Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A FeAsibility items Checklist for assessing implementation characTeristics of patient-reported Outcome measures in Research, Regulation and Routine clinical care (FACTOR3): Development and evaluation.

Clinical medicine (London, England)·2026
Same author

Practitioner perceptions of biodiversity criteria for solar suitability analyses in the United States.

npj biodiversity·2026
Same author

Impact of long COVID on diverse Australian populations: a multi-site, longitudinal prospective cohort study protocol.

BMJ open·2026
Same author

Health-related quality of life domains relevant to people in Europe undergoing cancer treatment: a systematic review of qualitative research.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation·2026
Same author

Evaluation of a novel communication workshop for healthcare professionals working with metastatic breast cancer patients.

Breast (Edinburgh, Scotland)·2026
Same author

Optimising a behavioural intervention to support endocrine therapy adherence for women with breast cancer: protocol for the ROSETA optimisation factorial randomised controlled trial.

Trials·2026
Same journal

Integrating health economics and implementation science: a call to action.

BMC medical research methodology·2026
Same journal

Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.

BMC medical research methodology·2026
Same journal

Sparse multi-way DMDC for longitudinal classification in high dimension low sample size data.

BMC medical research methodology·2026
Same journal

Tree-based exploratory identification of predictive biomarkers in non-randomized data.

BMC medical research methodology·2026
Same journal

Comparative evaluation of interrupted time series analytical methods for healthcare quality improvement research: a Monte Carlo simulation study.

BMC medical research methodology·2026
Same journal

Methodological advances in claims-based dementia algorithms: integrating medication and clinical data for medicare populations.

BMC medical research methodology·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Rasch fit statistics and sample size considerations for polytomous data.

Adam B Smith1, Robert Rush, Lesley J Fallowfield

  • 1Cancer Research UK - Clinical Centre, St, James's University Hospital, Leeds, UK. a.b.smith@leeds.ac.uk

BMC Medical Research Methodology
|May 31, 2008
PubMed
Summary
This summary is machine-generated.

For polytomous data, Rasch mean square fit statistics are reliable across different sample sizes, unlike t-statistics. This finding aids accurate psychometric analysis in health research.

More Related Videos

Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jul 4, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

Area of Science:

  • Psychometrics and Health Research
  • Item Response Theory

Background:

  • Rasch fit statistics (mean squares, t-statistics) assess item fit to latent traits.
  • Previous studies show sample size affects fit statistics for dichotomous data.
  • Little is known about this relationship for polytomous data.

Purpose of the Study:

  • Investigate the relationship between Rasch fit statistics and sample size for polytomous data.
  • Inform psychometric analysis in health research using Rasch models.

Main Methods:

  • Collated data from 4072 cancer patients (PHQ-9, HADS).
  • Drew 80 samples (10 per size) with replacement for 8 sample sizes (n=25 to 3200).
  • Applied Rating Scale and Partial Credit Models, deriving infit/outfit mean square and t-fit statistics.

Main Results:

  • T-statistics showed high sensitivity to sample size variations.
  • Mean square fit statistics remained stable across sample sizes for polytomous data.

Conclusions:

  • Mean square fit statistics are largely independent of sample size for polytomous data.
  • Published recommended ranges can reliably identify model misfit.
  • Supports robust application of Rasch models in health research.