Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 297

The DOI matches its OpenAlex record; 3 of the 3 references it cites were found in a registry or an index.

Does PLS have advantages for small sample size or non-normal data?

Dale L. Goodhue, William W. Lewis, Ronald L. Thompson·2012·MIS Quarterly
DOI 10.5555/2481655.2481672
Open this paper in CoChat — free account

Abstract

There is a pervasive belief in the MIS research community that PLS has advantages over other techniques when analyzing small sample sizes or data with non-normal distributions. Based on these beliefs, major MIS journals have published studies using PLS with sample sizes that would be deemed unacceptably small if used with other statistical techniques. We used Monte Carlo simulation more extensively than previous research to evaluate PLS, multiple regression, and LISREL in terms of accuracy and statistical power under varying conditions of sample size, normality of the data, number of indicators per construct, reliability of the indicators, and complexity of the research model. We found that PLS performed as effectively as the other techniques in detecting actual paths, and not falsely detecting non-existent paths. However, because PLS (like regression) apparently does not compensate for measurement error, PLS and regression were consistently less accurate than LISREL. When used with small sample sizes, PLS, like the other techniques, suffers from increased standard deviations, decreased statistical power,and reduced accuracy. All three techniques were remarkably robust against moderate departures from normality, and equally so. In total, we found that the similarities in results across the three techniques were much stronger than the differences.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

OpenAlex · 10.5555/2481655.2481672

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Cited by

Metadata

Authors
Goodhue, D. L., Lewis, W. W., & Thompson, R. L.
Venue
MIS Quarterly
Publisher
MIS Quarterly
Published
2012-09-01
Volume
36
Issue
3
Pages
981-1001
ISSN
0276-7783 (ISSN-L)
DOI
10.5555/2481655.2481672
OpenAlex
W2132743748
Cited by
297
References
3
FWCI
24.67
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.