Dependent Variable. The Self-Report of Offending scale (Huizinga, Esbensen, & Xxxxxx 1991) was adapted in this dataset to measure the respondents’ reports of antisocial and illegal activities. Twenty-four items are used to assess aggressive crimes, income- generating crimes, and public order offenses. These offenses include vandalism, arson, set fire, burglary, shoplifting, received stolen property, used credit card illegally, stole car, sold marijuana, sold other drugs, carjacked, drove drunk, been paid by someone for sex, forced someone to have sex, killed someone, shot someone, shot at someone, robbery with weapon, robbery with no weapon, beaten someone, in fight, fight part of gang, and carried gun. An offending variety score was created, which represents the number of different delinquent acts committed in the previous 6 months2 -- coded from 0 (no delinquent acts) to 1.0 (all 22 acts were committed). Variety scores have been previously used to assess criminal activity. For example, Hindelang, Hirschi, and Weis (1981) use variety scores to index criminal activity and other studies have been published on the validity of variety scores (Xxxxxxxx & Xxxxxxx 1985, 1986; Xxxxxxx, XxxXxxxxx, & Xxxxxxx 2002). The variety score is used because it has the least skewed distribution of the self-report measures included in the dataset (see Appendix C).
Dependent Variable. Table 1 shows the results testing H1: Juvenile offenders who have (a) weak conventional bonds to society and (b) low self-control are more likely to be susceptible than those with strong conventional bonds to society and high self-control. Table 1 has an adjusted R2 of .09 and is statistically significant at the .000 level. Overall, the results suggest that there are several significant factors that influence susceptibility, which include parental monitoring, perceptions of chances for success, grades, self-control, gender, and ethnicity. The social bonds variable, parental monitoring, is significant holding all other variables constant. However, the relationship is in the opposite direction that is hypothesized, which suggests that juveniles with strong parental monitoring are more likely to be susceptible. I will discuss this more below. Perceptions of chances for success is also highly statistically significant. This suggests that adolescents who have a better outlook on the future are less susceptible to peer influence. In the model, grades is modestly statistically significant, holding all other variables constant. This suggests that adolescents who report better grades are less susceptible to peer influence. The other social bond variables, bonding to teachers and maternal warmth, are not significant predictors of susceptibility. The second part of the first hypothesis is also tested in this model. Self-control has a relatively strong effect when regressed onto susceptibility, holding all other constant, and the relationship is in the hypothesized direction. This suggests that juveniles with greater self-control are less susceptible to their peers. Finally, the results indicate that ethnicity, namely African American, is significant at the .01 level. The direction of the relationship suggests that African Americans are less likely to be susceptible than their white counterparts. Also, sex is statistically significant, but only at the .05 level. This suggests that males are more susceptible than their female counterparts in the sample.
Dependent Variable. The starting point of my dependent variable measurement is the definition of contagion that I have assumed. To recap, Kodres and Xxxxxxxx (2002, 772) define 13 See Appendix III for the distribution of daily data by country and SDDS subscription status; see Appendix IV for descriptive information on each nation in the sample, including SDDS subscription details, geographical region and market type. contagion as “a general price movement in one market resulting from a shock in another market.” Given the authors’ specification of price movement, my measurement of contagion must be based on the value of a financial instrument. I have chosen government bonds as the study’s financial stability indicator since bond returns are determined by investors’ perception of investment risk. Government bonds have been issued and their returns quoted for a significant amount of time, so bond data is readily available. Furthermore, bonds are essential to government operations and are issued and traded regularly. Accordingly, the bond market is highly liquid and bond returns are generally accurate, especially in emerging economies where trading volumes are high. Alternative measures of country risk that were considered were stock market indices and credit default swap (CDS) rates. These instruments would be legitimate measures of volatility, but this paper only uses bond data to keep the scope tractable. The metric I have chosen to measure sovereign bond returns is XX Xxxxxx’x Emerging Market Bond Index (EMBI). The index is a bond return index, so lower values denote increased risk. The EMBI’s “[w]ell-defined liquidity criteria ensure the index provides a fair and replicable benchmark,” so there is no need to control for market illiquidity or trading volumes (XX Xxxxxx). Finally, the EMBI has been widely employed in past research on contagion (Xxxxxxxx and Schmukler 2002; Alexander, et al. 2008; Glennester and Shin 2003; Xxxxxxxx, et al. 2003). A restriction of using the EMBI is that my sample is limited to a group of emerging markets that issue bonds and are quoted by the EMBI. However, these emerging markets are of the most interest in my theory and exhibit cross-country, cross-time variation on SDDS membership. To arrive at the final analysis of my contagion variable there were a series of intermediate steps. First, I transformed the EMBI index into daily percent change values. Then I conducted a regression of these daily changes with a variety of explanatory variable. The...
Dependent Variable. Audit Quality Based on Table 1, the regression equation in this study is as follows: Y = 0.081 + 0.237X1 + 0.681X2 - 0.144X3. From table 1, it shows that due professional care is proven to have a significant effect on audit quality with a value of tstat. 4.469> ttable 1.985, or a value of 0.000<0.05. Judging from the direction, due professional care has a positive effect on audit quality. Time budget pressure is proven to have a significant effect on audit quality with a value of tstat. 14.964> ttable 1.985, or a value of 0.000 <0.05. Here, time budget pressure has a positive effect on audit quality. Dysfunctional behavior is proven to have a significant effect on audit quality with value tstat. -2,096 > ttable 1,985, or value 0,039 < 0,05. Dysfunctional behavior has a negative effect on audit quality. Furthermore, based on Table 2 shows that due professional care, time budget pressure, and dysfunctional behavior together have an effect on audit quality with a value of Fstat. 298,826 > Ftable 2,70 and a significance value of 0.000 <0.05. Furthermore, based on Table 3 shows that the value of the coefficient (R) is 0.905, indicating that the relationship between variables is very strong, with the coefficient of determination (Adjusted R square) of 0.902 or 90.2%. This means that the variable due professional care, time budget pressure, and dysfunctional behavior can explain the audit quality variable by 90.2.8%, while the remaining 9.8% is explained by other variables outside this estimation model.
Dependent Variable. 3.2.2.1 Relational Norms Bilateral relationships are the domain of social exchange theory. Thus these are the expectations and behaviors which are evolved overtime as a result of interaction between two parties. The hazards of opportunism can be subsided in relational exchanges as a result of establishing long term relationships and helpful norms which consequently develop into a shared set of values with the softer ingredients of cooperation, solidarity, honesty (Samouel, 2007). Thus, norms are developed in the idiosyncratic setups over the passage of time. Therefore, we have taken the relational norms as a dependent variable in our study and we have investigated the impact of time duration over the relational norms based upon the criticality of logistic service performance. As we have come to know that there is positive association between a longer history of relationship duration and relational norms in buyer-seller relationships evolved over time as well as the current practices (Buvik and Halskau, 2001). In the business environment of Pakistan, it could be very interesting to study the relational norms as society may have different composition in various perspectives of established relational norms.
Dependent Variable binary variable on the decision to integrate
Dependent Variable. Given the hypothesis that CDF deployment increases the likelihood of COIN victory, COIN outcome is the dependent variable. Drawing on Stam (1996) and Xxxxx and Xxxxxx (2009), the variable can take on three discrete values: win, draw, or lose. Operationally, insurgencies end either as a result of a military defeat or by way of a negotiated settlement. In the event of a military outcome, an incumbent is considered to have lost if its armed forces are destroyed or evicted from the capital. Likewise, an incumbent is considered victorious if the insurgent organization is destroyed or substantially weakened. Whenever a decisive military outcome is not observed at an insurgency’s end, either a compromise settlement has been negotiated or one of the participants has conceded all rival demands. An insurgency is considered to have ended in a draw if the warring parties reached a compromise agreement in which the government conceded some, though not all, insurgent demands. The second possibility is that an insurgent gives up its armed struggle having received no government concessions or hostilities end because the incumbent concedes all or nearly all rebel demands. In such cases, the outcome is coded as a victory and defeat, respectively. In coding outcomes, I use a narrow definition of draws in that the insurgent movement must obtain either a power- sharing or autonomous arrangement. The possibility exists that a rebel organization’s initial aim is limited to a power-sharing or autonomous arrangement, the subsequent achievement of which may strike some observers as a defeat for the incumbent rather than a draw. However, given that bargaining demands are endogenous to estimates of relative power and resolve, coding such cases as victories would get at a different conceptual definition—the accuracy of belligerents’ estimates. In coding the dependent variable, I rely primarily on the Correlates of War Project’s (version 4.0) descriptions of each case (Xxxxxxx and Xxxxxx 2010). I subse- quently substantiate each coding using a number of other reference materials, includ- ing Xxxxxxxx and Xxxxxxx’x (2005) Encyclopedia of Wars, Xxxxxxxxxx’x (2002) Warfare and Armed Conflicts, Xxxxxxx’x (2001) and Ackermann, Xxxxxxxx, Xxxxx, Xxxxxx and Xxxxxxxx’x (2008) encyclopedias of world history, Xxxx’x (2007) Dictionary of Wars, as well as a large number of individual case histories. A codebook documenting the coding decision for each observation is included in Appendix A. As far ...
Dependent Variable. English language learning. 19Calderón Xxxx, Xxxx. Los fundamentos curriculares en la enseñanza del inglés a distancia: un acercamiento de la teoría y de la reflexión de la práctica educativa. Revista Educación. Edición: 2009: No 29. San Xxxx.
Dependent Variable. According to the study of previous literature, the structured air travel demand can be modeled as a function of Geo-economic and service-related characteristics of the Møre og Romsdal county. Such as the population of each airport within the located city, the travel frequency each airport offered, the size of airport etc. Moreover, the right side of this model includes those two types of variables that respectively influence the quantity of air travel demand for the four airports. Usually typical variables as the causing factors for air travel demand usually are population, income of passengers, employment of cities (metropolitan areas) and the distance between departure airport and destination airport. The air travel demand variation of alternatives in a city is mainly explained by affect factors’ characteristics of these alternatives. In other words, because of airport competition, air travel demand for an airport depends on the attractiveness of this airport’s Characteristics. Those characteristics can also affect the attractiveness to an airport compared to the competitor airport. Does the ground access distance and accesses time be beneficial and attractive factors for air travel demand? This will be examined in our study. Also there are some factors which affect the air travel demand commonly however not significant to the demand for a single airport city. Meanwhile there are some specific factors which can affect the air travel demand for a single airport city. According to the previous study of air travel behavior travel information in our dataset, we can observe that traveler behavior has a great impact on air travel demand and different regions have its own traveler behavior characteristic. Different Individuals use different decision-making process when choosing the best suited travel mode (Chou, 1992). In our study, the final demand function for the four airports should define the relationship between the travel frequency and certain factors. We will examine all the variables which are related to the travel demand and find the most significant variables. In other words, we will use the SPSS to examine the affecting factors’ significant level and do the adjustment for the model to get the most appropriate model for the air travel demand in Møre og Romsdal county.
