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Mean gender typicality ratings obtained in a control study Supplementary Material were included as a covariate in the analysis of both accuracy and reaction times. The former Race-by-Emotion effect on male faces was expected and corresponds to a ceiling effect on the reaction times to Caucasian male faces. Indeed, reaction time for neutral female Chinese faces was relatively long, akin to that for angry female Chinese faces Figure 2B and unlike that for neutral female Caucasian faces Figure 2A.
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Since there was no hypothesis regarding this effect, it will not be discussed further. Best LMM of adult inverse reaction time from correct trials. Reaction times for gender categorization in Experiments 1 adults and 2 children. Only reaction times from correct trials are included.
Top: Caucasian A and Chinese B female faces. Bottom: Caucasian C and Chinese D male faces. However, further decomposition revealed that it had different roots in Caucasian and Chinese faces.
The impairing effect of an angry expression on female face categorization was clearest on the relatively easy Caucasian faces, while a converse facilitating effect on male face categorization was most evident for the relatively difficult Chinese faces.
The effect of Gender was largest for the difficult Chinese faces. The angry expression increased reaction times for Caucasian female faces Figure 2A and conversely reduced them for Chinese male faces Figure 2D. ANOVA of d-prime for adult gender categorization. ANOVA of male-bias for adult gender categorization. Male bias was high overall, also replicating the finding by O'Toole et al.
Since Emotion affects the male bias but not sensitivity in Chinese faces, it follows that the effect of Emotion on the male bias is not solely mediated by its effect on sensitivity.
Sensitivity and male bias for gender categorization in Experiments 1 adults and 2 children. Discussion The effect of anger on gender categorization was evident on reaction time, as participants were 1 slower when categorizing the gender of angry Caucasian female faces, 2 slower with angry Chinese female faces, and 3 quicker with angry Chinese male faces.
However, a ceiling effect on accuracy for male faces made it impossible to definitively support this idea. To firmly conclude in favor of a true bias, it should be observed that angry expressions both hinder female face categorization as was observed and enhance male face categorization which was not observed.
While a small but significant increase in accuracy for angry vs. Different from the present results, O'Toole et al. The source of the difference is uncertain, one possibility being that the greater difficulty of the task used in O'Toole et al. Finally, O'Toole et al. However, the sample for the current study did not include enough male participants to allow us to analyze this possible effect.
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Experiment 2: Gender Categorization in Children One way to understand the male bias is to investigate its development.
If the angry male bias develops through extensive experience with peers observing male aggression during the school years, it follows that the angry male bias should be smaller in children than in adults and that the bias would increase during the school years, a time period when children observe classmates mostly males engaging in aggressive acts inclusive of fighting and bullying.
In Experiment 2, we conducted the same gender categorization task as in Experiment 1 with 64 children aged from 5 to The inclusion of children in the age range from 5 to 6, as well the testing of 7—8, 9—10, and 11—12 year-olds, is important from a developmental perspective. Experiment 2 should additionally allow us to 1 overcome the ceiling effect on gender categorization for male faces that was observed in Experiment 1 as children typically perform worse than adults in gender categorization tasks, e.
While facial expression perception also develops over childhood and even adolescence Herba and Phillips, , recognition performance for own-race expressions of happiness and anger have been reported to be at ceiling from 5 years of age Gao and Maurer, ; Rodger et al. Methods Participants and Preprocessing Thirteen 5—6 year-olds 9 boys , 16 7—8 year-olds 3 boys , 15 9—10 year-olds 9 boys , and 14 11—12 year-olds 3 boys from a predominantly Caucasian environment were included in the final sample.
These age groups were chosen a priori due to the minimal need to re-design the experiment: children from 5 to 6 years of age may complete computer tasks and follow directions. A range of age groups was then selected from 5 to 6 years old onwards, covering the developmental period from middle to late childhood, and the time when children begin formal schooling. The experiment was approved by the University of Victoria Human Research Ethics Board and informed parental consent was obtained.
Additionally, trials from participants were excluded if their reaction times were extremely short less than , , , or ms for 5—6 year olds, 7—8 year olds, 9—10 year olds, or 11—12 year olds, respectively or further than 2 standard deviations away from the participant's own distribution.
Such invalid trials were handled as missing values, leading to the exclusion of The cut-offs used to exclude trials with very short reaction times were selected graphically based on the distribution of reaction times within each age group. Due to an imbalance in the gender ratio across age groups, the participant's gender was included as a between-subject factor in the analyses.
Results Reaction Times There was a significant Race-by-Gender-by-Emotion interaction in the best linear mixed model LMM of children's inverse reaction times from correct trials Table 4 , along with a three-way Age-by-Gender-by-Participant gender interaction, an Age-by-Race-by-Emotion interaction, and a Participant gender-by-Gender-by-Emotion interaction.
Best LMM of children's inverted reaction times from correct trials.
The interaction of Gender, Emotion, and Participant gender was due to the effect of Gender on angry faces reaching significance in female female faces, inverted RT: 9.
The interaction of Race and Emotion with Age reflected the shorter reaction times of 5—6 year olds when categorizing the gender of Caucasian vs. Faster responses to smiling Caucasian faces by the youngest participants probably reflect the familiarity, or perception of familiarity in these stimuli.
Children were slower when categorizing the gender of angry vs. The interaction of Gender and Emotion was present in all participants but most evident in female participants. It was absent in Chinese faces.
In other words, an angry expression slows gender categorization in own-race Caucasian but not in other-race Chinese faces. Neither for sensitivity nor for male-bias did the Race-by-Emotion interaction or its subcomponents interact with Age.
ANOVA of male-bias for children's gender categorization.
This pattern is remarkably similar to that found in reaction times. In contrast, anger increased the male-bias in Caucasian Figure 3C as well as Chinese faces Figure 3D , although to a lesser extent in the latter category. In other words, the biasing effect of anger cannot be reduced to an effect of perceptual difficulty.
That is, the male-biasing effect of anger is evident by its interfering effect during female trials as well as by its converse facilitating effect during male trials. These last observations are compatible with the idea that angry expressions bias gender categorization. The effect can be observed across all ages and even with unfamiliar Chinese faces, although in a diminished form.
The set of indicators is chosen among organisational archival data with the intent of collecting objective and numerable markers e. Thus, they make reference mainly to: organisational structure, turnover, sickness absence, injuries, human resource practices and environmental risks.
As Rugulies noted [ 37 ], archival data are useful for assessing quantitative and emotional demands at work. The following sub-stages foresee the participation of workers. The choice of appropriate instrument at this stage is determined by the type of information to be detected or the peculiarities of the context. Data collected during the sub-stages are essential for deepening knowledge of the context and interpreting the results at the end of the assessment process.
The tools used at this stage are described in the following. The second sub-stage envisages the use of focus group technique. This qualitative technique may be used in a preliminary phase to address the most common issues, which derive from the lack of knowledge of the context cf. This methodology is useful because it provides information about the degree of consensus and disagreement.
Furthermore, such an instrument allows for richer information because it helps to determine how much all data contribute to saturation for the focus groups the so-called saturation within-group data [ 39 ]. Groups are formed considering that enough diversity can stimulate discussion, while an appropriate level of homogeneity can facilitate comparison between groups [ 40 ].
Focus groups are designed and conducted following the instructions provided by the literature [ 38 , 41 , 42 ]. During the sessions, two members of the research group play two different roles, one as a facilitator and moderator of the group work, and the other as an external observer, who tracks the interactions between participants taking field notes.
A semi-structured frame is established prior to the beginning of each session, indicating that the participants can intervene in a way that is as similar as possible to an informal conversation. In order to guide and reactivate the conversation when necessary, the interviewers can use a script with questions referring to psychosocial risks and characteristics of the work environment, covering all the relevant levels of analysis including personal, interpersonal and organisational.
The questioning route also takes into account relevant information obtained during the preparatory meetings with the steering committee. The focus group developed in the StART method includes questions on organisational psychosocial risks. The notation system is according to Creswell and Plano Clark [ 52 ]. The capital letters in QUANT refer to the greater weight assigned to the quantitative data, while qualitative data resulting from the focus group serve the purpose of guiding the choice of the questionnaire scales and are merged with the other results to assess conjointly the presence of psychosocial risks related to stress.
The next two sub-stages do not strictly follow a sequential path. The third sub-stage is questionnaire administration. It collects quantitative data that allow a comparison between subjects or groups, and has moderate costs. Unlike other questionnaires used in order to perform the assessment of the psychosocial work environment e. According to Bakker and Demerouti [ 10 ], every work environment is considered to have its own specific risk factors associated with job stress. These factors can be classified into two general categories, which are job demands e.
For these reasons, the questionnaire encompasses two sections. The first which is stable investigates the factors more associated with work-related stress in literature, both job demands e. Moreover, it includes personal outcomes e. The second section strictly depends on the results of the focus groups, the data collected with the OIS and the information collected during the meetings with the steering committee.
The validity of such a questionnaire is supported by the selection of widely validated scales e. The fourth sub-stage concerns the observational checklist OC. The main aim of the observation is to make an objective evaluation of psychosocial risk factors related to specific job positions. The focus of the observation is not the single physical person, but rather the typical tasks of a given organisational position. The objective is to identify job demands and events that may impede or interrupt the worker activities: potential stressors are considered a disturbance in the work regulation process, as they represent conditions that hinder the achievement of the objectives when there are no resources that can be used to cope with such obstacles [ 46 ].
In particular there should be: a a preliminary study of the observation methodology including potential observation biases ; b an analysis of general informations about occupations as well as a job analysis ; c at least two tutorial exercises observations with subsequent discussion of problems encountered; finally d a full comprehension of dimensions of the observational checklist.
Indicators chosen to detect psychological risk factors depend on the results of the preliminary phase information collected via OIS and focus groups and the tasks performed by the specific position observed.
To date, few studies have tried to combine different sources of data [ 46 , 47 ]. Factors measuring increased connection and bonding between listeners over shared musical tastes and group listening experiences have been determined Chin and Rickard, ; Mas-Herrero et al. Other measures have focused on the value of music in social situations, such as increased atmosphere and celebration Kuntsche et al. Therefore, music listening may have an important function in the development and maintenance of positive relationships with others.
At the same time, using existing measures, the relationship between the social functions of music and wellbeing outcomes have not been firmly established. For instance, in one study, the factor measuring social connection on the MUSE questionnaire was not associated with enhanced subjective, psychological or social wellbeing; rather it was significantly associated with increased use of the emotion regulation strategy of suppression, which predicted lower levels of wellbeing Chin and Rickard, a.
A survey study of younger adults by Papinczak et al. Theoretically, social FML should be related to greater psychological wellbeing Ryff and Keyes, and social wellbeing Keyes, , however, empirical support is lacking. In relation to the social function of identity, although further scale development work was not undertaken, identity FML have been extracted using Principal Components Analysis PCA in survey studies with adolescent Lonsdale and North, and older samples Laukka, In line with theory and research it is predicted that if identity FML are uncovered in factor analysis in the current study, they may relate to greater SWB through increased positive affect Kahn et al.
Previous investigations have uncovered a number of FML that could be described as eudaimonic functions.
Participants engaged in focus groups voted that a number of these FML i. Outside of musical contexts, such eudaimonic experiences, particularly transcendence, have been associated with increased happiness and life satisfaction, and greater meaning in life Gillham et al.
Empirical studies of FML have tended not to include items to measure these eudaimonic functions, thus it remains to be seen whether eudaimonic experiences in music listening also relate positively to subjective, psychological, and social wellbeing outcomes. Factors relating to listening to music for its cognitive effects are a focus of existing measures of the FML Chamorro-Premuzic and Furnham, ; Chin and Rickard, Cognitive functions include music analysis Chamorro-Premuzic and Furnham, , and it is possible that the pleasure derived from the analysis of music may relate to enhanced SWB.
A more reflective style of music listening may also provide a sense of awe and appreciation, stimulating self-reflection and insight Cupchik, ; Groarke and Hogan, , which may theoretically relate to higher psychological wellbeing Ryff and Singer, The use of music to regulate cognitive states like curiosity and creativity, as well as focus, attention, and motivation have been noted in surveys North et al.
These effects of music may support listeners in the achievement of everyday goals that depend on cognitive engagement and proficiency DeNora, ; Groarke and Hogan, Goal-attainment and achievement are central to models of wellbeing Ryan and Deci, ; Seligman, , and have been related to emotional wellbeing in empirical research Schultheiss et al.
Therefore the pursuit of such cognitive goals by music listening may also relate to increased wellbeing. This view is consistent with some available research. For example, the cognitive and emotion regulation factor on the MUSE questionnaire was associated with higher subjective, psychological and social wellbeing Chin and Rickard, a , b.
These effects were fully mediated by increased use of the affect regulation strategy reappraisal. Qualitative research has also proposed that cognitive regulation in music may support affect regulation goals DeNora, ; Papinczak et al. Thus, it is hypothesized that cognitive FML may be associated with higher self-reported wellbeing in the current study.
The construct validity of a measure is assessed by forming theoretically-based hypotheses regarding potential relationships with other measures Carmines and Zeller, In this study construct validity will be assessed using convergent validity, that is, how closely scores on the measure under development converge with scores on other measures of related constructs Furr and Bacharach, In line with theory and research summarized above, significant associations should be observed between FML constructs as measured by the AFML scale and measures of affect, affect regulation, and wellbeing.
A measure's construct validity can also be established using concurrent validity, that is, by demonstrating even greater convergence with scores on other measures of the same construct.
Materials and Methods Design The current scale development study involved two phases: Firstly, the initial scale development phase involved item generation, assessing the dimensionality of the measure using EFA, reducing the initial pool of questionnaire items, and examining the reliability and construct validity of the AFML scale.
The second phase involved confirming the factor structure derived from EFA in a separate sample of participants recruited 9 months later. In the first phase construct validity was assessed by testing hypothesized relationships between AFML factors and subjective wellbeing outcomes. In the second confirmatory phase additional relationships between FML factors and psychological and social wellbeing outcomes were examined, in addition to relationships with a general measure of emotion regulation, and an existing general measure of music listening functions.
At least five items and one reverse scored item were generated for 38 hypothesized functions of music listening derived from these sessions. Content Validity Four content experts on scale development 3 in music psychology, 1 in psychometrics responding to an online questionnaire rated these items for their clarity, relevance, and comprehensiveness. The experts made a number of suggestions to increase the clarity and meaningfulness of items, removal of redundant items, and restructuring of the affect regulation subscales to allow for more differentiated responding.
Overall, items were rated by experts as relevant and were retained for EFA. All items were rated as very or quite clear. Although participants rated the items highly, they also reported that the questionnaire was long and repetitive in certain respects. However, all items were retained for factor analysis in order to identify the items of highest psychometric quality for inclusion in the final AFML scale. DeVellis emphasizes the need for a large pool of items, and multiple indicators for each hypothesized construct at the development stage.
Therefore, multiple indicators including 1 reverse scored item representing each of the remaining 33 hypothesized FML were administered to a development sample for item reduction and EFA.
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Further Scale Development Procedure Potential participants were invited to take part in an online survey of why they listen to music via online advertisements, university email campaigns, and national media.
All participants provided informed consent, and completed the questionnaire packet online using Survey Gizmo. Participants Development sample In the development phase, 1, participants initially consented to take part. One instructed item was included to identify insufficient effort responding i. Thirty seven participants were removed from the EFA analysis for failing to select the correct response to the instructed item.
The final sample included in analyses in both phases of the study includes only those participants who completed all items in the online questionnaire, and who selected the correct response to the instructed item.In relation to the social function of identity, although further scale development work was not undertaken, identity FML have been extracted using Principal Components Analysis PCA in survey studies with adolescent Lonsdale and North, and older samples Laukka, Therefore, each age group presents a unique set, or profile, of face processing strategies that may be more or less affected by the potential intersection of cues between male and angry faces.
Factors relating to listening to music for its cognitive effects are a focus of existing measures of the FML Chamorro-Premuzic and Furnham, ; Chin and Rickard, While a number of FML scales have been developed to measure specific functions of music listening, such as mood regulation Saarikallio, , or specific musical experiences like absorption Sandstrom and Russo, , the current study sought to validate a general measure of the FML.
A semi-structured frame is established prior to the beginning of each session, indicating that the participants can intervene in a way that is as similar as possible to an informal conversation. The monitoring of psychosocial factors and the implementation of improvement actions imply that the cycle may continue, beginning again from previous stages.
The instruments that collect quantitative data questionnaire and OS have already been validated. Stage 2 — Collection of data Stage 2 encompasses four sub-stages. Participants completed a forced-choice gender-categorization task. Finally, O'Toole et al.
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