PEIR-IDEAS — Patients Engaged in Research Survey Enhanced by Inclusion, Diversity, Equity, Acceptance, and Safety (PEIR-IDEAS) Scoring (Standalone Function)

Status: pre-CRAN • simple R script for scoring PEIRS

This repository provides a single R function, calculate_peirs_score(), to score the Patients Engaged in Research Scale (PEIRS) - IDEAS.
It returns per-respondent raw totals, items answered, a standardized 0–100 score, and a response status flag. A custom summary() method prints describe‑style statistics (n, mean, SD, min, quartiles, max).

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Quick Start (two ways)

A) Use directly from GitHub

# Load the function straight from this repo (no installation needed)
source("https://raw.githubusercontent.com/quantlover/PEIR-IDEAS/main/calculate_peirs_score.R")

# Example: score a data frame 'dat' with PEIRS items as columns
scores <- calculate_peirs_score(dat)
summary(scores)

B) Use locally

# Download calculate_peirs_score.R from this repo, then:
source("calculate_peirs_score.R")

scores <- calculate_peirs_score(dat)
summary(scores)

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Data Requirements

• One row = one respondent.
• Columns named with PEIRS item IDs (defaults):
  pr2, pr4, pr7, pr11, pr12, pr13, pr14, t1, t2, t3, t4, t5, t6, t7, su1, su2, su3, be1, be2, be3, cn1, cn2, cn3, cn4, fv1, fv2, fv3.
• Responses typically on Likert 1–5. Non-numeric columns are coerced to numeric; failures become NA.

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Minimal Reproducible Example

# Simulate toy data with the default 23 items
set.seed(123)
items <- c("pr2","pr4","pr7","pr11","pr12","pr13","pr14",
           "t1","t2","t3","t4","t5","t6","t7",
           "su1","su2","su3",
           "be1","be2","be3",
           "cn1","cn2","cn3","cn4",
           "fv1","fv2","fv3")

dat <- as.data.frame(matrix(sample(1:5, 50*length(items), replace=TRUE), nrow=50))
names(dat) <- items

# Load the scorer
# source("calculate_peirs_score.R")   # if local
# or source("https://raw.githubusercontent.com/quantlover/PEIR-IDEAS/main/calculate_peirs_score.R")

scores <- calculate_peirs_score(dat)
head(scores, 3)
summary(scores)

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Interpreting PEIRS_standardized (0–100)

• ≥ 70% → High engagement
• < 70% → Low engagement

Notes:

• The standardized score adjusts for the number of items answered; it is comparable across respondents when using the same item set.
• Use Response_Status to flag incomplete responses (default rule: < 10 items answered ⇒ “Too few items”).

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Common Options

# Allow partial item sets (compute with present items, warn about missing)
scores <- calculate_peirs_score(dat, strict = FALSE)

# Custom minimum answered threshold
scores <- calculate_peirs_score(dat, min_items_required = 12)

# Non-standard Likert range (e.g., 0–10 mapped to 0–4)
scores <- calculate_peirs_score(dat, scale_from = c(0,10), scale_to = c(0,4))

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License

Released under the MIT License. See the LICENSE file.

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Suggested Citations

• Abelson J, Li K, Wilson G, Shields K, Schneider C, Boesveld S. Supporting quality patient and public engagement in health system organizations: development and usability testing of the PEIRS tool. Health Expectations. 2016;19(4):817–827. https://doi.org/10.1111/hex.12378
• PEIR-IDEAS (pre-CRAN). “calculate_peirs_score(): Scoring the Patients Engaged in Research Scale (PEIRS) - IDEAS in R.” GitHub: quantlover/PEIR-IDEAS, 2025.