• English
    • Norsk
  • English 
    • English
    • Norsk
  • Administration
View Item 
  •   Home
  • Øvrige samlinger
  • Høstingsarkiver
  • CRIStin høstingsarkiv
  • View Item
  •   Home
  • Øvrige samlinger
  • Høstingsarkiver
  • CRIStin høstingsarkiv
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Differentiating between mixed-effects and latent-curve approaches to growth modeling

McNeish, Daniel; Matta, Tyler
Journal article; AcceptedVersion; Peer reviewed
View/Open
Revised+Document.pdf (1.234Mb)
Year
2017
Permanent link
http://urn.nb.no/URN:NBN:no-66573

CRIStin
1529128

Metadata
Show metadata
Appears in the following Collection
  • CEMO Centre for Educational Measurement [136]
  • CRIStin høstingsarkiv [15004]
Original version
Behavior Research Methods. 2017, 1-17, DOI: http://dx.doi.org/10.3758/s13428-017-0976-5
Abstract
In psychology, mixed-effects models and latent-curve models are both widely used to explore growth over time. Despite this widespread popularity, some confusion remains regarding the overlap of these different approaches. Recent articles have shown that the two modeling frameworks are mathematically equivalent in many cases, which is often interpreted to mean that one’s choice of modeling framework is merely a matter of personal preference. However, some important differences in estimation and specification can lead to the models producing very different results when implemented in software. Thus, mathematical equivalence does not necessarily equate to practical equivalence in all cases. In this article, we discuss these two common approaches to growth modeling and highlight contexts in which the choice of the modeling framework (and, consequently, the software) can directly impact the model estimates, or in which certain analyses can be facilitated in one framework over the other. We show that, unless the data are pristine, with a large sample size, linear or polynomial growth, and no missing data, and unless the participants have the same number of measurements collected at the same set of time points, one framework is often more advantageous to adopt. We provide several empirical examples to illustrate these situations, as well as ample software code so that researchers can make informed decisions regarding which framework will be the most beneficial and most straightforward for their research interests.
 
Responsible for this website 
University of Oslo Library


Contact Us 
duo-hjelp@ub.uio.no


Privacy policy
 

 

For students / employeesSubmit master thesisAccess to restricted material

Browse

All of DUOCommunities & CollectionsBy Issue DateAuthorsTitlesThis CollectionBy Issue DateAuthorsTitles

For library staff

Login
RSS Feeds
 
Responsible for this website 
University of Oslo Library


Contact Us 
duo-hjelp@ub.uio.no


Privacy policy