Role of Nursing in Healthcare Delivery Coursework

Role of Nursing in Healthcare Delivery - Coursework Example Therefore we can say that in this modern century the role of nursing managers has somewhat change now. In addition to the direct clinical and medical care, the nurses are involved in many other aspects of the health care industry. The additional duties may also include quality management and improvement, case management, data collection and analysis, insurance review analysis, patient educations and sometimes the regular training programs to train the rest of the medical staff (Cipriano, 2010). All of these additional tasks are included in the roles, duties or we can say responsibilities of a nurse manager. In modern times, the nurses are also named as the health providers and the health researchers. At higher level of nursing managers, the duties and the responsibilities of a nurse may change from others. A nurse manger may have to supervise all the staff and the hospital just to coordinate their activities. The budgeting activity may also fall on the shoulders of a nurse manger so that he or she can manage the allocated budget according to the proper planning. The hospital may get famous by the level of its services and the care, which they give to their patients; therefore, it is the role of the nurse manager to maintain the high quality or the standard of the health care services (Donovan, 2010). Nurses play an important or we must say a central role in the cost containment, quality and safety provision to the patients. Working at any level the role of nurse is to observe the current and emerging trends so that she or he can make innovation in their services and thus improve the quality of their health care provisions. The aim of the nurses and especially the nursing managers is to achieve the shared and mutual goals of efficiency and effectiveness in the practice (Tiffin, 2012). Tiffin, C. (2012), ‘Beyond the Bed Side: The Changing Roles of Nurses Today’, Huffington Post, Retrieved on July 22, 2014 from

Presidential Election of 1828 Essay Example for Free

Presidential Election of 1828 Essay A rematch between two bitter rivals, Andrew Jackson and John Quincy Adams, the presidential election of 1828 was highlighted by the split of electoral votes in New York and Maryland. Andrew Jackson had swept through the west, gaining every single state, and even got Pennsylvania. The winner from the election of 1824 by the ‘corrupt’ bargain, John Q. Adams, had gained the support of all the northeast states. However, the real surprise was the split electoral votes in Maryland and New York. The northern states loved Adams because he favored elites and their manufacturing industries. The south and west favored Jackson because he believed in equal opportunity for any citizen of the United States of America. Two states, Maryland and New York, did not give all their electoral votes to either Adams or Jackson, but were divided equally among the two. The reason for this split was both states were divided into districts that all had one vote. These districts could settle on who they wanted to give their electoral vote to. In every single other state, the electoral votes were decided upon by the state legislature, and once decided, all the electoral votes would be given to one candidate. However, in lone New York, the whole state could back Adams, but if one small self-sufficient farmer district wanted Jackson, then they could award their one electoral vote exclusively for him. So, if there was a dispute in states with a system like New York, the electoral vote could be split. The split between these two states showed how divided and diverse one state could be. If one little district went against the majority, it changes where the electoral votes are distributed, and can thus change the outcome of the election. New York and Maryland proved that one little group can make a large difference. These small changes made the election of 1828 unique, and actually exemplified how dissimilar one state’s people could be.

Fixed and random effects of panel data analysis

Fixed and random effects of panel data analysis Panel data (also known as longitudinal or cross-sectional time-series data) is a dataset in which the behavior of entities are observed across time. With panel data you can include variables at different levels of analysis (i.e. students, schools, districts, states) suitable for multilevel or hierarchical modeling. In this document we focus on two techniques use to analyze panel data:_DONE_ Fixed effects Random effects FE explore the relationship between predictor and outcome variables within an entity (country, person, company, etc.). Each entity has its own individual characteristics that may or may not influence the predictor variables (for example being a male or female could influence the opinion toward certain issue or the political system of a particular country could have some effect on trade or GDP or the business practices of a company may influence its stock price). When using FE we assume that something within the individual may impact or bias the predictor or outcome variables and we need to control for this. This is the rationale behind the assumption of the correlation between entitys error term and predictor variables. FE remove the effect of those time-invariant characteristics from the predictor variables so we can assess the predictors net effect. _DONE_ Another important assumption of the FE model is that those time-invariant characteristics are unique to the individual and should not be correlated with other individual characteristics. Each entity is different therefore the entitys error term and the constant (which captures individual characteristics) should not be correlated with the others. If the error terms are correlated then FE is no suitable since inferences may not be correct and you need to model that relationship (probably using random-effects), this is the main rationale for the Hausmantest (presented later on in this document). The equation for the fixed effects model becomes: Yit= ÃŽÂ ²1Xit+ ÃŽÂ ±i+ uit[eq.1] Where ÃŽÂ ±i(i=1à ¢Ã¢â€š ¬Ã‚ ¦.n) is the unknown intercept for each entity (nentity-specific intercepts). Yitis the dependent variable (DV) where i= entity and t= time. Xitrepresents one independent variable (IV), ÃŽÂ ²1 is the coefficient for that IV, uitis the error term _DONE_ Random effects assume that the entitys error term is not correlated with the predictors which allows for time-invariant variables to play a role as explanatory variables. In random-effects you need to specify those individual characteristics that may or may not influence the predictor variables. The problem with this is that some variables may not be available therefore leading to omitted variable bias in the model. RE allows to generalize the inferences beyond the sample used in the model. To decide between fixed or random effects you can run a Hausman test where the null hypothesis is that the preferred model is random effects vs. the alternative the fixed effects (see Green, 2008, chapter 9). It basically tests whether the unique errors (ui) are correlated with the regressors, the null hypothesis is they are not. Testing for random effects: Breusch-Pagan Lagrange multiplier (LM)The LM test helps you decide between a random effects regression and a simple OLS regression. The null hypothesis in the LM test is that variances across entities is zero. This is, no significant difference across units (i.e. no panel effect). Here we failed to reject the null and conclude that random effects is not appropriate. This is, no evidence of significant differences across countries, therefore you can run a simple OLS regression. EC968 Panel Data Analysis Steve Pudney ISER University of Essex 2007 Panel data are a form of longitudinal data, involving regularly repeated observations on the same individuals Individuals may be people, households, firms, areas, etc Repeat observations may be different time periods or units within clusters (e.g. workers within firms; siblings within twin pairs)+DONE_ Some terminology A balanced panel has the same number of time observations (T) on each of the n individuals An unbalanced panel has different numbers of time observations (Ti) on each individual A compact panel covers only consecutive time periods for each individual there are no gaps Attrition is the process of drop-out of individuals from the panel, leading to an unbalanced and possibly non-compact panel A short panel has a large number of individuals but few time observations on each, (e.g. BHPS has 5,500 households and 13 waves) A long panel has a long run of time observations on each individual, permitting separate time-series analysis for each_DONE_ Advantages of panel data With panel data: à ¢Ã¢â€š ¬Ã‚ ¢ We can study dynamics à ¢Ã¢â€š ¬Ã‚ ¢ The sequence of events in time helps to reveal causation à ¢Ã¢â€š ¬Ã‚ ¢ We can allow for time-invariant unobservable variables BUTà ¢Ã¢â€š ¬Ã‚ ¦ à ¢Ã¢â€š ¬Ã‚ ¢ Variation between people usually far exceeds variation over time for an individual à ¢Ã¢â‚¬ ¡Ã¢â‚¬â„¢ a panel with T waves doesnt give T times the information of a cross-section à ¢Ã¢â€š ¬Ã‚ ¢ Variation over time may not exist or may be inflated by measurement error à ¢Ã¢â€š ¬Ã‚ ¢ Panel data imposes a fixed timing structure; continuoustime survival analysis may be more informative Panel Data Analysis Advantages and Challenges Cheng Hsiao May 2006 IEPR WORKING PAPER 06.49 Panel data or longitudinal data typically refer to data containing time series observations of a number of individuals. Therefore, observations in panel data involve at least two dimensions; a cross-sectional dimension, indicated by subscript i, and a time series dimension, indicated by subscript t. However, panel data could have a more complicated clustering or hierarchical structure. For instance, variable y may be the measurement of the level of air pollution at station _ in city j of country i at time t (e.g. Antweiler (2001), Davis (1999)). For ease of exposition, I shall confine my presentation to a balanced panel involving N cross-sectional units, i = 1, . . .,N, over T time periods, t = 1, . . ., T._DONE_ There are at least three factors contributing to the geometric growth of panel data studies. (i) data availability, (ii) greater capacity for modeling the complexity of human behavior than a single cross-section or time series data, and (iii) challenging methodology. Advantages of Panel Data Panel data, by blending the inter-individual differences and intra-individual dynamics have several advantages over cross-sectional or time-series data: (i) More accurate inference of model parameters. Panel data usually contain more degrees of freedom and more sample variability than cross-sectional data which may be viewed as a panel with T = 1, or time series data which is a panel with N = 1, hence improving the efficiency of econometric estimates (e.g. Hsiao, Mountain and Ho-Illman (1995)._DONE_ (ii) Greater capacity for capturing the complexity of human behavior than a single cross-section or time series data. These include: (ii.a) Constructing and testing more complicated behavioral hypotheses. For instance, consider the example of Ben-Porath (1973) that a cross-sectional sample of married women was found to have an average yearly labor-force participation rate of 50 percent. These could be the outcome of random draws from a homogeneous population or could be draws from heterogeneous populations in which 50% were from the population who always work and 50% never work. If the sample was from the former, each woman would be expected to spend half of her married life in the labor force and half out of the labor force. The job turnover rate would be expected to be frequent and 3 the average job duration would be about two years. If the sample was from the latter, there is no turnover. The current information about a womans work status is a perfect predictor of her future work status. A cross-sectional data is not able to distinguish between these two possibilities, but panel data can because the sequential observations for a number of women contain information about their labor participation in different subintervals of their life cycle. Another example is the evaluation of the effectiveness of social programs (e.g. Heckman, Ichimura, Smith and Toda (1998), Hsiao, Shen, Wang and Wang (2005), Rosenbaum and Rubin (1985). Evaluating the effectiveness of certain programs using cross-sectional sample typically suffers from the fact that those receiving treatment are different from those without. In other words, one does not simultaneously observe what happens to an individual when she receives the treatment or when she does not. An individual is observed as either receiving treatment or not receiving treatment. Using the difference between the treatment group and control group could suffer from two sources of biases, selection bias due to differences in observable factors between the treatment and control groups and selection bias due to endogeneity of participation in treatment. For instance, Northern Territory (NT) in Australia decriminalized possession of small amount of marijuana in 1996. Evaluating the effects of decriminalization on marijuana smoking behavior by comparing the differences between NT and other states that were still non-decriminalized could suffer from either or both sorts of bias. If panel data over this time period are available, it would allow the possibility of observing the before- and affect-effects on individuals of decriminalization as well as providing the possibility of isolating the effects of treatment from other factors affecting the outcome. 4 (ii.b) Controlling the impact of omitted variables. It is frequently argued that the real reason one finds (or does not find) certain effects is due to ignoring the effects of certain variables in ones model specification which are correlated with the included explanatory variables. Panel data contain information on both the intertemporal dynamics and the individuality of the entities may allow one to control the effects of missing or unobserved variables. For instance, MaCurdys (1981) life-cycle labor supply model under certainty implies that because the logarithm of a workers hours worked is a linear function of the logarithm of her wage rate and the logarithm of workers marginal utility of initial wealth, leaving out the logarithm of the workers marginal utility of initial wealth from the regression of hours worked on wage rate because it is unobserved can lead to seriously biased inference on the wage elasticity on hours worked since initial wealth is likely to be correlated with wage rate. However, since a workers marginal utility of initial wealth stays constant over time, if time series observations of an individual are available, one can take the difference of a workers labor supply equation over time to eliminate the effect of marginal utility of initial wealth on hours worked. The rate of change of an individuals hours worked now depends only on the rate of change of her wage rate. It no longer depends on her marginal utility of initial wealth._DONE_ (ii.c) Uncovering dynamic relationships. Economic behavior is inherently dynamic so that most econometrically interesting relationship are explicitly or implicitly dynamic. (Nerlove (2002)). However, the estimation of time-adjustment pattern using time series data often has to rely on arbitrary prior restrictions such as Koyck or Almon distributed lag models because time series observations of current and lagged variables are likely to be highly collinear (e.g. Griliches (1967)). With panel 5 data, we can rely on the inter-individual differences to reduce the collinearity between current and lag variables to estimate unrestricted time-adjustment patterns (e.g. Pakes and Griliches (1984))._DONE_ (ii.d) Generating more accurate predictions for individual outcomes by pooling the data rather than generating predictions of individual outcomes using the data on the individual in question. If individual behaviors are similar conditional on certain variables, panel data provide the possibility of learning an individuals behavior by observing the behavior of others. Thus, it is possible to obtain a more accurate description of an individuals behavior by supplementing observations of the individual in question with data on other individuals (e.g. Hsiao, Appelbe and Dineen (1993), Hsiao, Chan, Mountain and Tsui (1989)). (ii.e) Providing micro foundations for aggregate data analysis. Aggregate data analysis often invokes the representative agent assumption. However, if micro units are heterogeneous, not only can the time series properties of aggregate data be very different from those of disaggregate data (e.g., Granger (1990); Lewbel (1992); Pesaran (2003)), but policy evaluation based on aggregate data may be grossly misleading. Furthermore, the prediction of aggregate outcomes using aggregate data can be less accurate than the prediction based on micro-equations (e.g., Hsiao, Shen and Fujiki (2005)). Panel data containing time series observations for a number of individuals is ideal for investigating the homogeneity versus heterogeneity issue. (iii) Simplifying computation and statistical inference. Panel data involve at least two dimensions, a cross-sectional dimension and a time series dimension. Under normal circumstances one would expect that the 6 computation of panel data estimator or inference would be more complicated than cross-sectional or time series data. However, in certain cases, the availability of panel data actually simplifies computation and inference. For instance: (iii.a) Analysis of nonstationary time series. When time series data are not stationary, the large sample approximation of the distributions of the least-squares or maximum likelihood estimators are no longer normally distributed, (e.g. Anderson (1959), Dickey and Fuller (1979,81), Phillips and Durlauf (1986)). But if panel data are available, and observations among cross-sectional units are independent, then one can invoke the central limit theorem across cross-sectional units to show that the limiting distributions of many estimators remain asymptotically normal (e.g. Binder, Hsiao and Pesaran (2005), Levin, Lin and Chu (2002), Im, Pesaran and Shin (2004), Phillips and Moon (1999)). (iii.b) Measurement errors. Measurement errors can lead to under-identification of an econometric model (e.g. Aigner, Hsiao, Kapteyn and Wansbeek (1985)). The availability of multiple observations for a given individual or at a given time may allow a researcher to make different transformations to induce different and deducible changes in the estimators, hence to identify an otherwise unidentified model (e.g. Biorn (1992), Griliches and Hausman (1986), Wansbeek and Koning (1989)). (iii.c) Dynamic Tobit models. When a variable is truncated or censored, the actual realized value is unobserved. If an outcome variable depends on previous realized value and the previous realized value are unobserved, one has to take integration over the truncated range to obtain the likelihood of observables. In a dynamic framework with multiple missing values, the multiple 7 integration is computationally unfeasible. With panel data, the problem can be simplified by only focusing on the subsample in which previous realized values are observed (e.g. Arellano, Bover, and Labeager (1999)). The advantages of random effects (RE) specification are: (a) The number of parameters stay constant when sample size increases. (b) It allows the derivation of efficient 10 estimators that make use of both within and between (group) variation. (c) It allows the estimation of the impact of time-invariant variables. The disadvantage is that one has to specify a conditional density of ÃŽÂ ±i given x Ëœ _ i = (x Ëœ it, . . ., x ËœiT ), f(ÃŽÂ ±i | x Ëœ i), while ÃŽÂ ±i are unobservable. A common assumption is that f(ÃŽÂ ±i | x Ëœi) is identical to the marginal density f(ÃŽÂ ±i). However, if the effects are correlated with x Ëœit or if there is a fundamental difference among individual units, i.e., conditional on x Ëœit, yit cannot be viewed as a random draw from a common distribution, common RE model is misspecified and the resulting estimator is biased. The advantages of fixed effects (FE) specification are that it can allow the individualand/ or time specific effects to be correlated with explanatory variables x Ëœ it. Neither does it require an investigator to model their correlation patterns. The disadvantages of the FE specification are: (a) The number of unknown parameters increases with the number of sample observations. In the case when T (or N for ÃŽÂ »t) is finite, it introduces the classical incidental parameter problem (e.g. Neyman and Scott (1948)). (b) The FE estimator does not allow the estimation of the coefficients that are time-invariant. In order words, the advantages of RE specification are the disadvantages of FE specification and the disadvantages of RE specification are the advantages of FE specification. To choose between the two specifications, Hausman (1978) notes that if the FE estimator (or GMM), ˆ ÃƒÅ½Ã‚ ¸_DONE_ ËœFE, is consistent whether ÃŽÂ ±i is fixed or random and the commonly used RE estimator (or GLS), ˆ ÃƒÅ½Ã‚ ¸ ËœRE, is consistent and efficient only when ÃŽÂ ±i is indeed uncorrelated with x Ëœit and is inconsistent if ÃŽÂ ±i is correlated with x Ëœit. The advantage of RE specification is that there is no incidental parameter problem. The problem is that f(ÃŽÂ ±i | x Ëœ i) is in general unknown. If a wrong f(ÃŽÂ ±i | x Ëœi) is postulated, maximizing the wrong likelihood function will not yield consistent estimator of ÃŽÂ ² Ëœ . Moreover, the derivation of the marginal likelihood through multiple integration may be computationally infeasible. The advantage of FE specification is that there is no need to specify f(ÃŽÂ ±i | x Ëœ i). The likelihood function will be the product of individual likelihood (e.g. (4.28)) if the errors are i.i.d. The disadvantage is that it introduces incidental parameters. Longitudinal (Panel and Time Series Cross-Section) Data Nathaniel Beck Department of Politics NYU New York, NY 10012 [emailprotected] http://www.nyu.edu/gsas/dept/politics/faculty/beck/beck home.html Jan. 2004 What is longitudinal data? Observed over time as well as over space. Pure cross-section data has many limitations (Kramer, 1983). Problem is that only have one historical context. (Single) time series allows for multiple historical context, but for only one spatial location. Longitudinal data repeated observations on units observed over time Subset of hierarchical data observations that are correlated because there is some tie to same unit. E.g. in educational studies, where we observe student i in school u. Presumably there is some tie between the observations in the same school. In such data, observe yj,u where u indicates a unit and j indicates the jth observation drawn from that unit. Thus no relationship between yj,u and yj,u0 even though they have the same first subscript. In true longitudinal data, t represents comparable time. Generalized Least Squares An alternative is GLS. If is known (up to a scale factor), GLS is fully efficient and yields consistent estimates of the standard errors. The GLS estimates of _ are given by (X0à ¢Ã‹â€ Ã¢â‚¬â„¢1X) à ¢Ã‹â€ Ã¢â‚¬â„¢1X0à ¢Ã‹â€ Ã¢â‚¬â„¢1Y (14) with estimated covariance matrix (X0à ¢Ã‹â€ Ã¢â‚¬â„¢1X) à ¢Ã‹â€ Ã¢â‚¬â„¢1 . (15) (Usually we simplify by finding some trick to just do a simple transform on the observations to make the resulting variance-covariance matrix of the errors satisfy the Gauss-Markov assumptions. Thus, the common Cochrane-Orcutt transformation to eliminate serial correlation of the errors is almost GLS, as is weighted regression to eliminate heteroskedasticity.) The problem is that is never known in practice (even up to a scale factor). Thus an estimate of , ˆ , is used in Equations 14 and 15. This procedure, FGLS, provides consistent estimates of _ if ˆ  is estimated by residuals computed from consistent estimates of _; OLS provides such consistent estimates. We denote the FGLS estimates of _ by Ëœ_. In finite samples FGLS underestimates sampling variability (for normal errors). The basic insight used by Freedman and Peters is that X0à ¢Ã‹â€ Ã¢â‚¬â„¢1X is a (weakly) concave function of . FGLS uses an estimate of , ˆ , in place of the true . As a consequence, the expectation of the FGLS variance, over possible realizations of ˆ , will be less than the variance, computed with the . This holds even if ˆ  is a consistent estimator of . The greater the variance of ˆ , the greater the downward bias. This problem is not severe if there are only a small number of parameters in the variance-covariance matrix to be estimated (as in Cochrane-Orcutt) but is severe if there are a lot of parameters relative to the amount of data. Beck TSCS Winter 2004 Class 1 8 ASIDE: Maximum likelihood would get this right, since we would estimate all parameters and take those into account. But with a large number of parameters in the error process, we would just see that ML is impossible. That would have been good. PANEL DATA ANALYSIS USING SAS ABU HASSAN SHAARI MOHD NOR Faculty of Economics and Business Universiti Kebangsaan Malaysia [emailprotected] FAUZIAH MAAROF Faculty of Science Universiti Putra Malaysia [emailprotected] 2007 Advantages of panel data According to Baltagi (2001) there are several advantages of using panel data as compared to running the models using separate time series and cross section data. They are as follows: Large number of data points 2)Increase degrees of freedom reduce collinearity 3) Improve efficiency of estimates and 4) Broaden the scope of inference The Econometrics of Panel Data Michel Mouchart 1 Institut de statistique Università © catholique de Louvain (B) 3rd March 2004 1 text book Statistical modelling : benefits and limita- tions of panel data 1.5.1 Some characteristic features of P.D. Object of this subsection : features to bear in mind when modelling P.D. à ¢Ã¢â€š ¬Ã‚ ¢ Size : often N (] of individual(s)) is large Ti (size of individual time series) is small thus:N >> Ti BUT this is not always the case ] of variables is large (often: multi-purpose survey) à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ Sampling : often individuals are selected randomly Time is not rotating panels split panels _ : individuals are partly renewed at each period à ¢Ã¢â€š ¬Ã‚ ¢ à ¢Ã¢â€š ¬Ã‚ ¢ à ¢Ã¢â€š ¬Ã‚ ¢ non independent data among data relative to a same individual: because of unobservable characteristics of each individual among individuals : because of unobservable characteristics common to several individuals between time periods : because of dynamic behaviour CHAPTER 1. INTRODUCTION 10 1.5.2 Some benefits from using P.D. a) Controlling for individual heterogeneity Example : state cigarette demand (Baltagi and Levin 1992) à ¢Ã¢â€š ¬Ã‚ ¢ Unit : 46 american states à ¢Ã¢â€š ¬Ã‚ ¢ Time period : 1963-1988 à ¢Ã¢â€š ¬Ã‚ ¢ endogenous variable : cigarette demand à ¢Ã¢â€š ¬Ã‚ ¢ explanatory variables : lagged endogenous, price, income à ¢Ã¢â€š ¬Ã‚ ¢ consider other explanatory variables : Zi : time invariant religion ( ± stable over time) education etc. Wt state invariant TV and radio advertising (national campaign) Problem : many of these variables are not available This is HETEROGENEITY (also known as frailty) (remember !) omitted variable ) bias (unless very specific hypotheses) Solutions with P.D. à ¢Ã¢â€š ¬Ã‚ ¢ dummies (specific to i and/or to t) WITHOUT killing the data à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ differences w.r.t. to i-averages i.e. : yit 7! (yit à ¢Ã‹â€ Ã¢â‚¬â„¢  ¯yi.)_DONE_ CHAPTER 1. INTRODUCTION 11 b) more information data sets à ¢Ã¢â€š ¬Ã‚ ¢ larger sample size due to pooling _ individual time dimension In the balanced case: NT observations In the unbalanced case: P1_i_N Ti observations à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ more variability ! less collinearity (as is often the case in time series) often : variation between units is much larger than variation within units_DONE_ c) better to study the dynamics of adjustment à ¢Ã¢â€š ¬Ã‚ ¢ distinguish repeated cross-sections : different individuals in different periods panel data : SAME individuals in different periods à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ cross-section : photograph at one period repeated cross-sections : different photographs at different periods only panel data to model HOW individuals ajust over time . This is crucial for: policy evaluation life-cycle models intergenerational models_DONE_ CHAPTER 1. INTRODUCTION 12 d) Identification of parameters that would not be identified with pure cross-sections or pure time-series: example 1 : does union membership increase wage ? P.D. allows to model BOTH union membership and individual characteristics for the individuals who enter the union during the sample period. example 2 : identifying the turn-over in the female participation to the labour market. Notice: the female, or any other segment ! i.e. P.D. allows for more sophisticated behavioural models e) à ¢Ã¢â€š ¬Ã‚ ¢ estimation of aggregation bias à ¢Ã¢â€š ¬Ã‚ ¢Ãƒ ¢Ã¢â€š ¬Ã‚ ¢ often : more precise measurements at the micro level Comparing the Fixed Effect and the Ran- dom Effect Models 2.4.1 Comparing the hypotheses of the two Models The RE model and the FE model may be viewed within a hierarchical specification of a unique encompassing model. From this point of view, the two models are not fundamentally different, they rather correspond to different levels of analysis within a unique hierarchical framework. More specifically, from a Bayesian point of view, where all the variables (latent or manifest) and parameters are jointly endowed with a (unique) probability measure, one CHAPTER 2. ONE-WAY COMPONENT REGRESSION MODEL 37 may consider the complete specification of the law of (y, ÃŽÂ ¼, _ | Z, ZÃŽÂ ¼) as follows: (y | ÃŽÂ ¼, _, Z, ZÃŽÂ ¼) _ N( Z_ _ + ZÃŽÂ ¼ÃƒÅ½Ã‚ ¼, _2 I(NT)) (2.64) (ÃŽÂ ¼ | _, Z, ZÃŽÂ ¼) _ N(0, _2 ÃŽÂ ¼ I(N)) (2.65) (_ | Z, ZÃŽÂ ¼) _ Q (2.66) where Q is an arbitrary prior probability on _ = (_, _2 , _2 ÃŽÂ ¼). Parenthetically, note that this complete specification assumes: y _2 ÃŽÂ ¼ | ÃŽÂ ¼, _, _2 , Z, ZÃŽÂ ¼ ÃŽÂ ¼(_, Z, ZÃŽÂ ¼) | _2 ÃŽÂ ¼ The above specification implies: (y | _, Z, ZÃŽÂ ¼) _ N( Z_ _ , _2 ÃŽÂ ¼ ZÃŽÂ ¼ Z0ÃŽÂ ¼ + _2 I(NT)) (2.67) Thus the FE model, i.e. (2.64), considers the distribution of (y | ÃŽÂ ¼, _, Z, ZÃŽÂ ¼) as the sampling distribution and the distributions of (ÃŽÂ ¼ | _, Z, ZÃŽÂ ¼) and (_ | Z, ZÃŽÂ ¼) as prior specification. The RE model, i.e. (2.67), considers the distribution of (y | _, Z, ZÃŽÂ ¼) as the sampling distribution and the distribution of (_ | Z, ZÃŽÂ ¼) as prior specification. Said differently, in the RE model, ÃŽÂ ¼ is treated as a latent (i.e. not obervable) variable whereas in the FE model ÃŽÂ ¼ is treated as an incidental parameter. Moreover, the RE model is obtained from the FE model through a marginalization with respect to ÃŽÂ ¼. These remarks make clear that the FE model and the RE model should be expected to display different sampling properties. Also, the inference on ÃŽÂ ¼ is an estimation problem in the FE model whereas it is a prediction problem in the RE model: the difference between these two problems regards the difference in the relevant sampling properties, i.e. w.r.t. the distribution of (y | ÃŽÂ ¼, _, Z, ZÃŽÂ ¼) or of (y | _, Z, ZÃŽÂ ¼), and eventually of the relevant risk functions, i.e. the sampling expectation of a loss due to an error between an estimated value and a (fixed) parameter or between a predicted value and the realization of a (latent) random variable. This fact does however not imply that both levels might be used indifferently. Indeed, from a sampling point of view: (i) the dimensions of the parameter spaces are drastically different. In the FE model, when N , the number of individuals, increases, the ÃŽÂ ¼i s being CHAPTER 2. ONE-WAY COMPONENT REGRESSION MODEL 38 incidental parameters also increases in number: each new individual introduces a new parameter.

Graduation Speech: Is County High the Best School in America? :: Graduation Speech, Commencement Address

Good evening students, faculty, staff, family, and friends. I was NOT surprised to see County High School missing from Newsweek's list of the Top 100 High Schools in America for 2012. The magazine based their ranking on Advanced Placement scores and Ivy League acceptances. In the accompanying article on high school life, the journalist attempted to find the real worth of a high school education. Certainly, the excruciatingly painful 3-hour-long tests cannot sum up an entire four years of experience as a high school student. Speaking from personal knowledge, I can tell you those tests are in no shape, manner, or form fun. The journalist posed a most interesting question regarding modern education, asking, "What happened to time for fun, football games, and memories of life in high school?" Surely if national rankings were based on the overall academic, athletic, and social experience of young adults, County High would find its name at the TOP of the list. In my mind, the perfect kind of high school experience takes place here at County High. Looking back on the past four years, I have done my fair share of complaining, but I wouldn't have wanted to complain about anything else with anyone else. We've pleaded with teachers and staff to let us into football games, basketball games, and school dances without our school ID's, simply because we didn't feel like carrying them. We've managed to sneak water bottles, milk shakes, and French fries out of the cafeteria, while also managing to get caught every now and then. We've argued that our pink and tan polos actually resemble shades of orange and white, and when it came to a senior class trip, we simply couldn't bear 180 more days without one. Over the past four years, I hope you have tried to find the best in every situation and look for the best in every person. To me, County High has been the greatest place to experience high school, and you, my classmates, my friends, my best friends, you have made the past four years enjoyable and survivable. I honestly couldn't have asked for a more intelligent, more talented, and more promising group of people to spend four entire years of my life with. Where else can you sign out of school early with 30 classmates simply to spend a sunny afternoon on the beach? Where else is there a basketball-player who refers to himself as "Big Mac" and a Prom Queen who answers to "Fatty McFat-Fat? Graduation Speech: Is County High the Best School in America? :: Graduation Speech, Commencement Address Good evening students, faculty, staff, family, and friends. I was NOT surprised to see County High School missing from Newsweek's list of the Top 100 High Schools in America for 2012. The magazine based their ranking on Advanced Placement scores and Ivy League acceptances. In the accompanying article on high school life, the journalist attempted to find the real worth of a high school education. Certainly, the excruciatingly painful 3-hour-long tests cannot sum up an entire four years of experience as a high school student. Speaking from personal knowledge, I can tell you those tests are in no shape, manner, or form fun. The journalist posed a most interesting question regarding modern education, asking, "What happened to time for fun, football games, and memories of life in high school?" Surely if national rankings were based on the overall academic, athletic, and social experience of young adults, County High would find its name at the TOP of the list. In my mind, the perfect kind of high school experience takes place here at County High. Looking back on the past four years, I have done my fair share of complaining, but I wouldn't have wanted to complain about anything else with anyone else. We've pleaded with teachers and staff to let us into football games, basketball games, and school dances without our school ID's, simply because we didn't feel like carrying them. We've managed to sneak water bottles, milk shakes, and French fries out of the cafeteria, while also managing to get caught every now and then. We've argued that our pink and tan polos actually resemble shades of orange and white, and when it came to a senior class trip, we simply couldn't bear 180 more days without one. Over the past four years, I hope you have tried to find the best in every situation and look for the best in every person. To me, County High has been the greatest place to experience high school, and you, my classmates, my friends, my best friends, you have made the past four years enjoyable and survivable. I honestly couldn't have asked for a more intelligent, more talented, and more promising group of people to spend four entire years of my life with. Where else can you sign out of school early with 30 classmates simply to spend a sunny afternoon on the beach? Where else is there a basketball-player who refers to himself as "Big Mac" and a Prom Queen who answers to "Fatty McFat-Fat?

Billy Prior-Character Study Essay

* Prior is introduced in chapter 5 and is portrayed as very defensive ‘he stares straight through you’ Pg 41. * Prior is a 22-year-old second lieutenant whose neurosis manifests itself, initially, through an inability to talk or remember the events, which have led to his breakdown. * His is of a working-class background but his mother has done her best to push her son up the social ladder, regardless whether this is best for him. * Billy’s mothers desire to better himself has resulted in a rebellious tendency to continually question authority which is shown in many of his dialogues with prior. * He is an inquisitive and intelligent man but because he ‘broke down’ he feels his courage failed him. * Prior has a flippant, cynical attitude and his discussions with rivers are frequently tinged with sarcasm both against rivers and his methods, and against the war in general * He describes his feelings at going into battle in sexual terms; his wartime nightmares and sexual dreams become confused and this brings on a sense of self-revulsion. This in turn, leads Prior further into depression. * Prior’s sexual frustrations are bound up with his war neurosis and his need to prove himself both as a soldier and a man. * Billy feels he has a duty to serve his country but on the other hand his ambitions preempt this duty as a reason to return to war. He clearly has a deep desire to return to the front and prove to himself that he is not a coward but this feeling is reserved a little by self-preservation * Prior hides his feelings and this is shown in his writing. He is at times is quite satirical â€Å"not tonight, Wilhelm. I’ve got a headache†? * On page 49 he shows a more aggressive side when he is very aggressive towards rivers * He develops a relationship with Sarah a ‘Geordie’ girl on pages 89+130 * Prior stood up Sarah in the book which she did not like but it was because he had got back late the previous night when on his last expedition with Sarah. * Billy begins to show his feelings on page 120 as he starts to open up a little more about his experiences. * â€Å"I wasn’t wearing the badge because I was with a girl† Pg.95 This shows how Prior is embarrassed about staying at Craiglockhart and shows his caring side. * â€Å"I find myself trying to impress you’ on page 64 this shows a more caring side to Billy Prior and more subtle side to him. This would also help the reader further empathise with prior. * On page 55 the reader gets to meet priors readers and it soon becomes clear that Billy does not get on well at all with his father â€Å"he seemed too have no feeling for his son except content†-â€Å"Must I? I’m not proud† This shows another side to prior and the lack of love he has felt whilst growing up. * By page 104 the hypnosis begins to work and gets to him and after a few meeting cry’s. Prior grabs Rivers by the arms and began butting him in the chest. Hard enough to hurt. This shows how Rivers touched a nerve in the hypnosis and bridges are finally being crossed. * On page 105 we get to find out about his war experiences. Prior is a complex and confused character, who is lost and desperately in need of help. It is only when he is finally prepared to admit this to himself that his road to recovery can begin.

Gun Control Essays (995 words) - Gun Politics In The United States

Gun Control Essays (995 words) - Gun Politics In The United States Gun Control Americans are faced with an ever-growing problem of violence. Our streets have become a battleground where the elderly are beaten for their social security checks, where terrified women are viciously attacked and raped. Each day teenage gangsters shoot it out for a patch of turf to sell their illegal drugs, and where innocent children are caught daily in the crossfire of drive-by shootings. We cannot ignore the damage that these criminals are doing to our society, and we must take actions to stop these horrors. However, the efforts by some misguided individuals to eliminate the legal ownership of firearms does not address the real problem at hand, and simply disarms the innocent law-abiding citizens who are most in need of a form of self-defense. To fully understand the reasons behind the gun control efforts, we must look at the history of our country, and the role firearms have played in it. The second amendment to the Constitution of the United States makes firearm ownership legal in this country. There were good reasons for this freedom, reasons which persist today. Firearms in the new world were used initially for hunting, and occasionally for self-defense. However, when the colonist felt that the burden of British oppression was too much for them to bear, they picked up their personal firearms and went to war. Standing against the British armies, these rebels found themselves opposed by the greatest military force in the world at that time. The founding fathers of the country understood that an armed populace aided in fighting off oppression. They made the right to keep and bear arms a constitutionally guaranteed right. Thomas Jefferson said in the draft of the Virginia Constitution No man shall ever be debarred the use of arms(n. pag.). To day Congress, claiming that they want to take guns out of the hands of criminals, have worked to pass legislation that would take the guns out of the hands of law-abiding citizens instead. The question is the efforts of gun control do not address the real problem of crime. Therefore, if we pass laws restricting ownership of firearms, which category of people does it affect? The simple answer is that gun control laws affect law- abiding citizens only. Criminals will continue to violate these new laws, they will continue to carry their firearms, and they will find their efforts at crime much easier when they know that their victims will be unarmed. An unarmed man stands little chance against an armed one. In many states, including Florida and Texas, citizens have stated that they want to preserve their right to carry firearms for self- defense. Since the late 1980's, Florida has been issuing concealed weapons permits to law-abiding citizens, and these citizens have been carrying their firearms to defend themselves from rampant crime. The result is that the incidence of violent crime has actually dropped in contrast to the national average. Previously, Florida had been leading the nation in this category, and the citizens of that state have welcomed the change (Florida State Firearm Laws n. pag.). Gun control advocates tried to claim that there would be bloodshed in the streets when these citizens were given the right to carry. They tried to claim that the cities of Florida would become like Dodge City with shootouts on every street corner, and duels over simple disagreements. These gun control advocates were wrong. More than 200,000 concealed carry permits have been issued so far, with only 36 of these permits revoked for improper use of a firearm (Facts You Can Use n.pag.). This statistic is easy to understand. It is the law-abiding citizens who are going through the process of getting concealed carry permits so that they may legally carry a firearm. The people who go through this legal process do not want to break the law, and they do not intend to break the law. The people who do intend to break the law will carry their guns whether or not the law allows them to do so. Today, criminals often carry illegal weapons, including sawed-off shotguns, machine guns, and homemade zip-guns, clearly showing their disregard for the current laws which make these items illegal. When they are caught, the courts regularly dismiss