| 1. |
- Eerola, Mervi, et al.
(författare)
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Analysis of Life History Calendar Data
- 2018
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Ingår i: Wiley StatsRef: Statistics Reference Online. - : John Wiley & Sons. - 9781118445112 ; , s. 1-8
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The life history calendar (LHC) is a data‐collection tool for obtaining reliable retrospective data on several life domains. LHC data can be analyzed either with probabilistic modeling of transitions between the life states or with sequence analysis, a data‐mining method that requires minimal simplification of the original data. The life events define the multistate model and its event‐specific hazards and the parallel life domains in multidimensional sequence analysis. These two approaches complement each other, and recently also several ways to combine them have been suggested.
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| 2. |
- Ivarsson, Andreas, 1984-, et al.
(författare)
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Prediction of injury risk in sports
- 2019
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Ingår i: Wiley StatsRef. - : John Wiley & Sons. - 9781118445112
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Bokkapitel (refereegranskat)abstract
- Sport injuries are a major problem associated with sport participation. To develop preventive strategies and programs, it is important to identify factors that will increase the likelihood of sport injuries. In most sport injury risk factor research, statistical analyses are performed; however, many of the most common statistical analyses provide limited information about predictors of sport injury risk. The common analyses used in previous studies do not acknowledge the complexity associated with investigating risk factors for sport injuries. To better capture this complexity, suggested in most theoretical frameworks, more appropriate of statistical approaches should be used. In this article we present how latent profile analysis, latent change score analysis, and latent growth curve analysis can be used to overcome some of the limitations with more traditional analyses. Lastly, we also elaborate on future directions for analyses in sport injury risk factor research. More specifically, we present how advanced statistical models, such as classification and regression trees (CART) analysis and random forest analysis, can be used to provide researchers and clinicians with results that are more clinically meaningful.
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| 3. |
- Von Rosen, Dietrich
(författare)
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Profile Analysis
- 2016
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Ingår i: Wiley StatsRef: Statistics Reference Online. - : Wiley. - 9781118445112
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Bokkapitel (refereegranskat)abstract
- Classical profile analysis is reviewed. There are three basic hypothesis that are usually considered: test of parallelism, test of equality, and test of flatness. The main theme is to connect these tests within the frame of the analysis of the growth curve model. Moreover, a number of extensions are briefly mentioned including models with random effects, profile analysis within the growth curve model, profile analysis without an assumption of normality, and profile analysis with high-dimensional data
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