Abstract: The aim of the current study was to develop and
validate a Response to Stressful Situations Scale (RSSS) for the
Portuguese population. This scale assesses the degree of stress
experienced in scenarios that can constitute positive, negative and
more neutral stressors, and also describes the physiological,
emotional and behavioral reactions to those events according to their
intensity. These scenarios include typical stressor scenarios relevant
to patients with schizophrenia, which are currently absent from most
scales, assessing specific risks that these stressors may bring on
subjects, which may prove useful in non-clinical and clinical
populations (i.e. Patients with mood or anxiety disorders,
schizophrenia). Results from Principal Components Analysis and
Confirmatory Factor Analysis of two adult samples from general
population allowed to confirm a three-factor model with good fit
indices: χ2 (144)= 370.211, p = 0.000; GFI = 0.928; CFI = 0.927; TLI =
0.914, RMSEA = 0.055, P(rmsea ≤0.005) = .096; PCFI = .781.
Further data analysis of the scale revealed that RSSS is an adequate
assessment tool of stress response in adults to be used in further
research and clinical settings, with good psychometric characteristics,
adequate divergent and convergent validity, good temporal stability
and high internal consistency.
Abstract: Previous studies on financial distress prediction choose
the conventional failing and non-failing dichotomy; however, the
distressed extent differs substantially among different financial
distress events. To solve the problem, “non-distressed”, “slightlydistressed”
and “reorganization and bankruptcy” are used in our article
to approximate the continuum of corporate financial health. This paper
explains different financial distress events using the two-stage method.
First, this investigation adopts firm-specific financial ratios, corporate
governance and market factors to measure the probability of various
financial distress events based on multinomial logit models.
Specifically, the bootstrapping simulation is performed to examine the
difference of estimated misclassifying cost (EMC). Second, this work
further applies macroeconomic factors to establish the credit cycle
index and determines the distressed cut-off indicator of the two-stage
models using such index. Two different models, one-stage and
two-stage prediction models are developed to forecast financial
distress, and the results acquired from different models are compared
with each other, and with the collected data. The findings show that the
one-stage model has the lower misclassification error rate than the
two-stage model. The one-stage model is more accurate than the
two-stage model.