insufficient-effort 1.4.2

Python library for detecting Insufficient Effort Responding (IER) in survey data

IER

A Python package for detecting Insufficient Effort Responding (IER) in survey data using various statistical indices and methods.

Overview

When taking online surveys, participants sometimes respond to items without regard to their content. These types of responses, referred to as insufficient effort responding (IER) or careless responding, constitute significant problems for data quality, leading to distortions in data analysis and hypothesis testing.

The ier package provides solutions designed to detect such insufficient effort responses by allowing easy calculation of indices proposed in the literature. For a comprehensive review of these methods, see Curran (2016).

Features

• Multiple Detection Methods: Supports 20+ indices for detecting careless responding
• Flexible Input: Works with lists, numpy arrays, pandas DataFrames, and polars DataFrames
• Robust Implementation: Handles missing data and edge cases
• Type Hints: Full type annotations for IDE support

Installation

From PyPI

pip install insufficient-effort

From Source

git clone https://github.com/Cameron-Lyons/ier.git
cd ier
pip install -e .

Optional Dependencies

For enhanced functionality (e.g., chi-squared outlier detection):

pip install insufficient-effort[full]

Quick Start

import numpy as np
from ier import irv, mahad, longstring, evenodd, psychsyn

# Sample survey data (rows = participants, columns = items)
data = np.array([
    [1, 2, 3, 4, 5, 6, 7, 8],  # Normal responding
    [3, 3, 3, 3, 3, 3, 3, 3],  # Straightlining
    [1, 5, 1, 5, 1, 5, 1, 5],  # Alternating pattern
])

# Intra-individual response variability (low = straightlining)
print("IRV:", irv(data))

# Mahalanobis distance (high = outlier)
print("Mahad:", mahad(data))

# Longest string of identical responses
print("Longstring:", longstring(data))

Available Functions

Consistency Indices

evenodd(x, factors, diag=False)

Computes even-odd consistency by correlating responses to even vs odd items within each factor.

from ier import evenodd

data = [[1, 2, 3, 4, 5, 6], [2, 3, 4, 5, 6, 7]]
factors = [3, 3]  # Two factors with 3 items each
scores = evenodd(data, factors)

psychsyn(x, critval=0.60, anto=False, diag=False)

Identifies highly correlated item pairs and computes within-person correlations.

from ier import psychsyn, psychant

data = [[1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6]]
scores = psychsyn(data, critval=0.5)  # Synonyms
scores = psychant(data, critval=-0.5)  # Antonyms

individual_reliability(x, n_splits=100, random_seed=None)

Estimates response consistency using repeated split-half correlations.

from ier import individual_reliability, individual_reliability_flag

data = [[1, 2, 1, 2, 1, 2], [1, 5, 2, 4, 3, 3]]
reliability = individual_reliability(data, n_splits=50)
flags = individual_reliability_flag(data, threshold=0.3)

person_total(x, na_rm=True)

Correlates each person's responses with the sample mean response pattern.

from ier import person_total

data = [[1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [1, 2, 3, 4, 5]]
scores = person_total(data)  # [1.0, -1.0, 1.0]

semantic_syn(x, item_pairs, anto=False) / semantic_ant(x, item_pairs)

Computes consistency for predefined semantic synonym/antonym pairs.

from ier import semantic_syn, semantic_ant

data = [[1, 1, 5, 5], [1, 2, 5, 4]]
pairs = [(0, 1), (2, 3)]  # Predefined synonym pairs
scores = semantic_syn(data, pairs)

guttman(x, na_rm=True, normalize=True)

Counts response reversals relative to item difficulty ordering.

from ier import guttman, guttman_flag

data = [[1, 2, 3, 4, 5], [5, 4, 3, 2, 1]]
errors = guttman(data)
flags = guttman_flag(data, threshold=0.5)

mad(x, positive_items, negative_items, scale_max=None)

Mean Absolute Difference between positively and negatively worded items. High MAD indicates careless responding (not attending to item direction).

from ier import mad, mad_flag

# Columns 0,2 are positively worded; columns 1,3 are negatively worded
data = [
    [5, 1, 5, 1],  # Attentive: high on pos, low on neg
    [5, 5, 5, 5],  # Careless: ignores item direction
]
scores = mad(data, positive_items=[0, 2], negative_items=[1, 3], scale_max=5)
scores, flags = mad_flag(data, positive_items=[0, 2], negative_items=[1, 3])

Response Pattern Indices

longstring(x, avg=False)

Computes the longest (or average) run of identical consecutive responses.

from ier import longstring

# Single string
longstring("AAABBBCCDAA")  # ('A', 3)

# Matrix of responses
data = [[1, 1, 1, 2, 3], [1, 2, 3, 4, 5]]
longstring(data)  # [('1', 3), ('1', 1)]
longstring(data, avg=True)  # [1.67, 1.0]

irv(x, na_rm=True, split=False, num_split=1)

Computes intra-individual response variability (standard deviation).

from ier import irv

data = [[1, 2, 3, 4, 5], [3, 3, 3, 3, 3]]
scores = irv(data)  # High for varied, low for straightlining

# Split-half IRV
scores = irv(data, split=True, num_split=2)

u3_poly(x, scale_min=None, scale_max=None)

Proportion of extreme responses (at scale endpoints).

from ier import u3_poly

data = [[1, 5, 1, 5, 3], [3, 3, 3, 3, 3]]
extreme = u3_poly(data, scale_min=1, scale_max=5)

midpoint_responding(x, scale_min=None, scale_max=None, tolerance=0.0)

Proportion of midpoint responses.

from ier import midpoint_responding

data = [[1, 2, 3, 4, 5], [3, 3, 3, 3, 3]]
mid = midpoint_responding(data, scale_min=1, scale_max=5)  # [0.2, 1.0]

response_pattern(x, scale_min=None, scale_max=None)

Returns multiple response style indices at once.

from ier import response_pattern

patterns = response_pattern(data, scale_min=1, scale_max=5)
# Returns dict with: extreme, midpoint, acquiescence, variability

Statistical Outlier Detection

mahad(x, flag=False, confidence=0.95, na_rm=False, method='chi2')

Computes Mahalanobis distance for multivariate outlier detection.

from ier import mahad, mahad_summary

data = [[1, 2, 3], [2, 3, 4], [3, 4, 5], [10, 10, 10]]
distances = mahad(data)
distances, flags = mahad(data, flag=True, confidence=0.95)

# Methods: 'chi2', 'iqr', 'zscore'
distances, flags = mahad(data, flag=True, method='iqr')

lz(x, difficulty=None, discrimination=None, theta=None, model='2pl')

Standardized log-likelihood (lz) person-fit statistic based on Item Response Theory. Negative values indicate aberrant response patterns.

from ier import lz, lz_flag

# Binary response data (0/1)
data = [
    [1, 1, 1, 0, 0, 0],  # Normal pattern
    [0, 0, 0, 1, 1, 1],  # Aberrant pattern (fails easy, passes hard)
]
scores = lz(data)  # Negative = suspicious
scores, flags = lz_flag(data, threshold=-1.96)

# Use 1PL (Rasch) model
scores = lz(data, model='1pl')

# Provide custom item parameters
scores = lz(data, difficulty=[-1, -0.5, 0, 0.5, 1, 1.5])

Response Time Indices

response_time(times, metric='median')

Computes response time statistics per person.

from ier import response_time, response_time_flag, response_time_consistency

times = [[2.1, 3.4, 2.8], [0.5, 0.4, 0.6], [2.5, 2.3, 2.7]]

avg_times = response_time(times, metric='mean')
med_times = response_time(times, metric='median')
min_times = response_time(times, metric='min')

# Flag fast responders
flags = response_time_flag(times, threshold=1.0)

# Coefficient of variation (low = suspiciously uniform)
cv = response_time_consistency(times)

Composite Index

composite(x, indices=None, method='mean', standardize=True)

Combines multiple IER indices into a single composite score. Higher scores indicate greater likelihood of careless responding.

from ier import composite, composite_flag, composite_summary

data = [
    [1, 2, 3, 4, 5, 4, 3, 2, 1, 2],  # Normal
    [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],  # Straightliner
]

# Default: combines IRV, longstring, Mahalanobis, psychsyn, person-total
scores = composite(data)

# Select specific indices
scores = composite(data, indices=['irv', 'longstring', 'mahad'])

# Different combination methods
scores = composite(data, method='sum')   # Sum of z-scores
scores = composite(data, method='max')   # Maximum z-score

# Flag careless responders
scores, flags = composite_flag(data, threshold=1.5)
scores, flags = composite_flag(data, percentile=95.0)

# Detailed summary with individual index scores
summary = composite_summary(data)
print(summary['indices_used'])  # ['irv', 'longstring', 'mahad', ...]
print(summary['indices'])       # Dict of individual index scores

Working with DataFrames

The package works with pandas and polars DataFrames:

import pandas as pd
import polars as pl
from ier import irv

# Pandas
df_pandas = pd.DataFrame([[1, 2, 3], [4, 5, 6]])
scores = irv(df_pandas)

# Polars
df_polars = pl.DataFrame([[1, 2, 3], [4, 5, 6]])
scores = irv(df_polars)

Handling Missing Data

Most functions handle NaN values appropriately:

import numpy as np
from ier import irv, mahad

data = np.array([
    [1, 2, np.nan, 4],
    [np.nan, 2, 3, 4],
    [1, 2, 3, 4]
])

irv_scores = irv(data, na_rm=True)
mahad_scores = mahad(data, na_rm=True)

Contributing

Contributions are welcome! Please open an issue first to discuss changes.

License

MIT License - see LICENSE for details.

Citation

@software{ier2026,
  title={IER: Python package for detecting Insufficient Effort Responding},
  author={Lyons, Cameron},
  year={2026},
  url={https://github.com/Cameron-Lyons/ier}
}

References

• Curran, P. G. (2016). Methods for the detection of carelessly invalid responses in survey data. Journal of Experimental Social Psychology, 66, 4-19.
• Dunn, A. M., Heggestad, E. D., Shanock, L. R., & Theilgard, N. (2018). Intra-individual response variability as an indicator of insufficient effort responding. Journal of Business and Psychology, 33(1), 105-121.