Dependent Variables. Study 2, Individual Level, Classification Consistency For individuals, we addressed the classical statistical question whether a particular sample result is repeatable in a new sample. For measurement, repeatability refers to re-administering the same test to the same individuals a great number of times, and then determining to which degree the same conclusion about an individual was drawn. Figure 1 shows two propensity distributions (the small distributions) and the corresponding proportions p of correct classification—rejection for person A and selection for person B. Obviously, proportion p is larger as someone’s true score is located further away from the cut score, and the question is whether short tests produce too many small p values; that is, too many applicants on either side of the cut score that would be misclassified too often based on repeated observation of their test performance. Xxxxx and colleagues (2007) suggested that organizations set a lower bound to p, so that they make explicit what they think is the minimally acceptable certainty one requires for decisions about an individual. For example, an organization could require p to be at least .9, meaning that upon repetition an individual may be not be Downloaded by [Radboud Universiteit Nijmegen] at 06:01 04 November 2013 + − − + = = − + misclassified more often than in 10% of the test repetitions. Thus, for the propen- sity distribution on the right in Figure 1 the unshaded area must cover no more than 10% of the total area. The quality of a particular personnel-selection scenario and a particular test or test battery may be measured by the proportion of unsuited individuals (for whom T < XC) that have p values of .9 or higher; this is called classification consistency and denoted CC (the minus sign refers to the rejection area). Likewise, the classification consistency CC for the selection area could be determined. Large CC values suggest that the personnel-selection scenario and the test or test battery used produce certainty about individual decisions for many ap- plicants. The question is whether CC and CC are large enough when short tests are used. We studied CC and CC for lower bound values p .7 and p .9. + − Xxxxxxxxx and Xxxxxx (1997) and Xxxxxxx and Xxxxxx (2002) discussed similar approaches to rejection/selection problems but used different outcome measures; see Emons and colleagues (2007) for a critical discussion of their approaches. The latter authors concluded that the clas...
Dependent Variables. Variables measured in the Experiment 2 are listed below in the order as they were measured during the experiment. Demographic variables Participants reported their gender, age, education level, and employment status. The main research questions (RQ)
Dependent Variables. Using the 2007-cohort and the RISc-data, we examine whether or not an offender is imprisoned (in/out) and if so, for how long. This approach is consistent with prior research.4 Incarceration is measured with a dichotomous variable coded 0 for non- incarceration and 1 for incarceration, and the length of the prison sentence is a con- tinuous measure capturing the total days of confinement ordered by the judge.5 The in/out decision is not analyzed using the Prison Project data because the vast majority of the suspects in this sample received a prison sentence (94%) (see also Xxxxxxx et al., Xxxxx Xxxxxxx et al. 2010; Xxxxx, Xxxxxxxxxx, and Xxxxxxx, 2010). Instead, we examine whether or not the unsuspended length of the imposed prison sentence exceeds the time served in pretrial detention.6 For the subgroup of offenders who receive “extra time,” we examine the length of this extra term of imprisonment, and for the full sample, the full length of the prison sentence is measured. Among the total 2007-cohort, 18 percent are incarcerated, with a mean term of imprisonment of 226 days. A prison sentence is imposed in 47 percent of the cases in the RISc-data, and the average prison term is 345 days. Of all offenders in the Prison Project, 61 percent receives a prison length at sentencing that exceeds the term of pretrial detention, with an average “extra term” of 418 days. The full prison length is approximately 370 days. Differences in the above described results are caused by differences in population and measurement between datasets. Important differences are related to the extent through which defendants have been selected through the criminal justice system: the 2007-cohort includes all suspects, the RISc-data only those with a pre-sentencing report, and the Prison Project includes pretrial detained offenders only. Consequently, when the results of ethnic disparities in sentencing are presented, it is especially valuable to pay attention to differences within datasets rather than between. The dichotomous sentencing outcomes are modeled with logistic regression. For those incarcerated, sentence length outcomes are transformed logarithmically and modeled with OLS regression. The log transformation normalizes the skewed distri- bution and addresses the fact that additional days of incarceration may have different meaning for different sentence lengths. Also, it allows for more convenient interpreta- tion of the sentence lengths in terms of their proportional incre...
Dependent Variables. Economic Governance Institu- tions Hall and Soskice (2001) differentiated between two ideal types of governance sys- tems: liberal and coordinated. Because they were primarily concerned with broadly characterizing the ideal types and their innovational outcomes, they did not provide precise indicators. Xxxxx Xxxxxx (2005) has criticized this reliance on dichotomous ideal types because it does not allow for the capture of important gradations, unex- pected hybrids, or changes over time. Rather, he advocates evaluating the strength of each possible governance institution for every observation. By evaluating ‘degrees of coordination’ in corporate governance institutions, Gourevitch and Xxxxx (2005) offer clearer indicators and a more ordinal measure, along the lines recommended by Xxxxxx. But because they still base their analysis on a dichotomous typology, they mischaracterize observations that have stronger coordinating institutions than exist in liberal systems but that operate with a different internal logic than coordinated institutions. Though the VoC scholars have identified five such institutions, I am focusing on two: inter-firm linkage and corporate governance institutions. I also evaluate recent research by Xxxxxxx (2010) concerning a third, worker training institutions. Corporate governance institutions structure the relationship between investors and firms. They are central to much of the work on this literature (Gourevitch, 2003; Gourevitch & Xxxxx, 2005; Gourevitch & Xxxxx, 2002) and have a powerful impact on the structure of the economy as a whole. Inter-firm linkage institutions structure the relationships among firms, both horizontally and vertically. Linkages and linkage institutions such as business associations are often cited as important by the devel- opment literature (Brimble & Xxxxx, 2007).9 I expect that there will be a lag in the 9I anticipate that corporate governance institutions will be the most pliable, that inter-firm linkage institutions will take longer to develop, and that worker training institutions will take the longest to develop. This is not so much because corporate governance institutions ought to be especially easy to develop, but because the complementarities noted in the VoC literature may make some institutions more essential than others. Hall and Soskice argue that each of the economic governance institutions may strengthen and reinforce each of the others. While this seems to be true, it may be that some have a...
Dependent Variables. The dependent variable in these analyses was the surgeon’s training and experience score. This score is the sum of ranked characteristics that serve as a marker of physician training or experience. A characteristic with a rank of 1 indicates sub-optimal training or experience; a rank of 2 indicates moderate level of training or experience; and a rank of 3 indicates optimal training or experience. The characteristics and their ranked classifications for these analyses included the number of surgeon’s board certifications: none (1 point), one (2 points) and two (3 points); subspecialty training: general surgeon (1 point), colorectal surgeon (2 points), and colorectal surgical oncologist (3 points); location of surgeon’s medical school: foreign (1 point), unknown (2 points), and U.S. (3 points); and surgeon’s colon cancer case volume tertile during study period 2001-2005 (based on the total number of claims for colon cancer surgery among patients in this study during study period 2001-2005): first through third tertile assigned 1-3 points, respectively. Each of the score components has been linked to patient outcomes and/or delivery of optimal treatment in earlier studies [7, 9, 10-13,16, 17]. After the points assigned for each characteristic are summed, a summary score is obtained (possible range 4-12) with higher scores indicating increased quality of a surgeon’s training and experience.
Dependent Variables. Table 5 describes dependent variables of teacher well-being that consist of four items and teacher turnover intentions consisting of number of years remaining in the profession and intentions to change schools. Teacher well-being in the TALIS 2018 dataset is defined as T3WELS: Workplace well-being and stress. It includes four items that measure the concept (e.g., TT3G51C “My job negatively impacts my mental health”). Well-being items are measured on a four-point scale from 1 to 4, with ‘‘1’’ standing for ‘‘Not at all” and ‘‘4’’ for ‘‘A lot”. The dependent variable of teacher turnover intentions is comprised of two items only: (a) years continuing being a teacher (TT3G50) where teachers had to indicate the number of years they intend to stay in the profession and (b) feeling I would like to change to another school if that were possible - T (TT3G53C) where items are measured on a four-point scale from 1 to 4, with ‘‘1’’ standing for ‘‘Strongly disagree” and ‘‘4’’ for ‘‘Strongly agree”. Table 2 Classification of Items Related to Demographic Variables Construct TALIS item Code Gender Teacher gender TT3G01 Age Teacher age TCHAGEGR Background and qualifications Highest level of formal education completed TT3G03 Table 3 Classification of Items Related to Independent Variables (Teacher-level Factors) Construct TALIS item Code School climate– Teacher’s perceived discipline T3DISC When the lesson begins, I have to wait quite a long time for students to quieten down TT3G41A Students in this class take care to create a pleasant learning atmosphere TT3G41B∗ I lose quite a lot of time because of students interrupting the lesson TT3G41C There is much disruptive noise in this classroom TT3G41D School climate– Teacher-student relations T3STUD In this school, teachers and students usually get on well with each other TT3G49A Most teachers in this school believe that the students’ well-being is important TT3G49B Most teachers in this school are interested in what students have to say TT3G49C If a student from this school needs extra assistance, the school provides it TT3G49D School climate– Participation among stakeholders T3STAKE This school provides staff with opportunities to actively participate in school decisions TT3G48A This school provides parents or guardians with opportunities to actively participate in school decisions TT3G48B This school provides students with opportunities to actively participate in school decisions TT3G48C This school has a culture of shared re...
Dependent Variables. As a main dependent variable for the pilot, we measured participants’ subjective experience of financial scarcity during the Household Task. To do so, we used a measure that is based on appraisals of financial scarcity and consists of 11 items (for a list of all items, see online supplement on the OSF). Following research on the experiential correlates of financial scarcity, these items concern the appraisal of having too little financial resources (Xxxx et al., 2012), a lack of control over one’s finances (Xxxxxxx et al, 2022b), whether participants felt capable of dealing with their financial situation (Xxxxxxx et al., 2022b), whether they were worried about their finances (Xx Xxxxxx & Xxxxxxxxx, 2020), whether they felt positive or negative about their finances, and whether they were stressed (Xxxxxxxxx & Xxxx, 2014). All items were framed to assess participants’ experience during the Household Task specifically and were measured on a seven-point Likert-scale, ranging from 1 = strongly disagree to 7 = strongly agree. The items showed very high internal consistency (Cronbach’s α = .96). Then, we asked participants about their actual financial situation to test whether the effectiveness of our manipulation was dependent upon participants’ real-life finances. Therefore, we used the Psychological Inventory of Financial Scarcity (PIFS; Xxx Xxxx et al., 2022) to assess how much financial scarcity participants experienced concerning their real-life finances, regardless of the results of the Household Task. The PIFS measures appraisals of insufficient resources and lack of control, in addition to rumination, worry, and short-term focus. The PIFS consists of twelve items measured on a seven-point Likert-scale, ranging from 1 = strongly disagree to 7 = strongly agree. In addition, as an objective measure of participants’ real-life finances, we included a single item to assess participants’ yearly net income (with the income brackets: less than £10k, £10k – £20k, […], £90k – £100k, £100k – £150k, more than £150k).6 Results and Discussion We conducted a one-way between-participants ANOVA with Financial Resources (debts, control, savings) as predictor and experienced financial scarcity as dependent variable. As hypothesized, Financial Resources had an effect on experienced financial scarcity, F(2, 147) = 133.34, p < .001, η² = .65. Planned contrasts revealed that participants in the debts condition experienced more financial scarcity (n = 49, M = 6.17, SD = 0.96) th...
Dependent Variables. Hospital participation in risk-related arrangements should in theory have a positive relationship on the amount of financial risk assumed. Accordingly, I measured hospital risk assumption by the amount of hospital net patient revenue tied to risk. This amount reflects the impact of hospitals participating in both mandatory and voluntary value-based payment program. I considered three separate measures for risk assumption: (1) total revenue at risk; (2) capitated revenue; and (3) shared-risk revenue. Total revenue at risk is the sum of a hospital’s revenue tied to capitated and shared-risk arrangements. The amount of capitated revenue reflects payments tied to capitation and revenue tied to shared-risk reflects payments tied to arrangements such as ACOs with shared savings. All revenue measures are considered as percentages of total net patient revenue.
Dependent Variables. 65 Survey Findings for Research Questions 1 and 2...................................................... 67 Findings for Research Question 3 ............................................................................. 71 School/Community Violence ........................................................................ 72 Physical Abuse .............................................................................................. 72 Bullying ......................................................................................................... 73 Complex Trauma ........................................................................................... 74 Sexual Abuse ................................................................................................. 74 Medical Trauma............................................................................................. 75 Summary.................................................................................................................... 76 CHAPTER 5: DISCUSSION ................................................................................................ 78 Summary of the Study ............................................................................................... 79
Dependent Variables. The research questions for this study included two dependent variables: teacher trauma (well-being) and teacher retention. The researcher ran a Xxxxxxx linear correlation between specific survey items to determine if there was an interrelationship between them, and they could be used for the two constructs, teacher trauma, and retention. Table 5.1 reports the Xxxxxxx correlation coefficients for the construct perceptions of teacher PTSD/Trauma, using survey items “experience painful images/thoughts/memories,” “avoid thoughts or feeling related to trauma,” “avoid places/people/conversations or activities that remind them of trauma,” “are irritable,” and “fearful or easily startled due to work-related trauma.”
