    "Frequency of visit",    type = "single_choice",
              required = TRUE, choice_set = "freq_visit"),
      sf_item("nationality",   "Nationality",           type = "single_choice",
              required = TRUE, choice_set = "nationality"),
      sf_item("education",     "Education level",       type = "single_choice",
              required = TRUE, choice_set = "education"),
      sf_item("profession",    "Profession",            type = "single_choice",
              required = TRUE, choice_set = "profession"),
      sf_item("companion",     "I visit with my",       type = "single_choice",
              required = TRUE, choice_set = "companion"),
      sf_item("group_size",    "My travel group size is", type = "single_choice",
              required = TRUE, choice_set = "group_size")
    )

### Scales

    make_scale <- function(id, label, ids) {
      sf_scale(id, label, items = ids, method = "mean")
    }

    scales <- list(
      make_scale("DMRE", "Digital marketing: relevance and engagement",  paste0("dmre_", 1:5)),
      make_scale("DMAU", "Digital marketing: accessibility and usefulness", paste0("dmau_", 1:5)),
      make_scale("DMEU", "Digital marketing: ease of use",               paste0("dmeu_", 1:5)),
      make_scale("DMPV", "Digital marketing: perceived value",           paste0("dmpv_", 1:5)),
      make_scale("DSQA", "Destination service quality: accommodation",   paste0("dsqa_", 1:4)),
      make_scale("DSQT", "Destination service quality: transport",       paste0("dsqt_", 1:5)),
      make_scale("DSUQ", "Destination sustainability quality",           paste0("dsuq_", 1:11)),
      make_scale("TS",   "Tourist satisfaction",                         paste0("ts_",   1:3)),
      make_scale("BI",   "Behavioural intention",                        paste0("bi_",   1:3))
    )

### Assemble the instrument

    choice_sets <- list(
      likert5, gender_cs, age_cs, visitor_cs, freq_cs, nationality_cs,
      education_cs, profession_cs, companion_cs, group_cs
    )

    rated_items <- c(dmre_items, dmau_items, dmeu_items, dmpv_items,
                     dsqa_items, dsqt_items, dsuq_items, ts_items, bi_items)

    study <- sf_instrument(
      title       = "Digital Marketing Effectiveness of Tourism Services",
      version     = "1.0.0",
      description = paste(
        "Questionnaire covering digital marketing effectiveness,",
        "destination service quality, sustainability quality,",
        "tourist satisfaction, and behavioural intention.",
        "Adopted from Sharafuddin, Madhavan & Wangtueai (2024),",
        "Administrative Sciences, 14(11), 273.",
        "https://doi.org/10.3390/admsci14110273"
      ),
      authors   = "Mohammed Ali Sharafuddin",
      languages = "en",
      components = c(choice_sets, rated_items, demo_items, scales)
    )

    study
    #> <sframe>
    #>   Title:      Digital Marketing Effectiveness of Tourism Services
    #>   Version:    1.0.0
    #>   Items:      55
    #>   Scales:     9
    #>   Analysis:   0 block(s)
    #>   Status:     not validated

## Validate and serialise

`validate_sframe()` checks every item ID, choice set reference, and scale membership before any data is collected. It returns a diagnostic that prints what it found, so printing the call is enough to read the result. `write_sframe()` saves the validated instrument with a SHA-256 hash so any post-collection edits are detectable.

    validate_sframe(study, strict = FALSE)
    #> <sframe validation>
    #>   Instrument:  Digital Marketing Effectiveness of Tourism Services (1.0.0)
    #>   Status:      valid
    #>   Checks:      18 run, 0 with problems

Read the parts of the diagnostic with the accessors rather than reaching into it. `summary()` shows every check that ran, including the ones that found nothing, which is how you tell a check that passed from a check that was never reached.

    v <- validate_sframe(study, strict = FALSE)
    sf_is_valid(v)
    #> [1] TRUE
    sf_problems(v)
    #> character(0)
    head(summary(v), 5)
    #>                  check status n_problems
    #> 1   duplicate_item_ids     ok          0
    #> 2       item_id_format     ok          0
    #> 3 duplicate_choice_ids     ok          0
    #> 4  duplicate_scale_ids     ok          0
    #> 5          item_labels     ok          0

    # Save the instrument. Keep this file alongside your analysis script.
    sframe_path <- write_sframe(study, file.path(tempdir(), "tourism_services_v1.sframe"),
                                 overwrite = TRUE)

    # Reload the instrument from disk at any time with:
    study2 <- read_sframe(sframe_path)
    identical(sf_meta(study)$title, sf_meta(study2)$title)
    #> [1] TRUE

### What the SHA-256 hash proves, and what it does not

The hash [write_sframe()] embeds confirms one thing: the `.sframe` file on disk is byte-identical to what was written. If any byte changes – an item’s wording, a scale’s membership, a plan’s variables – [read_sframe()] detects the mismatch and refuses to load the file rather than silently accepting it.

That is a narrow, specific guarantee. It proves file identity. It does not prove the survey has good questions, an unbiased sample, or a sound analysis, and it says nothing about p-hacking or HARKing on its own. The package’s actual defence against those two is a separate mechanism: the `analysis_plan` slot is bound to the instrument at design time, before data arrive, and `run_analysis_plan()` executes that pre-declared plan in one pass rather than letting a plan be assembled after looking at results. Keep the two claims distinct – “this file wasn’t edited after the fact” and “this analysis was decided before seeing the data” are different guarantees from different mechanisms, and neither substitutes for the other.

### `.sframe` is plain JSON, not a proprietary format

A `.sframe` file is ordinary, git-diffable, UTF-8 JSON – not a binary or R-specific object. Any tool that reads JSON can open one, and the format is documented independently of this package in `system.file("schema", "sframe_schema.json", package = "surveyframe")`, a JSON Schema a reviewer or a second tool can validate against without installing R.

    schema_path <- system.file("schema", "sframe_schema.json", package = "surveyframe")
    jsonlite::fromJSON(schema_path, simplifyVector = FALSE)$required
    #> [[1]]
    #> [1] "hash"
    #>
    #> [[2]]
    #> [1] "version"
    #>
    #> [[3]]
    #> [1] "meta"
    #>
    #> [[4]]
    #> [1] "items"
    #>
    #> [[5]]
    #> [1] "choices"
    #>
    #> [[6]]
    #> [1] "scales"

Because the file is plain text, two versions of it diff the same way any source file does. If `tourism_services_v1.sframe` were committed to a Git repository, revising it and running `git diff` on the file would show exactly which lines – which item, which choice, which plan block – changed, alongside Git’s own commit history for who changed it and when. That diff is Git’s job, not this package’s; see the next section for how the two connect.

### Disclosed revision: `amend_sframe()`

A hash mismatch alone can’t distinguish a legitimate correction (fixing a data-entry error, removing bot responses, correcting a misspecified model) from an undisclosed change – both break the hash identically. `amend_sframe()` gives legitimate revision a structured, disclosed path instead: it compares the instrument before and after a change, and appends a record – never overwrites – of what changed and why, directly inside the `.sframe` file next to the content it explains.

Amendments come in two tiers, because not every change carries the same risk to the plan’s design-time-binding logic:

- `"pipeline"` amendments (`data_correction`, `bot_removal`) are expected researcher hygiene and stay low-friction: a reason code and a short explanation are enough.
- `"design"` amendments (anything touching the analysis plan or a model – `model_respecification`, or `instrument_revision`/`other` by default) are exactly the kind of post-hoc change the design-time plan binding exists to guard against. They require a `deviation_report` describing what changed in the research question, method, or model and why, and record `signoff` explicitly – as a reviewer’s name, or the literal value `"none"` when no second sign-off was given, so an unreviewed design change stays visible to an auditor rather than looking identical to a signed-off one.

    # Suppose a pilot round surfaces an ambiguous item and it needs rewording.
    dmre_1_revised <- sf_item(
      "dmre_1", paste(dmre_stem, "relevant to my personal travel interests."),
      type = "likert", required = TRUE, choice_set = "likert5", scale_id = "DMRE"
    )

    study_revised <- study
    study_revised$items[[which(vapply(study$items, `[[`, character(1), "id")
                                == "dmre_1")]] <- dmre_1_revised

    study_amended <- amend_sframe(
      study, study_revised,
      reason_code = "instrument_revision",
      reason_text = "Clarified item wording after pilot feedback.",
      deviation_report = "Wording only; the construct measured is unchanged."
    )

    amendment_log(study_amended)
    #>              timestamp         reason_code
    #> 1 2026-08-20T06:56:53Z instrument_revision
    #>                                    reason_text   tier author
    #> 1 Clarified item wording after pilot feedback. design   <NA>
    #>                                     deviation_report signoff
    #> 1 Wording only; the construct measured is unchanged.    none
    #>                                                      previous_hash
    #> 1 61644747b9c720fd857fa68beb123a3548e47c005238a94098a529b871665723
    #>                                                           new_hash
    #> 1 d99b57c954077fe3066df862bce4600226058736f204d397f9ffe77784cd5284
    #>   changed_fields
    #> 1          items

`amendment_log()` returns the full history as a data frame – one row per disclosed change, printable or exported with `write.csv()` for an external audit trail. `write_sframe()` on the amended instrument persists the log in the file; a reviewer who later loads it with `read_sframe()` sees both the current content and every disclosed step that produced it. This does not weaken the hash check: an edit made directly to the file, bypassing `amend_sframe()`, still fails `read_sframe()`’s integrity check exactly as before.

Be clear about what this does and does not guarantee. `amend_sframe()` records a disclosed change – it cannot compel disclosure, the same way a preregistration deviation report can’t be forced. Nothing prevents a researcher from reconstructing an instrument from scratch and calling `write_sframe()` on it directly, skipping the amendment log entirely. What the mechanism actually guarantees is narrower and still useful: if a change is disclosed through `amend_sframe()`, the record is structured, timestamped, and permanent inside the file – not that every change will be.

### Linking a saved instrument to its Git commit

A bare hash gives no diff, author, or explanation for a change – Git already provides all three. `link_git_commit()` points the instrument at Git rather than duplicating it: it records the current commit’s SHA and subject line, so the SHA-256 hash’s role narrows to confirming that the file on disk matches what that specific, already-explained commit produced. It degrades gracefully with no error when Git isn’t installed or the path isn’t a repository – Git is optional, never a dependency of the rest of the package.

    link_git_commit(study_amended, repo_path = tempdir())
    #> $linked
    #> [1] FALSE
    #>
    #> $reason
    #> [1] "not a git repository"

## Set up data collection

### Store the Google Sheets endpoint in the instrument

When an endpoint URL is set on the instrument, `export_static_survey()` reads it automatically. Set the URL once, export as many times as needed without repeating the argument.

    # Replace with your deployed Apps Script URL after the setup steps below.
    study$render$google_sheets_endpoint <-
      "https://script.google.com/macros/s/YOUR_SCRIPT_ID/exec"

### Export the static HTML survey

`export_static_survey()` produces a single self-contained HTML file. Respondents open it in any browser, fill it in, and their submission is downloaded as a CSV and, if an endpoint is configured, posted to the Google Sheet at the same time. The file can be hosted on GitHub Pages, shared by email, or opened directly from disk.

    html_path <- export_static_survey(
      study,
      output_path = file.path(tempdir(), "tourism_services_survey.html"),
      open        = FALSE
    )
    #> Static survey written to '/tmp/Rtmpqq2PK1/tourism_services_survey.html' (85.2
    #> KB).
    file.exists(html_path)
    #> [1] TRUE

### What the respondent sees

The exported survey carries its own design, independent of the SurveyBuilder or SurveyStudio chrome used to build it. Question text sits in a serif typeface, options render as bordered cards with a selection tick, Likert items render as numbered squares, and a slim progress bar tracks completion. Every one of those colours derives from the instrument’s single theme colour, so the line below re-skins the whole survey without touching anything else.

    study$render$theme <- "#0f766e"

Two things about the export matter for planning a real deployment.

- It is designed for a phone first. Option cards and touch targets meet a 44 pixel minimum, and a matrix question (see the input-types demo in the SurveyBuilder vignette) reflows from a table into stacked, labelled cards below 600 pixels of width, so no question ever needs horizontal scrolling to complete on a small screen.
- It meets WCAG 2.2 AA. Every control carries an accessible name, keyboard focus is visible on option cards, a validation error is announced to assistive technology, and required questions are marked by more than colour alone.

### Generate the Google Sheets collector script

`export_google_sheet()` writes a Google Apps Script file. Deploy it in a Google Sheet and it creates a response tab with the correct column headers. Matrix, multiple-choice, and ranking questions each need more than one column, so the collector expands them automatically: a matrix gets one column per row, a multiple-choice question gets one 0/1 column per option, and a ranking question gets one column per option holding its rank. A five-option multiple-choice item named `channels`, for example, becomes five columns, `channels__option1` through `channels__option5`, each holding `0` or `1`, ready for analysis with no string-splitting step.

    script_path <- export_google_sheet(
      study,
      sheet_url  = "https://docs.google.com/spreadsheets/d/YOUR_SHEET_ID",
      output_dir = tempdir()
    )
    #> Apps Script written to: /tmp/Rtmpqq2PK1/surveyframe_collector.gs
    #> Follow the setup instructions inside the file to deploy it.
    file.exists(script_path)
    #> [1] TRUE

To deploy the script:

1. Open or create a Google Sheet.
2. Click Extensions, then Apps Script.
3. Paste the contents of the generated `.gs` file, replacing any existing code.
4. Click Deploy, then New deployment. Set type to Web app.
5. Set “Who has access” to Anyone.
6. Copy the Web App URL.
7. Paste the URL into `study$render$google_sheets_endpoint` above and re-export the survey.

### Pull live responses

Once the survey is live, read the growing response set with one call. Re-run this line and everything below it to refresh all results.

    responses <- read_sheet_responses(
      sheet_id   = "YOUR_SHEET_ID",
      instrument = study
    )

## Simulated responses for offline demonstration

The code block below generates 60 plausible responses so the remainder of this vignette runs without a network connection. Replace `responses` with the output of `read_sheet_responses()` and re-run from the quality section onward to use real data.

    set.seed(2024)
    n <- 60

    # Each construct is driven by a latent score plus item-level noise.
    # Correlations between constructs are introduced by sharing variance.
    lat <- function(mu, sigma) pmax(1, pmin(5, round(rnorm(n, mu, sigma))))
    add_noise <- function(x, sigma = 0.45) {
      pmax(1L, pmin(5L, as.integer(round(x + rnorm(n, 0, sigma)))))
    }

    # Higher service quality and sustainability lift satisfaction.
    lat_dsqa <- lat(3.7, 0.6)
    lat_dsqt <- lat(3.6, 0.6)
    lat_dsuq <- lat(3.5, 0.6)
    lat_ts   <- pmax(1, pmin(5, round(
      0.4 * lat_dsqa + 0.3 * lat_dsqt + 0.2 * lat_dsuq + rnorm(n, 0.6, 0.3)
    )))
    lat_bi   <- pmax(1, pmin(5, round(0.7 * lat_ts + rnorm(n, 0.5, 0.4))))

    # Digital marketing constructs are loosely correlated with each other.
    lat_dmre <- lat(3.8, 0.6)
    lat_dmau <- pmax(1, pmin(5, round(0.5 * lat_dmre + rnorm(n, 1.9, 0.4))))
    lat_dmeu <- pmax(1, pmin(5, round(0.4 * lat_dmre + rnorm(n, 2.2, 0.4))))
    lat_dmpv <- pmax(1, pmin(5, round(0.3 * lat_dmre + rnorm(n, 2.5, 0.4))))

    # Repeat visitors score slightly higher on satisfaction and intention.
    visitor_type <- sample(c("first_time", "repeat"), n,
                           replace = TRUE, prob = c(0.45, 0.55))
    lat_ts[visitor_type == "repeat"] <- pmin(5L, lat_ts[visitor_type == "repeat"] + 1L)
    lat_bi[visitor_type == "repeat"] <- pmin(5L, lat_bi[visitor_type == "repeat"] + 1L)

    # Build item columns from latent scores.
    make_cols <- function(lat, k, prefix) {
      setNames(
        as.data.frame(
          vapply(seq_len(k), function(i) add_noise(lat), integer(n))
        ),
        paste0(prefix, seq_len(k))
      )
    }

    sim_df <- cbind(
      data.frame(
        respondent_id = sprintf("R%03d", seq_len(n)),
        submitted_at  = format(
          seq(as.POSIXct("2025-01-10 09:00", tz = "UTC"),
              by = "1 hour", length.out = n),
          "%Y-%m-%dT%H:%M:%SZ"
        ),
        started_at = format(
          seq(as.POSIXct("2025-01-10 08:50", tz = "UTC"),
              by = "1 hour", length.out = n),
          "%Y-%m-%dT%H:%M:%SZ"
        ),
        gender = sample(c("male", "female", "transgender"),
                        n, TRUE, c(0.44, 0.55, 0.01)),
        age_band = sample(c("18_25", "26_40", "41_50", "51_60", "60_plus"),
                          n, TRUE, c(0.15, 0.40, 0.25, 0.15, 0.05)),
        visitor_type = visitor_type,
        freq_visit = sample(c("lt_1", "once", "2_3", "gt_3"),
                            n, TRUE, c(0.15, 0.25, 0.35, 0.25)),
        nationality = sample(
          c("thai", "chinese", "japanese", "korean", "indian",
            "australian", "british", "german", "american", "other"),
          n, TRUE),
        education = sample(c("high_school", "diploma", "undergraduate", "post_graduate"),
                           n, TRUE, c(0.05, 0.10, 0.48, 0.37)),
        profession = sample(c("student", "business", "salaried", "freelancer", "not_working"),
                            n, TRUE, c(0.15, 0.20, 0.45, 0.12, 0.08)),
        companion = sample(c("friends", "family", "other"),
                           n, TRUE, c(0.35, 0.55, 0.10)),
        group_size = sample(c("lt_3", "3_5", "gt_5"),
                            n, TRUE, c(0.30, 0.50, 0.20)),
        stringsAsFactors = FALSE
      ),
      make_cols(lat_dmre, 5, "dmre_"),
      make_cols(lat_dmau, 5, "dmau_"),
      make_cols(lat_dmeu, 5, "dmeu_"),
      make_cols(lat_dmpv, 5, "dmpv_"),
      make_cols(lat_dsqa, 4, "dsqa_"),
      make_cols(lat_dsqt, 5, "dsqt_"),
      make_cols(lat_dsuq, 11, "dsuq_"),
      make_cols(lat_ts,   3, "ts_"),
      make_cols(lat_bi,   3, "bi_")
    )

    # Align the data frame to the instrument.
    responses <- read_responses(sim_df, study,
                                 respondent_id = "respondent_id",
                                 submitted_at  = "submitted_at",
                                 meta_cols     = "started_at",
                                 strict        = FALSE)

    cat("Respondents:", nrow(responses), "\n")
    #> Respondents: 60
    cat("Columns:    ", ncol(responses), "\n")
    #> Columns:     58

## Quality checks

    qr <- quality_report(responses, study, respondent_id = "respondent_id")
    # as.data.frame() gives the summary row, so no reaching into the object.
    qr_summary <- as.data.frame(qr)
    quality_summary <- data.frame(
      Metric = c("Respondents", "Items", "Flagged for review", "Flag rate"),
      Value  = c(qr_summary$n_respondents, qr_summary$n_items, qr_summary$n_flagged,
                 sprintf("%.1f%%", 100 * qr_summary$flag_rate)),
      stringsAsFactors = FALSE
    )
    kable(quality_summary, align = c("l", "r"), caption = "Quality screening summary")

Quality screening summary
Metric | Value
--- | ---
Respondents | 60
Items | 55
Flagged for review | 59
Flag rate | 98.3%

The flagged count reflects straight-lining detection on simulated data, where random responses often repeat values. With real survey responses the flagging rate is typically much lower. The flag marks respondents for researcher review, not automatic exclusion.

    mr <- missing_data_report(responses, study)
    # as.data.frame() returns the item-level table, which is what the report is
    # mainly about.
    kable(as.data.frame(mr), digits = 2,
          col.names = c("Variable", "Missing (n)", "Missing (%)", "Valid (n)"),
          caption = "Item-level missingness")

Item-level missingness
| Variable | Missing (n) | Missing (%) | Valid (n)
--- | --- | --- | --- | ---
dmre_1 | dmre_1 | 0 | 0 | 60
dmre_2 | dmre_2 | 0 | 0 | 60
dmre_3 | dmre_3 | 0 | 0 | 60
dmre_4 | dmre_4 | 0 | 0 | 60
dmre_5 | dmre_5 | 0 | 0 | 60
dmau_1 | dmau_1 | 0 | 0 | 60
dmau_2 | dmau_2 | 0 | 0 | 60
dmau_3 | dmau_3 | 0 | 0 | 60
dmau_4 | dmau_4 | 0 | 0 | 60
dmau_5 | dmau_5 | 0 | 0 | 60
dmeu_1 | dmeu_1 | 0 | 0 | 60
dmeu_2 | dmeu_2 | 0 | 0 | 60
dmeu_3 | dmeu_3 | 0 | 0 | 60
dmeu_4 | dmeu_4 | 0 | 0 | 60
dmeu_5 | dmeu_5 | 0 | 0 | 60
dmpv_1 | dmpv_1 | 0 | 0 | 60
dmpv_2 | dmpv_2 | 0 | 0 | 60
dmpv_3 | dmpv_3 | 0 | 0 | 60
dmpv_4 | dmpv_4 | 0 | 0 | 60
dmpv_5 | dmpv_5 | 0 | 0 | 60
dsqa_1 | dsqa_1 | 0 | 0 | 60
dsqa_2 | dsqa_2 | 0 | 0 | 60
dsqa_3 | dsqa_3 | 0 | 0 | 60
dsqa_4 | dsqa_4 | 0 | 0 | 60
dsqt_1 | dsqt_1 | 0 | 0 | 60
dsqt_2 | dsqt_2 | 0 | 0 | 60
dsqt_3 | dsqt_3 | 0 | 0 | 60
dsqt_4 | dsqt_4 | 0 | 0 | 60
dsqt_5 | dsqt_5 | 0 | 0 | 60
dsuq_1 | dsuq_1 | 0 | 0 | 60
dsuq_2 | dsuq_2 | 0 | 0 | 60
dsuq_3 | dsuq_3 | 0 | 0 | 60
dsuq_4 | dsuq_4 | 0 | 0 | 60
dsuq_5 | dsuq_5 | 0 | 0 | 60
dsuq_6 | dsuq_6 | 0 | 0 | 60
dsuq_7 | dsuq_7 | 0 | 0 | 60
dsuq_8 | dsuq_8 | 0 | 0 | 60
dsuq_9 | dsuq_9 | 0 | 0 | 60
dsuq_10 | dsuq_10 | 0 | 0 | 60
dsuq_11 | dsuq_11 | 0 | 0 | 60
ts_1 | ts_1 | 0 | 0 | 60
ts_2 | ts_2 | 0 | 0 | 60
ts_3 | ts_3 | 0 | 0 | 60
bi_1 | bi_1 | 0 | 0 | 60
bi_2 | bi_2 | 0 | 0 | 60
bi_3 | bi_3 | 0 | 0 | 60
gender | gender | 0 | 0 | 60
age_band | age_band | 0 | 0 | 60
visitor_type | visitor_type | 0 | 0 | 60
freq_visit | freq_visit | 0 | 0 | 60
nationality | nationality | 0 | 0 | 60
education | education | 0 | 0 | 60
profession | profession | 0 | 0 | 60
companion | companion | 0 | 0 | 60
group_size | group_size | 0 | 0 | 60

    # sf_apa() gives a plain-language summary suitable for a methods section.
    cat(sf_apa(mr), "\n")
    #> Missing-data diagnostics were computed for 55 variable(s).

## Score and describe

`score_scales()` appends one column per scale to the data frame, using the scoring rules stored in the instrument. If a column with the same name as a scale already exists in the data frame, `score_scales()` skips that scale so pre-scored data is never overwritten.

    scored <- score_scales(responses, study)

    scale_cols <- c("DMRE", "DMAU", "DMEU", "DMPV",
                    "DSQA", "DSQT", "DSUQ", "TS", "BI")

    # Display scale means and standard deviations as a table
    scale_summary <- data.frame(
      Scale = scale_cols,
      Mean  = round(colMeans(scored[, scale_cols], na.rm = TRUE), 2),
      SD    = round(apply(scored[, scale_cols], 2, sd, na.rm = TRUE), 2),
      row.names = NULL
    )
    kable(scale_summary, digits = 2, caption = "Scale score summary")

Scale score summary
Scale | Mean | SD
--- | --- | ---
DMRE | 3.79 | 0.63
DMAU | 3.84 | 0.57
DMEU | 3.76 | 0.58
DMPV | 3.62 | 0.58
DSQA | 3.63 | 0.63
DSQT | 3.52 | 0.74
DSUQ | 3.59 | 0.68
TS | 4.32 | 0.61
BI | 3.79 | 0.71

    op <- par(mfrow = c(3, 3), mar = c(4, 3, 2, 1))
    for (s in scale_cols) {
      v <- scored[[s]]; v <- v[is.finite(v)]
      hist(v, col = "#16B3B1", border = "white", main = s, xlab = "Score", ylab = "")
    }

    par(op)

## Reliability

    if (requireNamespace("psych", quietly = TRUE)) {
      rr <- reliability_report(scored, study, omega = FALSE)
      # One row per scale, with NA where a statistic could not be computed.
      rr_df <- as.data.frame(rr)
      rel_df <- data.frame(
        Scale = paste0(rr_df$label, " (", rr_df$scale_id, ")"),
        Items = rr_df$n_items,
        N     = rr_df$n,
        Alpha = ifelse(is.na(rr_df$alpha), "n/a", sprintf("%.2f", rr_df$alpha)),
        stringsAsFactors = FALSE
      )
      kable(rel_df, row.names = FALSE, align = c("l", "c", "c", "r"),
            caption = "Scale reliability")
    }

Scale reliability
Scale | Items | N | Alpha
--- | --- | --- | ---
Digital marketing: relevance and engagement (DMRE) | 5 | 60 | 0.87
Digital marketing: accessibility and usefulness (DMAU) | 5 | 60 | 0.86
Digital marketing: ease of use (DMEU) | 5 | 60 | 0.87
Digital marketing: perceived value (DMPV) | 5 | 60 | 0.85
Destination service quality: accommodation (DSQA) | 4 | 60 | 0.86
Destination service quality: transport (DSQT) | 5 | 60 | 0.90
Destination sustainability quality (DSUQ) | 11 | 60 | 0.95
Tourist satisfaction (TS) | 3 | 60 | 0.86
Behavioural intention (BI) | 3 | 60 | 0.82

A Cronbach’s alpha of 0.70 or above is conventionally accepted as adequate internal consistency (Nunnally, 1978). For scales with three or more items, McDonald’s omega is a more accurate estimate because it accounts for unequal factor loadings across items. Alpha assumes all items load equally on the factor. Pass `omega = TRUE` when items within a scale differ substantially in their contribution. See `?reliability_report` for details.

## EFA readiness

Before a confirmatory factor analysis, it is good practice to confirm that the item correlations are strong enough to support factoring. `efa_report()` runs the KMO measure of sampling adequacy and Bartlett’s test of sphericity, and uses parallel analysis to suggest the number of factors.

    if (requireNamespace("psych", quietly = TRUE)) {
      er <- efa_report(scored, study)
      print(er)
    }

A KMO value of 0.60 or above and a significant Bartlett test confirm that the correlation structure supports factor analysis. The suggested factor count from parallel analysis is a guide. Theory should take precedence.

## Construct validity

`validity_report()` computes composite reliability (CR) and average variance extracted (AVE) from supplied factor loadings. Discriminant validity is assessed via the Fornell-Larcker criterion and HTMT when construct scores are supplied.

    # Supply a named list of loadings (construct -> item loadings vector)
    loadings_list <- list(
      DMRE = c(dm_1 = 0.78, dm_2 = 0.82, dm_3 = 0.75),
      TS   = c(ts_1 = 0.84, ts_2 = 0.80)
    )
    vr <- validity_report(loadings_list)
    print(as.data.frame(vr))

AVE above 0.50 and CR above 0.70 are the conventional thresholds for convergent validity. The Fornell-Larcker criterion requires that the square root of each construct’s AVE exceeds its highest correlation with any other construct.

## Analysis plan

The plan binds each research question to a statistical technique and the variable roles it needs. `run_analysis_plan()` executes every block and returns one result per question.

    sf_plan(study) <- list(
      list(
        id               = "RQ1",
        research_question = "Are digital marketing perceptions associated with tourist satisfaction?",
        family           = "association",
        method           = "correlation_pearson",
        roles            = list(x = "DMRE", y = "TS"),
        options          = list(alpha = 0.05)
      ),
      list(
        id               = "RQ2",
        research_question = "Is service quality associated with tourist satisfaction?",
        family           = "association",
        method           = "correlation_pearson",
        roles            = list(x = "DSQT", y = "TS"),
        options          = list(alpha = 0.05)
      ),
      list(
        id               = "RQ3",
        research_question = "Do service quality and sustainability quality predict satisfaction?",
        family           = "regression",
        method           = "regression_linear",
        roles            = list(predictors = c("DSQA", "DSQT", "DSUQ"),
                                dependent  = "TS"),
        options          = list(alpha = 0.05)
      ),
      list(
        id               = "RQ4",
        research_question = "Do first-time and repeat visitors differ in satisfaction?",
        family           = "group_comparison",
        method           = "mann_whitney",
        roles            = list(group = "visitor_type", outcome = "TS"),
        options          = list(alpha = 0.05)
      ),
      list(
        id               = "RQ5",
        research_question = "Does satisfaction predict behavioural intention?",
        family           = "regression",
        method           = "regression_linear",
        roles            = list(predictors = "TS", dependent = "BI"),
        options          = list(alpha = 0.05)
      )
    )

    length(sf_plan(study))
    #> [1] 5

## Assumption checks

Before running the analysis plan, check statistical assumptions for the variables involved. `assumption_report()` tests normality (Shapiro-Wilk for samples below 5,000), homogeneity of variance (Levene) for grouped tests, and linearity for regression pairs.

    if (requireNamespace("psych", quietly = TRUE)) {
      ar <- assumption_report(scored, study)
      print(ar)
    }
    #> Assumption Report

Review the `$normality` and `$homogeneity` slots to decide whether parametric or non-parametric alternatives are appropriate. The analysis plan can override the test method per block if the default is not suitable.

## Run the analysis plan

    results <- run_analysis_plan(scored, study)

Each result carries an APA-formatted statistic, an effect size, a writing prompt, and the methodological reference that supports the chosen test.

    results_table(results)

RQ | Research question | Method | Result (APA) | Effect
--- | --- | --- | --- | ---
RQ1 | Are digital marketing perceptions associated with tourist satisfaction? | pearson | r(58) = 0.19 [-0.07, 0.42], p = 0.153 | small
RQ2 | Is service quality associated with tourist satisfaction? | pearson | r(58) = -0.04 [-0.29, 0.22], p = 0.778 | negligible
RQ3 | Do service quality and sustainability quality predict satisfaction? |  | R² = 0.210, F(3, 56) = 4.97, p = 0.004 |
RQ4 | Do first-time and repeat visitors differ in satisfaction? |  | U = 744, z = -4.62, p < .001, r = 0.60 [0.41, 0.76], Hodges-Lehmann shift = 0.67 [0.67, 1.00] | large
RQ5 | Does satisfaction predict behavioural intention? |  | R² = 0.291, F(1, 58) = 23.83, p < .001 |

The full writing prompt for each result is available in `r$prompt`. The first one reads:

    cat(results[[1]]$prompt)
    #> There was a positive, small non-significant correlation between DMRE and TS, r(58) = 0.19 [-0.07, 0.42], p = 0.153. Explain what this means for your research question.

The `prompt` field is a sentence template for the methods or results section. The researcher fills in the substantive interpretation. The package supplies the statistic and the label. This separation is intentional: statistical significance and practical significance are distinct judgements.

### Plots on demand

When ggplot2 is installed, `plots = TRUE` attaches a brand-styled chart to every supported block: bar charts for frequency and chi-square blocks, and scatter plots with a regression overlay for correlation and regression blocks. Plotting stays opt-in, so nothing changes for installations without ggplot2.

    results_p <- run_analysis_plan(scored, study, plots = TRUE)
    first_plot <- Filter(function(r) !is.null(r$plot), results_p)[[1]]
    first_plot$plot

Inferential blocks also return a `$table` data frame ready for `knitr::kable()`, and the HTML report picks both up automatically.

## Results report

`render_results()` writes one section per research question. `render_report()` adds a codebook, quality summary, and descriptives alongside the analysis results.

    results_path <- render_results(
      results,
      study,
      output_file = file.path(tempdir(), "tourism_results.html")
    )
    cat("Results report written:", results_path, "\n")
    #> Results report written: /tmp/Rtmpqq2PK1/tourism_results.html
    cat("Size:", round(file.size(results_path) / 1024, 1), "KB\n")
    #> Size: 13.5 KB

The results report contains one section per research question, with the APA statistic, effect size, writing prompt, and the reference for the chosen method, each paired with its own chart drawn directly beneath its table. A Likert item’s distribution renders as a diverging stacked bar, with agreement and disagreement categories running away from a shared zero line and any neutral category split evenly across it, so the balance of opinion is readable at a glance rather than inferred from a plain frequency bar.

    render_report(
      study,
      data             = scored,
      output_file      = file.path(tempdir(), "tourism_report.html"),
      include_codebook = TRUE,
      include_quality  = TRUE,
      include_missing  = TRUE,
      include_descriptives = TRUE,
      include_reliability  = TRUE,
      include_analysis = TRUE,
      include_models   = FALSE
    )

## The live workflow

The section below shows the full sequence from a live Google Sheet. Every step from `read_sheet_responses()` onward is identical to the simulated workflow. Re-run this block each time you want updated results.

    # 1. Pull the latest responses from the Google Sheet.
    responses <- read_sheet_responses(
      sheet_id   = "YOUR_SHEET_ID",
      instrument = study
    )

    # 2. Run quality checks on the new data.
    quality_report(responses, study, respondent_id = "respondent_id")

    # 3. Score the scales.
    scored <- score_scales(responses, study)

    # 4. Run the pre-declared analysis plan.
    results <- run_analysis_plan(scored, study)

    # 5. Render the updated report.
    render_report(
      study,
      data             = scored,
      output_file      = "tourism_report_latest.html",
      include_codebook = TRUE,
      include_quality  = TRUE,
      include_missing  = TRUE,
      include_descriptives = TRUE,
      include_reliability  = TRUE,
      include_analysis = TRUE,
      include_models   = FALSE
    )

As more respondents complete the survey, re-running from the `read_sheet_responses()` call above refreshes every result, table, and figure in the report without changing any analysis code.

## Summary

The instrument built in this vignette can be loaded into SurveyBuilder for visual editing or distributed directly as the exported HTML file. The Google Sheets script connects online submissions to R through a single function call. Because the questionnaire, the scales, and the analysis plan are stored together in the sframe, the design and the analysis travel as one object.

    citation("surveyframe")