Linda S. Fidell's research while affiliated with California State University and other places
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Publications (7)
Assessment of aircraft noise-induced sleep disturbance is problematic for several reasons. Current assessment methods are based on sparse evidence and limited understandings; predictions of awakening prevalence rates based on indoor absolute sound exposure levels (SELs) fail to account for appreciable amounts of variance in dosage-response relation...
Awakenings attributable to transportation noise intrusions into residential sleeping quarters are surprisingly rare events. Current methods for estimating such awakenings from indoor sound exposure levels are problematic for several reasons. They are based on sparse evidence and limited understandings; they fail to account for appreciable amounts o...
A recent social survey of the annoyance of low amplitude sonic booms included both prompt and delayed-response questions about the annoyance of sonic booms heard by respondents in the home over the course of two weeks. Interviews were conducted via smartphone with 49 voluntary test participants. Most of the prompt annoyance judgments were made with...
ANOVA (analysis of variance) tests whether mean differences among groups on a single DV (dependent variable) are likely to have occurred by chance. MANOVA (multivariate analysis of variance) tests whether mean differences among groups on a combination of DVs are likely to have occurred by chance. For example, suppose a researcher is interested in t...
Preparatory data analyses (data screening) are conducted before a main analysis to assess the fit between the data and the assumptions of that main analysis. Different main analyses have different assumptions that vary in importance; violation of some assumptions can lead to the wrong inferential conclusion (and a potential failure of replication)...
Citations
... To characterize and discriminate samples, Principal Component and Discriminant function analyses were performed using "MASS" and "Lattice" packages implemented in the R platform (Vanables & Ripley 2002;Sarkar 2008). To test whether OTUs have the same average for different cranial measurements, we used unifactorial Multivariate Analysis of Variance (MANOVA) to calculate Wilks' lambda statistic (λ-Wilks') and the associated value of f's Rao (Tabachnick & fidell 2014). Pairwise comparisons were made with the Hotelling-pairwise test with Bonferroni-corrected p-value. ...
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... We used the principal axis method of factor extraction followed by oblimin rotations to account for the assumption of intercorrelations between the factors of the AFFEXX-C. According to the guidelines [32][33][34], items with factor loadings ≥ 0.50 and cross-loadings ≤ 0.25 were included. Based on sample 2 (n = 383), ii) internal consistency was tested with Cronbach's alpha, and iii) three confirmatory factor analyses (CFAs) of antecedent appraisals, core affective exercise experiences, and attraction-antipathy were conducted. ...
... The probability value of 0.000, which is less than a 5% level of significance, is an indication that the model is fit. According to (Fidell, Tabachnick, Mestre, & Fidell, 2013), a significant level of less than or equal to .05 is an indication that the model is fit for social science research. Therefore, it can be concluded that the independent variables (job dissatisfaction, stress, negative attitude towards work and high turnover rate) can significantly influence an organization's productivity negatively. ...
... Transformation or removal of outliers can help ensure this assumption. Violation of this assumption may lead to committing Type I error rates (Tabachnick & Fidell, 2001). Non-normal population distributions, especially, those that are thick-tailed or heavily skewed, considerably reduce the power of the test. ...
... Normal distribution was present for all CFSS-2 scales, along with total IHS scores for both groups and intervention group E, N, and PFO subscales. For other (sub)scales, normalization was achieved with outlier removal, as identified via boxplot primarily (Bakker & Wicherts, 2014) or using Z scores if not clear (Fidell & Tabachnick, 2003). One outlier was removed from the intervention (PERMA-Profiler scores and R, A, H, and TAC subscales) and control (P and LOP subscales) groups, two from the intervention (P and M subscales) and control (SRPV subscale) groups, and four from the PERMA-Profiler control group. ...
