--- title: "Creating Survey Objects in surveycore" output: rmarkdown::html_vignette: toc: true toc_depth: 3 bibliography: references.bib link-citations: true vignette: > %\VignetteIndexEntry{Creating Survey Objects in surveycore} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} library(surveycore) ``` ## Introduction Every analysis function in surveycore — `get_means()`, `get_totals()`, `get_freqs()`, `get_ratios()`, `get_corr()` — takes a **survey design object** as its first argument. That object includes things like which units were clustered together, which strata were defined, what weights apply, and other relevant information so that point and variance estimation can be properly calculated. Without it, point estimates may be biased and standard errors are almost certainly wrong [@lumley2010; @lohr2022]. This vignette answers one question: *given my data, which constructor do I call and how do I call it?* This vignette covers object *creation* only. Two things not covered are: - How to create weights and how different weighting mechanisms work. - What the different analysis/estimation functions (`get_means()`, `get_totals()`, etc.) do. Those are covered in `vignette("getting-started")`. --- ## 1. Decision Guide {#sec-decision} Read the first row that matches your data. | My data... | Constructor | Why | |--------------------------------------------------------------------------------|-------------------------|--------------------------------------------------------------------| | Is a probability sample and has weights and/or cluster IDs, strata | `as_survey()` | Taylor series linearization — the general case | | Is a probability sample and has replicate weight columns (repwt_1, repwt_2, …) | `as_survey_replicate()` | Uses the agency-supplied variance replicates | | Is a pure simple random sample with no clustering or strata | `as_survey()` | Omit `ids` and `strata`; creates an SRS design | | Is a non-probability sample with weights but no replicate weights | `as_survey_nonprob()` | SRS-approximation variance; SEs understate calibration uncertainty | | Is a non-probability panel with replicate weights | `as_survey_nonprob()` | Bootstrap/jackknife variance includes calibration uncertainty | | Was sampled in two stages with an expensive Phase 2 measurement | `as_survey_twophase()` | Two-phase variance accounting for both stages | ### Common surveys at a glance | Survey | Constructor | Design | |-------------------------------------|-------------------------|------------------------------------------------------------| | NHANES | `as_survey()` | Stratified cluster, Taylor series | | ANES | `as_survey()` | Stratified cluster, Taylor series | | GSS | `as_survey()` | Stratified multi-stage cluster | | Pew NPORS | `as_survey()` | Stratified address-based sample (no PSU) | | ACS PUMS (1-year) | `as_survey_replicate()` | 80 successive-difference replicate weights | | Pew Jewish Americans 2020 | `as_survey_replicate()` | 100 JK1 jackknife replicate weights | | BRFSS | `as_survey_replicate()` | Bootstrap replicate weights | | NAEP / PISA | `as_survey_replicate()` | JK2 jackknife replicate weights | | Nationscape (Democracy Fund + UCLA) | `as_survey_nonprob()` | Non-probability quota panel; ACS-calibrated raking weights | | Opt-in online panels | `as_survey_nonprob()` | Non-probability | --- ## 2. `as_survey()` — Taylor Series Designs {#sec-taylor} `as_survey()` is the right constructor for probability surveys with cluster and/or stratum information but no pre-computed replicate weights. It uses **Taylor series linearization** (also called the linearization or delta-method estimator), the standard approach for complex probability surveys [@lumley2010, ch. 2; @lohr2022, ch. 9]. ### 2.1 Core arguments | Argument | Codebook term | What it does | |-----------|-----------------------------------------------------|------------------------------------------| | `ids` | "PSU", "primary sampling unit", "cluster ID" | Stage-1 cluster identifier | | `weights` | "sampling weight", "person weight", "design weight" | Inverse of selection probability | | `strata` | "stratum", "design stratum", "sampling stratum" | Stratification variable | | `fpc` | "FPC", "finite population correction", "N" | Population size or sampling fraction | | `nest` | (see below) | Whether PSU IDs are locally unique | All arguments accept bare column names — no `~formula` syntax required. ### 2.2 The `nest` argument Many government surveys assign PSU IDs locally within each stratum. NHANES, for example, assigns IDs 1 and 2 within *every* stratum — PSU 1 in stratum 31 is a completely different unit from PSU 1 in stratum 32. If you do not account for this, surveycore treats PSU 1 from stratum 31 and PSU 1 from stratum 32 as the same cluster, which produces incorrect variance estimates. Set `nest = TRUE` when PSU IDs are not globally unique across strata [@lumley2010, p. 28]. A quick diagnostic: ```{r nest-diagnostic} # NHANES: only two distinct PSU values, but 15 strata # Each stratum has its own PSU 1 and PSU 2 → nest = TRUE length(unique(nhanes_2017$sdmvpsu)) # 2 length(unique(nhanes_2017$sdmvstra)) # 15 ``` If the number of unique PSU values is much smaller than the number of strata, the IDs are almost certainly nested and you need `nest = TRUE`. ### 2.3 The `fpc` argument The finite population correction (FPC) reduces variance estimates when you have sampled a substantial fraction of the population [@cochran1977, §2.8; @lohr2022, §2.8]. Supply either: - An **integer column** with the total population size in each stratum - A **numeric column** (0–1) with the sampling fraction FPC has a meaningful effect when the sampling rate exceeds roughly 5% [@cochran1977]. For large national surveys like NHANES and ANES, the sampling fraction is tiny and FPC can be safely omitted (`fpc = NULL`). ### 2.4 Multi-level clustering For two-stage designs — counties then households, schools then students — pass both levels of IDs as a vector: ```r as_survey(data, ids = c(county_id, household_id), weights = wt, strata = region) ``` ### 2.5 Worked example: NHANES 2017–2018 NHANES uses a stratified, multistage probability cluster sample. The design variables are documented in the analytic notes on the NHANES website [@lumley2010, ch. 4]: | Variable | Role | Argument | |------------|-----------------------------------------------------------|-----------| | `sdmvpsu` | Masked variance PSU (cluster ID) | `ids` | | `sdmvstra` | Masked variance stratum | `strata` | | `wtmec2yr` | 2-year MEC examination weight (blood pressure, lab tests) | `weights` | | `wtint2yr` | 2-year interview weight (income, education, etc.) | `weights` | ```{r nhanes} # Subset to MEC exam participants (ridstatr == 2) before using wtmec2yr. # The 550 interview-only participants have wtmec2yr = 0 and are not part # of the exam sample. nhanes_exam <- nhanes_2017[nhanes_2017$ridstatr == 2, ] svy_nhanes <- as_survey( nhanes_exam, ids = sdmvpsu, strata = sdmvstra, weights = wtmec2yr, nest = TRUE # PSU IDs are locally unique within strata ) svy_nhanes ``` For interview-only variables (income, education), use the full dataset with `wtint2yr` — all 9,254 participants have a positive interview weight: ```{r nhanes-interview} svy_nhanes_int <- as_survey( nhanes_2017, ids = sdmvpsu, strata = sdmvstra, weights = wtint2yr, nest = TRUE ) ``` ### 2.6 Worked example: ANES 2024 The 2024 American National Election Studies uses a stratified cluster design with separate pre- and post-election weights. Use the correct weight for the variables you are analyzing: | Variable | Role | Argument | |------------|---------------------------------------------------------------|-----------| | `v240103c` | PSU (FTF+Web combined) — cluster ID | `ids` | | `v240103d` | Stratum (FTF+Web combined) | `strata` | | `v240103a` | Pre-election weight — use for pre-election variables | `weights` | | `v240103b` | Post-election weight — use for validated vote choice | `weights` | ```{r anes} # Pre-election analysis (party ID, ideology, candidate preference) svy_anes_pre <- as_survey( anes_2024, ids = v240103c, strata = v240103d, weights = v240103a ) # Post-election analysis (validated vote choice: v242066, v242067) svy_anes_post <- as_survey( anes_2024, ids = v240103c, strata = v240103d, weights = v240103b ) ``` **Missing values:** ANES uses negative integer codes throughout — `−9` = Refused, `−8` = Don't know, `−1` = Inapplicable. Recode these to `NA` before analysis. Check `attr(anes_2024$v241177, "labels")` for the full set of codes for any variable. ### 2.7 Worked example: GSS 2024 The General Social Survey uses a stratified multi-stage cluster design. Two weights are available depending on whether non-response bias is a concern: | Variable | Role | Argument | |-------------|--------------------------------------------------------------------|-----------| | `vpsu` | Variance primary sampling unit | `ids` | | `vstrat` | Variance stratum | `strata` | | `wtssps` | Person post-stratification weight — standard analysis weight | `weights` | | `wtssnrps` | Person post-stratification weight, non-response adjusted | `weights` | ```{r gss} # Standard analysis weight svy_gss <- as_survey( gss_2024, ids = vpsu, strata = vstrat, weights = wtssps ) # Non-response adjusted weight (preferred when non-response bias is a concern) svy_gss_nr <- as_survey( gss_2024, ids = vpsu, strata = vstrat, weights = wtssnrps ) ``` **Missing values:** GSS uses `−100` = Inapplicable, `−99` = No answer, `−98` = Don't know, `−90` = Refused. These are stored as value labels on every column — check `attr(gss_2024$happy, "labels")` and recode to `NA` before analysis. ### 2.8 Worked example: Pew NPORS 2025 The 2025 National Public Opinion Reference Survey is an **address-based sample (ABS)** — units are drawn directly from the USPS Computerized Delivery Sequence file with no intermediate cluster stage. Each address is its own sampling unit, so there is no PSU variable. Omit `ids`: | Variable | Role | Argument | |-----------|-------------------------------------------------------------------|-----------| | `stratum` | Sampling stratum (10 levels, defined by census block group) | `strata` | | `weight` | Final raked weight — base weight calibrated to Census targets | `weights` | ```{r npors} svy_npors <- as_survey( pew_npors_2025, strata = stratum, weights = weight ) ``` --- ## 3. `as_survey_replicate()` — Replicate Weight Designs {#sec-rep} Use `as_survey_replicate()` when your data provider has supplied pre-computed replicate weight columns — columns like `repwt_1`, `repwt_2`, ..., or `pwgtp1`–`pwgtp80`. Replicate-based variance estimation works by repeatedly re-estimating the target statistic under small perturbations of the sample, embedding variance information directly in the weights [@wolter2007, ch. 1]. **Use the agency-supplied replicate weights when they are available.** Survey agencies tune these weights for their specific design. Using them correctly replicates published point estimates and standard errors and is generally considered the preferred approach for variance estimation with major public surveys [@lohr2022, §9.4]. ### 3.1 The `type` argument The `type` argument specifies which replication variance formula applies. Getting this wrong produces systematically incorrect standard errors. Identify the correct type from your codebook's technical documentation. | Type | Full name | Identifying signs in codebook | Common surveys | |-------------------------|------------------------------|---------------------------------------------------|------------------------------------------| | `"JK1"` | Jackknife-1 | "JK1"; one PSU dropped per replicate | NHES, some Pew studies | | `"JK2"` | Jackknife-2 | "JK2"; paired PSUs; exactly 2 PSUs per stratum | NAEP, PISA, most NCES surveys | | `"JKn"` | Jackknife-n | One stratum dropped per replicate | Less common; some multi-PSU designs | | `"BRR"` | Balanced Repeated Replication| "BRR"; exactly 2 PSUs per stratum required | Some CPS variants | | `"Fay"` | Fay's Modified BRR | "Fay BRR" or "Fay's method"; BRR with epsilon | Some Census Bureau surveys [@fay1989; @judkins1990] | | `"bootstrap"` | Bootstrap | "bootstrap replication weights"; 100–500 replicates | BRFSS | | `"successive-difference"` | Successive Difference | "SDR" or "successive difference replication" | ACS 1-year PUMS [@census2022] | | `"ACS"` | ACS variant | Specific to ACS 5-year methodology | ACS 5-year PUMS | The Fay epsilon parameter (`fay_rho`) controls how much each replicate weight differs from the full-sample weight. Its value is specified in the survey's technical documentation [@fay1989; @judkins1990]. ### 3.2 Worked example: ACS PUMS 2022 — Wyoming The ACS 1-year PUMS provides 80 successive-difference replicate weights for variance estimation, documented in the ACS Design and Methodology report [@census2022]: | Variable | Role | Argument | |----------------------|---------------------------------------------------------|---------------| | `pwgtp` | Person weight | `weights` | | `pwgtp1`–`pwgtp80` | Successive-difference replicate weights (80 replicates) | `repweights` | ```{r acs} svy_acs <- as_survey_replicate( acs_pums_wy, weights = pwgtp, repweights = pwgtp1:pwgtp80, type = "successive-difference" ) svy_acs ``` ### 3.3 Worked example: Pew Jewish Americans 2020 This Pew study provides 100 jackknife-1 replicate weights alongside the full-sample weight: | Variable | Role | Argument | |--------------------------------|-----------------------------------------------|--------------| | `extweight` | Full-sample base weight | `weights` | | `extweight1`–`extweight100` | JK1 jackknife replicate weights (100 replicates) | `repweights` | ```{r pew-jewish} svy_jewish <- as_survey_replicate( pew_jewish_2020, weights = extweight, repweights = extweight1:extweight100, type = "JK1" ) svy_jewish ``` ### 3.4 The `scale` and `rscales` arguments Most users can omit `scale` and `rscales`. surveycore computes defaults based on `type` and the number of replicates. Override them only when your codebook's technical documentation specifies custom values [@wolter2007, ch. 3]. --- ## 4. `as_survey_twophase()` — Two-Phase Designs {#sec-twophase} > **If you are not sure whether your design is two-phase, it almost certainly > is not.** Skip to [Section 5](#sec-srs) or [Section 6](#sec-nps). ### 4.1 What two-phase sampling is Two-phase (or double-sampling) designs collect data in two stages [@lumley2010, ch. 9]: 1. **Phase 1:** A large, inexpensive sample that records basic variables (demographics, a screening question, administrative records). 2. **Phase 2:** A subsample drawn from Phase 1 that collects expensive or difficult measurements — lab tests, in-person interviews, expert coding. The variance estimator accounts for uncertainty from both sampling stages [@saegusa2013]. You must have retained the Phase 1 data and know which Phase 1 units were selected into Phase 2. Common contexts: case-cohort studies, medical validation studies, surveys with a screening phase [@breslow1988]. ### 4.2 Arguments | Argument | What it does | |-----------------------------------|--------------------------------------------------------------------------| | `phase1` | A `survey_taylor` object representing the Phase 1 design | | `subset` | Bare name of a logical column: `TRUE` = selected into Phase 2 | | `ids2`, `strata2`, `probs2`, `fpc2` | Phase 2 design variables (all optional) | | `method` | `"full"` (default), `"approx"`, or `"simple"` | The `method` argument: - `"full"`: Correct variance accounting for both phases. Requires Phase 1 cluster information. - `"approx"`: Faster approximation; adequate when the Phase 1 sampling fraction is small. - `"simple"`: Ignores the Phase 1 design. Use only if Phase 1 is a census. ### 4.3 Worked example: National Wilms Tumor Study The `nwtco` dataset from the `survival` package records outcomes for 4,028 children enrolled in the National Wilms Tumor Study — a multi-institution clinical trial. This is a case-cohort design: a random subcohort was selected from all enrolled children (Phase 1), and expensive central-laboratory histology was measured only for subcohort members plus all relapse cases [@breslow1988]. ```{r nwtco, eval=requireNamespace("survival", quietly=TRUE)} nwtco <- survival::nwtco # in.subcohort is stored as 0/1 — must be logical for as_survey_twophase() nwtco$in.subcohort <- as.logical(nwtco$in.subcohort) # Phase 1: all 4,028 enrolled patients (each patient is their own unit) phase1 <- as_survey(nwtco, ids = seqno) # Phase 2: subcohort, with Phase 2 sampling stratified by relapse status svy_twophase <- as_survey_twophase( phase1, strata2 = rel, # Phase 2 strata: cases (rel=1) vs. non-cases (rel=0) subset = in.subcohort, # Logical column: TRUE = selected into Phase 2 method = "full" ) svy_twophase ``` --- ## 5. Simple Random Sample with `as_survey()` {#sec-srs} Use `as_survey()` without `ids` or `strata` when every unit in your target population had an equal, known probability of selection — no clustering, no stratification [@cochran1977, ch. 2; @lohr2022, ch. 2]. This design is common in: - Surveys of a complete organizational roster (all employees at a company, all students at a school) where units are drawn directly from a list - Small-scale research with a well-defined, numbered sampling frame - Pilot studies and classroom experiments When neither `ids` nor `strata` is specified, `as_survey()` creates a `survey_taylor` object with no cluster or stratum structure — the SRS special case of the Taylor series estimator. ### 5.1 The `fpc` argument matters more here Without clustering or stratification, the FPC has a proportionally larger effect on variance estimates than in complex designs [@cochran1977, §2.8]. Supply it when you know the population size or sampling fraction. For the example below, the population is N = 400 schools. ### 5.2 Worked example: School district survey A district administrator draws a simple random sample of 80 schools from a complete roster of 400 schools. Every school has an equal probability of selection (80/400 = 0.20) — the textbook SRS case [@cochran1977, ch. 2; @lohr2022, ch. 2]: ```{r apisrs} set.seed(101) N <- 400 # total schools in district n <- 80 # schools sampled school_survey <- data.frame( school_id = sample(seq_len(N), n), avg_score = round(rnorm(n, mean = 72, sd = 11), 1), pct_frpl = round(runif(n, 0.10, 0.85), 2), # % free/reduced price lunch enrollment = round(runif(n, 180, 850)), sw = N / n, # equal sampling weight = 400/80 = 5.0 fpc = N # population size for FPC ) svy_srs <- as_survey( school_survey, weights = sw, # each sampled school represents 5 schools in the population fpc = fpc # reduces SEs: we sampled 20% of the population ) svy_srs ``` Two things worth making explicit so this example is not misread: **The unit of analysis is the school, not the student.** Variables like `avg_score`, `pct_frpl`, and `enrollment` are school-level aggregates drawn from administrative records for each sampled school. This is a survey *of schools*. If you wanted individual student-level data from each selected school, you would need a two-stage cluster design — sample schools, then sample students within each school — and use `as_survey()` with `ids = school_id` to account for the clustering. **The weight is constant because this is SRS.** Each school was selected with probability 80/400 = 0.20, so each receives weight 1/0.20 = 5.0. The weight is the same for every school because no school was oversampled or undersampled relative to any other. Uniform weights are not a simplification — they are the defining signature of simple random sampling. --- ## 6. `as_survey_nonprob()` — Non-Probability Samples {#sec-nps} If you conduct surveys with non-probability samples, like with opt-in panels such as Cint, Dynata, Qualtrics panels, Prolific, or others, then this section is for you. This section assumes you have, at a minimum, either calibration (raking) weights or inverse-probability weights (IPW) via propensity scoring, but ideally you will also have replicate weights based on the calibration weights or IPW you already have. I'll go into more detail on why you want to have replicate weights in §6.2. ### 6.1 The fundamental distinction A **probability sample** gives every unit in the target population a known, positive inclusion probability. Design-based variance estimators are valid because the randomness that justifies them comes from the sampling mechanism itself [@cochran1977, ch. 1; @lohr2022, ch. 1]. A **non-probability sample**, like an opt-in online panel, has unknown inclusion probabilities. The decision to join a panel and to complete a particular survey is self-selected. No mechanical property of the data guarantees representativeness [@baker2013; @elliott2017]. As a result, there is additional uncertainty associated with non-probability samples that is not fully captured by traditional design-based variance estimation. ### 6.2 Variance estimation: two modes {#sec-nonprob-variance} `as_survey_nonprob()` has two variance estimation modes: **SRS approximation** (`repweights = NULL`, the default): If replicate weights are not supplied to `repweights` (i.e., `repweights = NULL`), then the standard errors treat the calibrated weights as fixed and apply a simple random sampling formula. This is convenient but underestimates the standard errors as the calibration step itself introduces additional uncertainty that the SRS formula does not capture [@elliott2017; @kolenikov2014]. **Bootstrap or jackknife replicate variance** (`repweights` supplied): If replicate weights are supplied in the `repweights` argument, the replicate weight variance estimation is used. This approach properly includes the uncertainty that comes from the calibration into the variance estimate and is the recommended method when replicate weights are available [@elliott2017; @kolenikov2014; @chrostowski2025]. Neither mode resolves the fundamental limitation common to all non-probability samples: standard errors cannot capture uncertainty from the unknown selection mechanism itself [@baker2013; @elliott2017]. #### Choosing `type` When `repweights` is supplied, the `type` argument selects the replicate scheme. Four types are supported for `survey_nonprob` objects: | `type` | Description | Default `scale` | When to use | |--------|-------------|-----------------|-------------| | `"bootstrap"` | Bootstrap resampling | `1/R` | Most vendor-supplied replicates | | `"JK1"` | Delete-one jackknife (`"jackknife"` is an alias) | `(R-1)/R` | Some research panels | | `"JK2"` | Stratified jackknife; requires explicit `rscales` | `1` | Clustered nonprob designs | | `"JKn"` | Equivalent to JK2 | `1` | Same as JK2 | ### 6.3 What you can and cannot claim | Claim | Valid? | Notes | |-----------------------------------------------------------------------------|-----------------|----------------------------------------------------------------------------------------------| | Point estimates representative of calibration margins | ✅ Yes | Calibrated to age, gender, education, etc. targets | | Estimates more accurate than unweighted | ✅ Usually | Especially for outcomes correlated with demographic variables | | Standard errors (SRS approx., no `repweights`) | ⚠️ Understated | Treats calibrated weights as fixed; calibration variance not propagated | | Standard errors (with `repweights`, calibration re-applied per replicate) | ⚠️ Approximately | Captures calibration uncertainty; cannot address selection mechanism uncertainty | | Results equivalent to a probability-sample estimate | ❌ No | Selection mechanism is unknown and cannot be fully corrected | This is the standard practice across the industry — used routinely by academic researchers, major survey organizations, and commercial firms [@baker2013; @mcphee2023]. The key is transparency: **your methods section should state that you used a non-probability sample, detail how the weights were created, and acknowledge that standard errors are approximate.** ### 6.4 Worked example: Democracy Fund + UCLA Nationscape (SRS approximation) {#sec-nonprob-nationscape} The Nationscape is a large-scale non-probability survey conducted by Democracy Fund + UCLA, fielded weekly from July 2019 through January 2021. Each wave used quota sampling to ensure that the data was representative and consisted of approximately 6,250 respondents from Lucid (now known as Cint). The data also contains weights created by raking the data to the American Community Survey across age, gender, education, race/ethnicity, region, and 2016 presidential vote choice. The variable name for these weights is `weight`. ```{r nationscape} svy_ns <- as_survey_nonprob(ns_wave1, weights = weight) svy_ns # Presidential approval rating (July 2019) get_freqs(svy_ns, pres_approval) ``` This produces a `survey_nonprob` object using SRS-approximation variance because no replicate weights were supplied. As a result, the standard errors will be too small as they do not take into account the additional variance introduced by the raking procedure. ### 6.5 Worked example: Bootstrap replicate weights {#sec-nonprob-repweights} When your data provider supplies bootstrap replicate weight columns — each representing one round of calibration re-applied to a bootstrap resample of the respondents — use `repweights` with `type = "bootstrap"`. The resulting variance estimates properly account for calibration uncertainty rather than treating the weights as fixed [@kolenikov2014; @chrostowski2025]. The example below uses synthetic data to illustrate the interface. In practice the replicate weight columns come from your vendor alongside the main calibration weight column. ```{r nonprob-bootstrap} set.seed(1) n <- 200 R <- 50 ns_synthetic <- data.frame( pres_approval = sample(c("Approve", "Disapprove", "DK"), n, replace = TRUE), age_grp = sample(c("18-34", "35-54", "55+"), n, replace = TRUE), weight = runif(n, 0.5, 2.5) ) # Replicate columns: calibration re-applied on each bootstrap draw. # In practice these come from your vendor alongside the main weight column. rep_mat <- matrix(runif(n * R, 0.3, 3.0), nrow = n) colnames(rep_mat) <- paste0("repwt_", seq_len(R)) ns_synthetic <- cbind(ns_synthetic, as.data.frame(rep_mat)) svy_np_boot <- as_survey_nonprob( ns_synthetic, weights = weight, repweights = starts_with("repwt_"), type = "bootstrap" ) svy_np_boot ``` Use `svy_np_boot` with `get_means()`, `get_freqs()`, and other estimation functions exactly as you would a plain `survey_nonprob` object — the variance estimator switches from SRS approximation to bootstrap automatically. ### 6.6 Worked example: University snowball sample A university sends an email to 100 students inviting them to take part in a survey. At the end of the survey, they encourage the students to share the survey with other students. In the end they end up recruiting 500 respondents. This is known as a snowball sample and is another example of non-probability sampling. If the sample was weighted to represent the broader student body population, then weights should be applied as shown below: ```r svy_campus <- as_survey_nonprob(campus_survey, weights = weight) ``` --- ## 7. When no constructor applies: convenience and purposive samples {#sec-no-constructor} Not every data collection fits the survey design framework. ### 7.1 Example: program evaluation classrooms A researcher surveys students in five classrooms that volunteered to participate in a new educational program and wants to assess whether the program changed their attitudes. The classrooms were not randomly selected from any defined population. There is no sampling mechanism to justify a design-based variance estimator, and no calibration weights that would correct for the non-random selection. The inferential question — whether the program *caused* attitude change — is a **causal inference** problem requiring a control group and appropriate methods (difference-in-differences, matching, regression discontinuity), not a survey design object. If the goal is purely **descriptive** — summarizing the attitudes of students in these specific classrooms without generalizing — you can treat the participants as a census. Add a column of 1s and use `as_survey()` without `ids` or `strata`: ```r classroom_data$wt <- 1 svy_participants <- as_survey(classroom_data, weights = wt) ``` Equal weights treat all participants as equally represented. The SEs reflect variation *among participants*. Do not interpret results as representative of all students at the school. ### 7.2 General decision rule | Design | Appropriate tool | Notes | |---|---|---| | Probability sample with design weights | `as_survey()`, `as_survey_replicate()` | Exact variance | | Pure SRS — equal probability, no clustering/strata | `as_survey()` (no `ids` or `strata`) | Exact variance; SRS special case of Taylor | | Any non-probability sample with weights | `as_survey_nonprob()` | Approximate variance | | Voluntary response or convenience sample, no weights | `as_survey()` with `weights = 1` (no `ids`/`strata`) | Conditional inference only; disclose | When you use `as_survey()` with equal weights and no `ids` or `strata` for a non-probability sample, surveycore produces estimates and SEs without error. The SEs are valid as a measure of variability *among the observed participants*. They should not be interpreted as uncertainty about a broader population unless the sample can be independently defended as representative. --- ## 8. Reference: Common Codebook Variables {#sec-reference} A lookup table for common codebook terms and how they map to constructor arguments: | Codebook term | Maps to | Notes | |------------------------------------------------------------------|------------------------------------------------------|-------------------------------------------| | "sampling weight", "survey weight", "person weight" | `weights =` | | | "PSU", "primary sampling unit", "cluster ID" | `ids =` | | | "stratum", "design stratum", "sampling stratum" | `strata =` | | | "FPC", "finite population correction", "population size" | `fpc =` | | | "replicate weights", "bootstrap weights", "BRR weights" | `repweights =` | Use `as_survey_replicate()` | | "base weight", "design weight" (with separate replicates) | `weights =` in `as_survey_replicate()` | | | "Fay coefficient", "Fay factor", "epsilon" | `fay_rho =` | With `type = "Fay"` | | "raking weights", "post-stratification weights", "cal weights" | `weights =` in `as_survey_nonprob()` | Non-probability design | | "two-phase", "double sampling", "case-cohort" | Phase 1 → `as_survey()`, then `as_survey_twophase()` | | --- ## References