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Langsrud, Øyvind

doi:10.1023/a:1023260610025

Cited by 384 publications

(118 citation statements)

References 31 publications

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“…In terms of hypothesis tests this type is the easiest one to interpret both in unbalanced and balanced cases. Nevertheless, in different situations different types of ANOVA tables are advised (Speed et al 1978;Senn 2007;Langsrud 2003;Macnaughton 2009). …”

Section: Discussionmentioning

confidence: 99%

“…We do not touch this topic here and refer to Venables (2000); Speed, Hocking, and Hackney (1978); Senn (2007); Langsrud (2003); Macnaughton (2009) for the discussions. In the lmerTest package instead we provide a tool for obtaining the three types of ANOVA tables for the objects returned by lmer, which are implemented via calculation of the appropriate hypothesis contrast matrix L in Equation 3.…”

Section: Types Of Hypothesis Testsmentioning

confidence: 99%

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lmerTest Package: Tests in Linear Mixed Effects Models

Kuznetsova¹,

Brockhoff²,

Christensen³

2017

J. Stat. Soft.

17,769

12,488

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One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I-III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects -both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well.

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Section: Discussionmentioning

confidence: 99%

Section: Types Of Hypothesis Testsmentioning

confidence: 99%

lmerTest Package: Tests in Linear Mixed Effects Models

Kuznetsova¹,

Brockhoff²,

Christensen³

2017

J. Stat. Soft.

17,769

12,488

View full text Add to dashboard Cite

show abstract

“…The extracted keystroke duration and keystroke latency features were submitted to two two-way 3 (Valence: negative, neutral, and positive) × 3 (Arousal: low, medium, and high) Repeat Measures ANOVAs [22], respectively. To analyse the accuracy rate of keyboard typing, the accuracy data (0 for incorrectly typed sequence and 1 for correctly typed sequence) of all the typed sequences was submitted to a two-way 3 (Valence: negative, neutral, and positive) × 3 (Arousal: low, medium, and high) Repeat Measures ANOVA.…”

Section: Methodsmentioning

confidence: 99%

The influence of emotion on keyboard typing: an experimental study using visual stimuli

2014

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BackgroundEmotion recognition technology plays the essential role of enhancement in Human-Computer Interaction (HCI). In recent years, a novel approach for emotion recognition has been reported, which is by keystroke dynamics. This approach can be considered to be rather desirable in HCI because the data used is rather non-intrusive and easy to obtain. However, there were only limited investigations about the phenomenon itself in previous studies. This study aims to examine the source of variance in keystroke typing patterns caused by emotions.MethodsA controlled experiment to collect subjects’ keystroke data in different emotional states induced by International Affective Picture System (IAPS) was conducted. Two-way Valence (3) × Arousal (3) ANOVAs were used to examine the collected dataset.ResultsThe results of the experiment indicate that the effect of emotion is significant (p < .001) in the keystroke duration, keystroke latency, and accuracy rate of the keyboard typing. However, the size of the emotional effect is small, compare to the individual variability.ConclusionsOur findings support the conclusion that the keystroke duration, keystroke latency, and also the accuracy rate of typing, are influenced by emotional states. Notably, the finding about the size of effect suggests that the accuracy rate of the emotion recognition could be further improved if personalized models are utilized. On the other hand, the finding also provides an explanation of why real-world applications which authenticate the identity of users by monitoring keystrokes may not be interfered by the emotional states of users. The experiment was conducted using standard instruments and hence is expected to be highly reproducible.

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“…As such, the Type III tests implicitly entertain models that are arguably unrealistic. It is for this reason that the Type II tests are sometimes regarded more favorably than the Type III (Nelder, 1977, 1994; Nelder and Lane, 1995; Langsrud, 2003; Fox, 2008; Fox and Weisberg, 2011). …”

Section: The Unbalanced Overparameterized Anova Modelmentioning

confidence: 99%

Testable Hypotheses for Unbalanced Neuroimaging Data

McFarquhar

2016

Front. Neurosci.

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Unbalanced group-level models are common in neuroimaging. Typically, data for these models come from factorial experiments. As such, analyses typically take the form of an analysis of variance (ANOVA) within the framework of the general linear model (GLM). Although ANOVA theory is well established for the balanced case, in unbalanced designs there are multiple ways of decomposing the sums-of-squares of the data. This leads to several methods of forming test statistics when the model contains multiple factors and interactions. Although the Type I–III sums of squares have a long history of debate in the statistical literature, there has seemingly been no consideration of this aspect of the GLM in neuroimaging. In this paper we present an exposition of these different forms of hypotheses for the neuroimaging researcher, discussing their derivation as estimable functions of ANOVA models, and discussing the relative merits of each. Finally, we demonstrate how the different hypothesis tests can be implemented using contrasts in analysis software, presenting examples in SPM and FSL.

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Cited by 384 publications

References 31 publications

lmerTest Package: Tests in Linear Mixed Effects Models

lmerTest Package: Tests in Linear Mixed Effects Models

The influence of emotion on keyboard typing: an experimental study using visual stimuli

Testable Hypotheses for Unbalanced Neuroimaging Data

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