↓ Skip to main content

Application and interpretation of multiple statistical tests to evaluate validity of dietary intake assessment methods

Overview of attention for article published in Nutrition Journal, April 2015
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
85 Dimensions

Readers on

mendeley
165 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Application and interpretation of multiple statistical tests to evaluate validity of dietary intake assessment methods
Published in
Nutrition Journal, April 2015
DOI 10.1186/s12937-015-0027-y
Pubmed ID
Authors

Martani J Lombard, Nelia P Steyn, Karen E Charlton, Marjanne Senekal

Abstract

Several statistical tests are currently applied to evaluate validity of dietary intake assessment methods. However, they provide information on different facets of validity. There is also no consensus on types and combinations of tests that should be applied to reflect acceptable validity for intakes. We aimed to 1) conduct a review to identify the tests and interpretation criteria used where dietary assessment methods was validated against a reference method and 2) illustrate the value of and challenges that arise in interpretation of outcomes of multiple statistical tests in assessment of validity using a test data set. An in-depth literature review was undertaken to identify the range of statistical tests used in the validation of quantitative food frequency questionnaires (QFFQs). Four databases were accessed to search for statistical methods and interpretation criteria used in papers focusing on relative validity. The identified tests and interpretation criteria were applied to a data set obtained using a QFFQ and four repeated 24-hour recalls from 47 adults (18-65 years) residing in rural Eastern Cape, South Africa. 102 studies were screened and 60 were included. Six statistical tests were identified; five with one set of interpretation criteria and one with two sets of criteria, resulting in seven possible validity interpretation outcomes. Twenty-one different combinations of these tests were identified, with the majority including three or less tests. Coefficient of correlation was the most commonly used (as a single test or in combination with one or more tests). Results of our application and interpretation of multiple statistical tests to assess validity of energy, macronutrients and selected micronutrients estimates illustrate that for most of the nutrients considered, some outcomes support validity, while others do not. One to three statistical tests may not be sufficient to provide comprehensive insights into various facets of validity. Results of our application and interpretation of multiple statistical tests support the value of such an approach in gaining comprehensive insights in different facets of validity. These insights should be considered in the formulation of conclusions regarding validity to answer a particular dietary intake related research question.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 165 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 32 19%
Student > Master 29 18%
Student > Ph. D. Student 23 14%
Researcher 17 10%
Student > Postgraduate 9 5%
Other 30 18%
Unknown 25 15%
Readers by discipline Count As %
Nursing and Health Professions 37 22%
Medicine and Dentistry 35 21%
Agricultural and Biological Sciences 14 8%
Social Sciences 9 5%
Neuroscience 6 4%
Other 29 18%
Unknown 35 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 March 2016.
All research outputs
#12,405,644
of 14,021,570 outputs
Outputs from Nutrition Journal
#1,058
of 1,106 outputs
Outputs of similar age
#198,868
of 240,403 outputs
Outputs of similar age from Nutrition Journal
#1
of 1 outputs
Altmetric has tracked 14,021,570 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,106 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 29.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 240,403 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them