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At the same time, funding from the National Institutes of Health was limited These constraining forces were counterbalanced by the astuteness of the prediction of a new morbidity in pediatrics (Haggerty, Roghmann, & Pless, 1975) More children with diseases that were often fatal (eg, cancer, cardiac disease, low birth weight infants) were surviving longer, but with attendant serious and/or chronic health problems The psychological implications of intensive long-term care of an ill infant, child, or adolescent for families were becoming more evident At the same time, the associations between behavior and health outcomes were more apparent Finally, in the health care setting, the provision of increasingly highly technological care within shorter hospital stays highlighted the challenges and complexity of providing care in the face of.

calculate the difference between the times of successive responses (interresponse times), calculate the reciprocal of the interresponse times (response speeds), and use the response speeds for further analysis There are various reasons for transforming a measure In some cases the distribution of the transformed data may be simpler than the distribution of the raw data For example, it may be more symmetrical, or the ratio of the standard deviation to the mean (the coef cient of variation) may be more constant in the transformed data than in the raw data In some cases a theory may make simpler predictions about one dependent variable than about another Another reason for transforming a measure is to use a dependent variable that is typically used by others in order to permit direct comparison of new results with previous ones.

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When controls are not displayed because they are outside of the visible portion of the UserForm, the accelerator keys for such controls continue to function. In this example, the user can press the Alt+L hot key (to select the Landscape mode option) even if that option is not visible. To block access to nondisplayed controls, you can write code to disable the controls when they are not displayed.

or postgraduate students. Other psychologists might teach on a part-time basis at local colleges and universities as adjunct professors or lecturers. Still others might teach by providing one-on-one clinical supervision of graduate students, interns, or postdoctoral fellows. During supervision, psychologists discuss the trainees clinical cases in depth while providing therapeutic guidance as they learn psychotherapy or psychological testing skills. Teaching may also occur in hospitals, clinics, or business environments. For example, a clinical psychologist might offer a stress management course for attorneys, business executives, nurses, clergy, police officers, or others. A psychologist might also teach a workshop on intimate relationships to young couples about to be married. A psychologist might teach other professionals such as doctors or clergy how to better maintain professional boundaries or understand psychopathology among the persons they counsel. As in psychological treatment facilities, there are numerous examples and opportunities for psychologists to teach in a wide variety of professional settings.

Probably the most important reason for transforming data is to obtain a dependent variable that accounts for a higher percentage of the variance in the data For example, a discrimination ratio, such as rate of response in the presence of a stimulus relative to the absence of a stimulus, often accounts for treatment effects better than an absolute measure, such as rate of response in the presence of a stimulus (Church, 1969) Two types of transformations have been found to be particularly useful One of them is the expression of a dependent variable as a relative, rather than absolute, value The other is a nonlinear but order-preserving transformation Examples are the logarithm, reciprocal, square root, and others in a ladder of transformations (Tukey, 1977) Some investigators are reluctant to use any transformations because of concern about distorting the raw data.

This concern is misguided because there is no particular reason to believe that the easiest variable to measure is the most fundamental for understanding the learning process Of course, it is important to specify precisely the transformations that are used because the conclusions that apply to a particular dependent variable may not apply to a transformation of the dependent variable For example, a signi cant interaction based on one dependent variable (such as time) may not be present on a transformation of that variable (such as speed) Summary Measures A measure of behavior is usually conceptualized as containing a true value plus random error The random error is assumed to have a mean of zero and a symmetrical (usually normal) distribution with some standard deviation If one examines the original measure of behavior, the true value can be lost in the random variability.

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