Is it really brand love? A cautionary note on Nguyen and Feng’s study

In this cautionary note, the author argues that the recent study by Nguyen and Feng (https://doi.org/10.1016/j.ijresmar.2020.10.001) did not investigate the antecedents and financial impacts of building brand love but rather those of brand liking. A close examination of some of the descriptive statistics reported by Nguyen and Feng indicates that their statistical models were run on consumer responses that are more indicative of brand liking than brand love. The author also demonstrates via one correlation estimate and one coefficient alpha value (taken from Nguyen and Feng’s article) that the single-item measure these authors used to gauge brand love is less than reliable. Marketing scholars, market researchers and brand managers are advised to be extremely cautious concerning the theoretical and managerial implications of that study.

Meta-Analysis of Correlation Coefficients: A Cautionary Tale on Treating Measurement Error

A scale to measure a psychological construct is subject to measurement error. When meta-analyzing correlations obtained from scale scores, many researchers recommend correcting measurement error. We considered three caveats when conducting meta-analysis of correlations: (1) the distribution of true scores can be non-normal, resulting in a violation of the normality assumption for raw correlations and Fisher's z transformed correlations; (2) coefficient alpha is often used as the reliability, but correlations corrected for measurement error using alpha can be inaccurate when some assumptions (e.g., tau-equivalence) of alpha are violated; and (3) item scores are often ordinal, making the disattenuation formula potentially problematic. Via three simulation studies, we examined the performance of two meta-analysis approaches with raw correlations and z scores. In terms of estimation accuracy and coverage probability of the mean correlation, results showed that (1) the true score distribution alone had slight influence; (2) when the tau-equivalence assumption was violated and coefficient alpha was used for correcting measurement error, the mean correlation estimate can be biased and coverage probability can be low; and (3) discretization of continuous items can result in under-coverage of the mean correlation even when tau-equivalence was satisfied. With more categories and/or items on a scale, results can improve when tau-equivalence was met or not. Based on these findings, we then gave recommendations when conducting meta-analysis of correlations.

Indirect Standardization as a Case Mix Adjustment Method to Improve Comparison of Children’s Hospitals’ Antimicrobial Use

Antimicrobial use (AU) in days of therapy per 1000 patient-days (DOT/1000pd) varies widely among children’s hospitals. We evaluated indirect standardization to adjust AU for case mix, a source of variation inadequately addressed by current measurements. Hospitalizations from the Pediatric Health Information System were grouped into 85 clinical strata. Observed to expected (O:E) ratios were calculated by indirect standardization and compared to DOT/1000pd. Outliers were defined by O:E z-scores. Antibacterial DOT/1000pd ranged from 345 to 776 (2.2-fold variation; interquartile range [IQR] 552-679), whereas O:E ratios ranged from 0.8 to 1.14 (1.4-fold variation; IQR 0.93-1.05). O:E ratios were moderately correlated with DOT/1000pd (correlation estimate 0.44; 95% CI 0.19-0.64; p=0.0009). Using indirect standardization to adjust for case mix reduces apparent AU variation and may enhance stewardship efforts by providing adjusted comparisons to inform interventions.

Overcoming Limitations of the Independent Clusters Model for CFA by Means of Bayes-Estimation and Buffered Simple Structure

The model of buffered simple structure is discussed as a method for modeling cross-loadings in confirmatory factor analysis. This method introduces assumptions from item sampling theory into confirmatory factor analysis. The independent clusters model, buffered simple structure, and Bayes estimation were compared by means of a simulation study based on three different population types. Population type A had zero cross-loadings, population type B had symmetrically distributed nonzero cross-loadings, and population type C had asymmetrically distributed nonzero cross-loadings. It turned out for population A that, although the independent clusters model yields the best loading estimate, it did not outperform Bayes estimation and buffered simple structure with respect to the factor inter-correlation estimate and model fit. One reason for this unexpected result could be that the specification of zero-cross loadings is suboptimal even when only sampling error introduces some cross-loadings. For populations B and C Bayes estimation and buffered simple structure clearly outperformed the independent clusters model. Overall, the results indicate that depending on the structure of cross-loadings in the population and depending on the focus on loading estimates or factor inter-correlation estimates, different modeling approaches might be appropriate.

A multifractal boundary spectrum for $${{\,\mathrm{SLE}\,}}_\kappa (\rho )$$

We study $${{\,\mathrm{SLE}\,}}_\kappa (\rho )$$ SLE κ ( ρ ) curves, with $$\kappa $$ κ and $$\rho $$ ρ chosen so that the curves hit the boundary. More precisely, we study the sets on which the curves collide with the boundary at a prescribed “angle” and determine the almost sure Hausdorff dimensions of these sets. This is done by studying the moments of the spatial derivatives of the conformal maps $$g_t$$ g t , by employing the Girsanov theorem and using imaginary geometry techniques to derive a correlation estimate.

Overcoming limitations of the independent clusters model for CFA by means of Bayes-estimation and buffered simple structure

The model of buffered simple structure is discussed as a method for modeling cross-loadings in confirmatory factor analysis. This method introduces assumptions from item sampling theory into confirmatory factor analysis. The independent clusters model, buffered simple structure, and Bayes estimation were compared by means of a simulation study based on three different population types. Population type A had zero cross-loadings, population type B had symmetrically distributed nonzero cross-loadings, and population type C had asymmetrically distributed nonzero cross-loadings. It turned out for population A that, although the independent clusters model yields the best loading estimate, it did not outperform Bayes estimation and buffered simple structure with respect to the factor inter-correlation estimate and model fit. One reason for this unexpected result could be that the specification of zero-cross loadings is suboptimal even when only sampling error introduces some cross-loadings. For populations B and C Bayes estimation and buffered simple structure clearly outperformed the independent clusters model. Overall, the results indicate that depending on the structure of cross-loadings in the population and depending on the focus on loading estimates or factor inter-correlation estimates, different modeling approaches might be appropriate.

AIREML estimation of genetic parameters and study of factors affecting growth and fertility performance of sire line of IBL-80 broiler chicken

The present investigation included the data of 29,879 birds pertaining to 8 generations, from 2010 to 2018 on growth line (PB1) of IBL-80 broiler. The mean estimates of growth and fertility traits were BWT0 (39.97±0.05 gms), BWT5 (1189.17±1.45 gms), BWT10 (1723.59±6.26 gms), BWT15 (2165.71±7.90 gms), BWT20 (2611.23±4.10 gms), ADG5 (32.36±0.07 gms/day), ADG10 (13.09±0.14 gms/day), ADG15 (12.38±0.13 gms/day), ADG20 (12.65±0.13 gms/day), AFE (171.80±0.21 days) and ENO40 (62.47±0.25) which indicated higher growth performance of PB1 affected its fertility performance. ADG5 had highest estimate indicating higher growth during chick stage. Least squares analysis indicated that effect of gender, month of hatch and generation were significant (p less than 0.01) for all growth and fertility traits. AIREML heritability estimates indicated appreciable additive variance in BWT0 (0.50), BWT5 (0.54) and ADG5 (0.20). Other growth and fertility traits had lower heritability which was due to stage wise selection in breeder flock. Phenotypic and genetic correlation estimate indicated negative association between growth and fertility traits.

Genetic parameters and combined selection for phosphorus, phytate, iron, and zinc in Mesoamerican common bean lines

ABSTRACT The development of common bean cultivars that contain satisfactory minerals and phytate concentrations for the different nutritional requirements of consumers is a new strategy of breeding programs. This work aimed to obtain estimates of genetic parameters for the concentrations of phosphorus, phytate, iron, and zinc in a recombinant inbred line (RIL) population of Mesoamerican common bean, to study the correlations between these traits, and to select common bean lines for the biofortification program and for diets that require the decrease in the intake of these minerals. The RIL were obtained from the cross between BRS Esteio and SCS 205 Riqueza. Genetic variability and transgressive segregation were detected for all traits evaluated. Heritability estimates for the concentrations of phosphorus, phytate, iron, and zinc ranged from intermediate (h2: 30.31%) to high (h2: 98.68%) magnitude, and quantitative inheritance was observed. The phosphorus concentration showed an intermediate correlation estimate with iron (r = 0.4157) and zinc (r = 0.5693) concentrations. Cultivar BRS Expedito and line L 56-17 have a low phytate concentration (≤ 1.29%) and a high iron concentration (≥ 95 mg kg-1 of dry matter - DM), and will be selected by the common bean biofortification program. Lines L 59-17, L 31-17, and L 26-17 and cultivars IPR Siriri and BRS Valente have a high phytate concentration (≥ 2.57%) and a low zinc concentration (≤ 26 mg kg-1 DM) and will be selected for diets that aim at using the beneficial properties of phytate and reducing the zinc intake.

Validation of the total illness burden index for prostate cancer (TIBI-CaP) in men with castration-resistant prostate cancer (CRPC): Data from TRUMPET.

256 Background: The TRUMPET registry is a prospective, observational cohort study of patients (pts) with CRPC designed to evaluate treatment patterns and health-related quality of life (HRQoL) outcomes associated with CRPC and its management in a real-world setting. Comorbidities may influence how physicians approach CRPC treatment; therefore, evaluation of comorbidity presence and severity is important. The TIBI-CaP questionnaire measures comorbidity, with the aim of this analysis to validate TIBI-CaP in CRPC. Methods: Data were collected from 302 enrolled CRPC pts treated in academic and community-based sites under routine care. Baseline data collected included clinical history and self-reported demographics, comorbidities, and HRQoL. TIBI-CaP scores were analyzed based on correlation analysis and analysis of variance (ANOVA). Estimated correlations were used to verify the association of TIBI-CaP scores to scores on the SF-12v2 and FACT-P questionnaires. ANOVA models were run with SF-12v2 and FACT-P as response and quartile ranges for TIBI-CaP scores as predictor. Results: Mean age was 73.7 years. 84.7% were white; 13.9% were black. 87.8% had M1 CRPC at study entry. Mean (SD) TIBI-CaP score was 5.3 (2.72) [range 0-13], with 42.4% of CRPC pts presenting with moderate/severe comorbidity burden (higher scores). TIBI-CaP scores had statistically significant (p value < 0.0002) negative correlations with all SF-12v2 composite and domain scores. Correlation estimates for physical condition and mental condition scores were -0.46 and -0.23, respectively. TIBI-CaP scores also had statistically significant (p value < 0.02) negative correlations with FACT-P total scores and all subscales. FACT-P total scores had a -0.44 correlation estimate. F-tests showed significant differences across the four quartiles of TIBI-CaP scores and SF-12v2 and FACT-P (all p values < 0.05). Conclusions: At baseline, TIBI-CaP scores were negatively correlated with CRPC pts baseline functional status as measured by the SF-12v2 and FACT-P questionnaires. TIBI-CaP was strongly associated with HRQoL physical subscales. This analysis demonstrates validity of TIBI-CaP in CRPC pts.