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The Scope and Complexity of Health Psychology Research
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Figure 14-11: The contents of this ListBox depend on the OptionButton selected.
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underlying Positive and Negative Affect dimensions. Watson et al. factor-analyzed the data from six large data sets, extracting two factors in each case. They then correlated the PANAS scales with regression-based estimates of these factors. The PANAS Positive Affect scale had correlations ranging from .89 to .95 with the Positive Affect factor scores, and from .02 to .17 with the Negative Affect factor. Conversely, the PANAS Negative Affect scale had correlations ranging from .91 to .93 with scores on the Negative Affect factor, and from .09 to .18 with the Positive Affect factor. Watson and Clark (1997) subsequently extended these ndings in 13 additional data sets. Eleven of these were betweensubject data sets with a combined sample size of 8,685. Across these samples, the PANAS Positive Affect scale had correlations ranging from .90 to .95 (Mdn = .93) with its corresponding factor score; similarly, the PANAS Negative Affect scale had convergent correlations ranging from .92 to .95 (Mdn = .94) with its target factor. In the nal two samples, respondents rated themselves repeatedly over a large number of occasions: One data set was based on momentary ratings (10,169 observations) and the other on daily ratings (11,322 observations). The data in each sample were standardized on a within-subject basis and then subjected to an overall factor analysis. The PANAS Positive Affect scale had correlations of .93 and .90 with its corresponding factor score in the momentary and daily ratings, respectively; the convergent correlations for the Negative Affect scale were .89 and .89, respectively. The construct validity of the PANAS is further supported by a diverse array of evidence. For instance, state versions of the scales (in which respondents rate how they are feeling currently, or how they have felt over the course of the day) have been shown to be sensitive to a variety of transient situational and biological factors. Thus, PANAS Negative Affect scores are signi cantly elevated in response to stress and are reduced following moderate exercise. Conversely, Positive Affect scores are signi cantly elevated following exercise and social interactions, and show a systematic circadian rhythm over the course of the day; they also have been shown to be highly sensitive to variations in the daily body temperature rhythm and the sleep-wake cycle (see Watson, 2000a; Watson et al., 1999). Furthermore, trait versions of the scales (in which respondents rate how they have felt over the past year, or how they feel in general) are strongly stable over several months and display substantial levels of stability over retest intervals as long as 7 years (Watson & Clark, 1994; Watson & Walker, 1996). In addition, self-ratings on the scales show signi cant convergent validity when correlated with corresponding judgments made by well-acquainted peers, such as friends,
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The cash flow from operating activities to current liabilities ratio is a measure of liquidity and is calculated as follows: Cash flow from operating activities Average current liabilities The cash flow from operating activities in our situation is $290,900 from Exhibit 10.7, and the current liabilities for year 0004 and year 0005 are shown in Exhibit 10.2. The result is $290,900 ($38,100 $40,300) / 2 $290,900 $39,200 7.42 times, or 742%
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In XML, CSS style sheets apply only to elements, not attributes. Attribute content will appear blank when a CSS style sheet is applied. XSL (Extensible Style Language) is the answer. XSL has two sections: transformations and formatting.
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EVOLVING CLUSTERING BASED ON GK SIMILARITY DISTANCE
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s 1 to 6 provide the reader with hands-on analysis problems, representing an opportunity for the reader to apply his or her newly acquired data mining expertise to
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TABLE 21.2 Study Artifacts Beyond Sampling Error That Alter the Value of Outcome Measures, With Examples From Personnel Selection Research 1. Error of measurement in the dependent variable. Example: Study validity will be systematically lower than true validity to the extent that job performance is measured with random error. 2. Error of measurement in the independent variable. Example: Study validity for a test will systematically understate the validity of the ability measured since the test is not perfectly reliable. 3. Dichotomization of a continuous dependent variable. Example: Turnover the length of time that worker stays with the organization is often dichotomized into more than . . . or less than . . . , whereby the cutoff point is some arbitrarily chosen interval such as 1 year or 6 months. 4. Dichotomization of a continuous independent variable. Example: Interviewers are often told to dichotomize their perceptions into acceptable versus reject. 5. Range variation in the independent variable. Example: Study validity will be systematically lower than true validity to the extent that hiring policy causes incumbents to have a lower variation on the predictor than is true of applicants. 6. Attrition artifacts: Range variation in the dependent variable. Example: Study validity will be systematically lower than true validity to the extent that there is systematic attrition in workers on performance, as when good workers are promoted out of the population or when poor workers are red for poor performance, or both. 7. Deviation from perfect construct validity in the independent variable. Example: Study validity will vary if the factor structure of the test differs from the usual structure of tests for the same trait. 8. Deviation from perfect construct validity in the dependent variable. Example: Study validity will differ from true validity if the criterion is de cient or contaminated. 9. Reporting or transcriptional error. Example: Reported study validities may differ from actual study validities due to a variety of reporting problems: inaccuracy in coding data, computational errors, errors in reading computer output, typographical errors by secretaries or by printers. These errors can be very large in magnitude. 10. Variance due to extraneous factors. Example: Study validity will be systematically lower than true validity if incumbents differ in job experience at the time their performance is measured.
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