PSYCH - Psychology
Please see attached - need it in 8 hours. There are 2 parts. APA citation is a must for all parts. PART 1 Read the attached article and answer the following questions –(MINIMUM 300 WORDS) • Why are ordinal (or Likert) scales typically the form of data collected when studying human behavior within the fields of education, management, psychology, and related social and behavioral sciences? • Describe the differences found when using a normal-curve (NC) transformation versus using Likert scales for the psychometric analysis of diversity measures with entrepreneurial teams. • In the author's evaluation of three different reliability estimates (standard deviation, error variance, and unidimensionality), which was found to the preferred method and which was the least preferred? Why? • Be sure to use scholarly references to back your assertions and cite! PART 2 Responses needed for these 2 discussions (minimum 150 words for each discussion) Discussion 1 Likert scales are very insignificant when it comes to studying human behavior in various fields like education, behavioral sciences, and psychology for different reasons. The scale is ordinal primarily because of its association with discrete values like 1,2 and 3, just to mention a few (Wu & Leung, 2017). The data collected in this case constitute continuous values in a given interval. It is important to mention that considering the Likert scale, interval value is never accepted. In the context of studying human behavior is critical to use the ordinal or Likert scale since the associated data resonate with the nature of humans' engagements. The attitudes and related opinions are highly critical sources of data that require an ordinal scale to collect. It follows that the objectivity of acquiring viable and feasibility information based on the discrete values is very high and significantly possible. Most of the respondents dealing with the Likert scale are capable of comprehending the implications of this psychometric scale holistically. Data collected from using the scale is very pragmatic and assists immensely in addressing views and opinions (Wu & Leung, 2017). The researchers apply this firm of scale to understand the underlying views and opinions from associated parties effectively. The Likert scale is a very important aspect of explicitly understanding the perspectives that human behavior can adopt. It remains critical to affirm that the scale is essential in acquiring data that is generally relevant. The scale types such as the Guttman scale, Thurstone scale, and Bogardus scale are essential and efficient in ensuring that the ultimate goal of collecting discrete data is met. Various questions are provided when using the Likert scale that serves a major role in promoting authenticity and reliability in various dimensions. The differences that exist between using normal-curve (NC) transformation versus using Likert scales are very vivid and highly pragmatic since they are based on the provided facts. The NC transformation scale is associated with data that are closer or nearer to the underlying values in a situation where the normal curve assumption is upheld (Deng et al., 2015). Another key difference between the nominal curve transformation and the Likert scale is that the associated transformed data is usually less skewed considering their average value or on average. This is a very significant component and factor to consider since it can get utilized in situations where there is a need to measure various diversities. It follows that transformed data is relevant and essential when it comes to measuring different diversities, thereby giving the most reliable and effective outcome. In evaluating the three different reliability estimates that are standard deviation, error variance, and unidimensionality, the most effective and preferred method is the standard deviation. It Is important to mention that standard deviation provides an opportunity to engage in accurate and reliable estimations. The three diversity measures are supposed to embrace effectiveness in the application when it comes to statistical analysis. In this context, adopting an option that provides amicable results and operational excellence in various dimensions remains a very subjective step. Standard deviation is an alternative that is most preferred since it yields a highly efficient and accurate parameter estimate as oppose to other reliability estimates (Deng et al., 2015). On the other hand, the unidimensionality reliability estimates are least preferred following the nature of estimates that are associated with the same. This reliability estimate compared to the other counterpart options is the least and less reliable. Discussion 2 An ordinal scale is a scale (of measurement) that uses labels to classify cases into ordered classes. Ordinal scale tests are often used in social science settings because of how they can measure behavioral attitudes by asking people to respond to a series of statements topics, in terms of the extent to which they agree or disagree that would tap into the test taker’s intellectual and emotional components of attitudes. Ordinal scales tests, such as the Likert tests, are easy to interpret once the data has been received, and it allows test-takers to provide opinions based on the statement’s description. Likert scales also offer anonymity on self- administered questions to reduce social pressure and desirability bias (McLeod, 2019). The limitation of the tests is the questions in place can have the tendency for the test takers to present themselves in a generally favorable fashion. An example would be if the test is measured based on discrimination; test-takers would lie because they do not want to be seen as bullies or racists. Lovler and Miller (2020) describe Normal Curves (NC) as a theoretical distribution that exists in our imagination as perfect and symmetrical, with the scores concentrated on the middle than the tails. The psychometric analysis article describes those 117 individuals who took the diversity measurement tests, which is about 13 items to measure diversity. Researchers used 4 waves to distribute the tests, only to use the first wave tests because there were not many changes to the individuals’ answers from the other wave distribution. Researchers found that the sample skewness and the distribution curve provided a smaller critical value (Deng, et al., 2014). The Likert scale provided more marginal and well-fitted data by the one-factor model, which is easier to interpret to measure human characteristics. The article describes that the reliability estimates following the transformed data tend to be smaller than the Likert data (Deng, et al., 2014). Based on the descriptions provided, since NC’s tend to be on a hypothetical side and provide “perfect” scores, using the Likert scale can be realistic and provides reliability scores. The test takers can manipulate the Likert tests, but it provides a more measurement ability to convert or transform test scores in a meaningful unit (Lovler and Miller, 2020). See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/273586003 Psychometric Properties of Measures of Team Diversity With Likert Data Article in Educational and Psychological Measurement · May 2014 DOI: 10.1177/0013164414541275 CITATION 1 READS 68 3 authors, including: Some of the authors of this publication are also working on these related projects: Asymptotics View project Estimating equation View project L. Deng Beihang University (BUAA) 6 PUBLICATIONS 38 CITATIONS SEE PROFILE Ke-Hai Yuan University of Notre Dame 140 PUBLICATIONS 3,974 CITATIONS SEE PROFILE All content following this page was uploaded by Ke-Hai Yuan on 12 June 2015. The user has requested enhancement of the downloaded file. https://www.researchgate.net/publication/273586003_Psychometric_Properties_of_Measures_of_Team_Diversity_With_Likert_Data?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_2&_esc=publicationCoverPdf https://www.researchgate.net/publication/273586003_Psychometric_Properties_of_Measures_of_Team_Diversity_With_Likert_Data?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_3&_esc=publicationCoverPdf https://www.researchgate.net/project/Asymptotics-2?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_9&_esc=publicationCoverPdf https://www.researchgate.net/project/Estimating-equation?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_9&_esc=publicationCoverPdf https://www.researchgate.net/?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_1&_esc=publicationCoverPdf https://www.researchgate.net/profile/L_Deng?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_4&_esc=publicationCoverPdf https://www.researchgate.net/profile/L_Deng?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_5&_esc=publicationCoverPdf https://www.researchgate.net/institution/Beihang_University_BUAA?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_6&_esc=publicationCoverPdf https://www.researchgate.net/profile/L_Deng?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_7&_esc=publicationCoverPdf https://www.researchgate.net/profile/Ke-Hai_Yuan?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_4&_esc=publicationCoverPdf https://www.researchgate.net/profile/Ke-Hai_Yuan?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_5&_esc=publicationCoverPdf https://www.researchgate.net/institution/University_of_Notre_Dame?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_6&_esc=publicationCoverPdf https://www.researchgate.net/profile/Ke-Hai_Yuan?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_7&_esc=publicationCoverPdf https://www.researchgate.net/profile/Ke-Hai_Yuan?enrichId=rgreq-11e7c553fa44ae415ef62e9d2d029269-XXX&enrichSource=Y292ZXJQYWdlOzI3MzU4NjAwMztBUzoyMzk1ODAxNDYzMDI5NzZAMTQzNDEzMTc2NjY1Mg%3D%3D&el=1_x_10&_esc=publicationCoverPdf http://epm.sagepub.com/ Measurement Educational and Psychological http://epm.sagepub.com/content/early/2014/07/03/0013164414541275 The online version of this article can be found at: DOI: 10.1177/0013164414541275 published online 4 July 2014Educational and Psychological Measurement Lifang Deng, George A. Marcoulides and Ke-Hai Yuan Psychometric Properties of Measures of Team Diversity With Likert Data Published by: http://www.sagepublications.com at: can be foundEducational and Psychological MeasurementAdditional services and information for http://epm.sagepub.com/cgi/alertsEmail Alerts: http://epm.sagepub.com/subscriptionsSubscriptions: http://www.sagepub.com/journalsReprints.navReprints: http://www.sagepub.com/journalsPermissions.navPermissions: What is This? - Jul 4, 2014OnlineFirst Version of Record >> by guest on July 5, 2014epm.sagepub.comDownloaded from by guest on July 5, 2014epm.sagepub.comDownloaded from http://epm.sagepub.com/ http://epm.sagepub.com/content/early/2014/07/03/0013164414541275 http://www.sagepublications.com http://epm.sagepub.com/cgi/alerts http://epm.sagepub.com/subscriptions http://www.sagepub.com/journalsReprints.nav http://www.sagepub.com/journalsPermissions.nav http://epm.sagepub.com/content/early/2014/07/03/0013164414541275.full.pdf http://online.sagepub.com/site/sphelp/vorhelp.xhtml http://epm.sagepub.com/ http://epm.sagepub.com/ Article Educational and Psychological Measurement 1–23 � The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0013164414541275 epm.sagepub.com Psychometric Properties of Measures of Team Diversity With Likert Data Lifang Deng1, George A. Marcoulides2, and Ke-Hai Yuan3 Abstract Certain diversity among team members is beneficial to the growth of an organization. Multiple measures have been proposed to quantify diversity, although little is known about their psychometric properties. This article proposes several methods to evalu- ate the unidimensionality and reliability of three measures of diversity. To approxi- mate the interval scale required by the measures of diversity, a transformation on the Likert-item scores is proposed. Ridge maximum likelihood is used to deal with the issue of small sample size, and methods for evaluating the significance of the difference of two reliability estimates with correlated samples are also developed. Results with a real data set on entrepreneurial teams indicate that different measures of diversity may correspond to significantly different estimates of reliability. Results also indicate that diversity measures obtained with the transformed data tend to be more unidi- mensional than their counterparts obtained from Likert data. However, diversity mea- sures obtained from Likert data tend to yield greater reliability estimates. Among the three examined measures of diversity, the standard deviation is found to yield greater and more efficient reliability estimates than the others and is thus recommended. Keywords unidimensionality, reliability, normal-curve transformation, ridge structural equation modeling 1 Beihang University, Beijing, China 2 University of California, Santa Barbara, CA, USA 3University of Notre Dame, Notre Dame, IN, USA Corresponding Author: Ke-Hai Yuan, University of Notre Dame, 123a Haggar Hall, Notre Dame, IN 46556, USA. Email: [email protected] by guest on July 5, 2014epm.sagepub.comDownloaded from http://epm.sagepub.com/ Introduction The compositional diversity of team members within an organization has been shown to affect the performance and growth of the organization (Harrison & Klein, 2007; Van Knippenberg, De Dreu, & Homan, 2004). Among various kinds of diver- sity (e.g., demographic, informational, experiential, or personality attributes), not all have been determined to be beneficial to the growth of an organization. Some researchers have indicated that it is merely the differences between team members in terms of their skill level, knowledge, and perspectives that are needed to foster crea- tivity and innovation (e.g., Guzzo & Shea, 1992). However, the findings in the extant literature have not been consistent (Jackson, Joshi, & Erhardt, 2003; Stewart, 2006; Van Knippenberg et al., 2004; Webber & Donahue, 2001) and indicate that our understanding of diversity and its role is still relatively limited. To facilitate a better understanding of diversity, Harrison and Klein (2007) classified diversity into three distinctive types: separation, variety, and disparity. Separation is for the difference in position or opinion among team members; variety is used to describe diversity in expertise, knowledge, or experience; and disparity refers to inequality in status or resources held among team members. Such a classification allows researchers to identify different roles of different types of diversity. A variety of measures have also been proposed to quantify different types of diversity among individuals within a team. According to Harrison and Klein (2007), separation should be measured by either the standard deviation or the average of Euclidean distances, variety should be measured by the so-called Blau’s (1977) index or entropy (Teachman, 1980), and disparity should be measured by the coefficient of variation or the ratio of the average of the absolute differences over the mean. Harrison and Klein (2007) also discussed the type of scales required by each of these diversity measures and emphasized that measuring separation requires the observed data on team members to be at the interval scale, whereas measuring disparity requires the observed data to be at the ratio scale. However, in the study of human behavior within the fields of education, management, psychology, and related social and behavioral sciences, it is extremely difficult to obtain data at the ratio or even interval scales. What are typically obtained are data collected from a survey using questionnaires that are commonly only ordinal or Likert type. Nevertheless, research- ers still regularly apply procedures that require interval-scale data to ordinal data. For example, factor analysis is commonly applied to Likert data for item selection or scale development (Raykov & Marcoulides, 2011). Although such a practice may still yield interpretable results, a better method is to factor analyze the polychoric correlation matrix (Babakus, Ferguson, & Jöreskog, 1987). Given that the observed values in Likert data used to determine the above-mentioned diversity measures are somewhat arbitrary, we propose to transform them to avoid the arbitrariness. The transformation is based on threshold values under the normal curve, parallel to those used in estimating polychoric correlations (Olsson, 1979). We can call it the normal- curve (NC) transformation. Although the transformed data are still limited in number of values, we argue that they are closer to the conditions required by diversity 2 Educational and Psychological Measurement by guest on July 5, 2014epm.sagepub.comDownloaded from http://epm.sagepub.com/ measures than the commonly used Likert data. To see the effect of the transforma- tion, we will study the psychometric properties of several diversity measures when applied to both Likert and transformed data. Unidimensionality and reliability are probably the two most basic psychometric properties one has to consider for any scale or instrument. Unidimensionality implies that the statistical dependence among the items can be accounted for by a single underlying latent trait, and reliability informs about the degree to which the observed individual differences are indicative of true individual differences on the latent dimension of interest. Measures of diversity, especially those for measuring separa- tion with Likert data, are also subject to such properties if they aim to properly cap- ture any underlying trait. In particular, when measurements in a scale are not unidimensional, the empirical meaning of the scale will be different from the mean- ing assigned to it, which will create interpretational confounding (e.g., Anderson & Gerbing, 1982; Bagozzi, 1980; Burt, 1973, 1976). Reliability is equally important because, for measurements with a low reliability index, the observed values of the obtained measurements can be mostly due to random errors. Additionally, because the value of the determined reliability index sets a bound on validity (Allen & Yen, 1979), a high reliability (index) is a necessary condition for high validity (Raykov & Marcoulides, 2011). We hope that by studying the unidimensionality and reliability of different measures of diversity, the inconsistent findings obtained to date on the roles of diversity can be better understood. The methodological development presented in this article was motivated by the need to study the psychometric properties of diversity measures based on 13 Likert items administered to entrepreneurial teams. Because the number of teams plays the role of sample size, which is not sufficiently large, a method to deal with the issue of small sample sizes was also needed especially when using factor analysis to evaluate the unidimensionality of the diversity measures. For such a purpose, we make use of the ridge maximum likelihood (ML) method originally developed in Yuan and Chan (2008). This method has been shown to yield more accurate parameter estimates than the normal-distribution-based maximum likelihood (NML) even for normally distrib- uted data. We also develop methods for evaluating the significance of the difference of two reliability estimates with correlated samples. This enables us to determine whether different measures of diversity correspond to significantly different reliabil- ity estimates. If different diversity measures yield significantly different reliability estimates, then it is better to use the one that corresponds to the greatest reliability. In the next section, the methodological components for studying the unidimen- sionality and reliability of different measures of diversity are given, including the formulations of different diversity measures, the NC transformation, ridge ML, and standard error (SE) for difference of reliability estimates. A real data set with Likert scale and its analysis are presented in the following section. We conclude with a dis- cussion and recommendations. It is important to note that our focus is on the psycho- metric properties (unidimensionality and reliability) of different measures of Deng et al. 3 by guest on July 5, 2014epm.sagepub.comDownloaded from http://epm.sagepub.com/ diversity, not on interrater reliability issues (for further details on interrater reliabil- ity, see Algina, 1978; Schuster & Smith, 2002; Shrout & Fleiss, 1979). Methodology This section first introduces the three diversity measures that will be used in the anal- ysis of the real data. Then, the NC transformation is described. Ridge ML for factor analysis is reviewed next. Formulas for SE of the difference of two reliability esti- mates are developed at the end of this section. These measures and techniques will be used to analyze the real data in the subsequent section. Diversity Measures Let xijk be the score of person k on item j within team i, k = 1, 2, . . . , ni; j = 1, 2, . . . , p; i = 1, 2, . . . , N: Three measures of diversity derived from xijk will be studied. These are the average of absolute distances (aad) among team members, aadij = 2 ni(ni � 1) Xni�1 k1 = 1 Xni k2 = k1 + 1 jxijk1 � xijk2j; ð1Þ the average of absolute deviations from the mean (aadm) of team members, aadmij = 1 ni � 1 Xni k = 1 jxijk � �xijj, ð2Þ where �xij = Pni k = 1 xijk=ni; and the standard deviation (sd) among team members, sdij = ½ 1 ni � 1 Xni k = 1 (xijk � �xij)2�1=2: ð3Þ Two measures for separation were recommended by Harrison and Klein (2007). One is the standard deviation in which the denominator is ni instead of ni � 1: Another is the square root of the average of the squared Euclidean distances (xijk1 � xijk2 ) 2, in which k1 = k2 is not distinguished from k1 6¼ k2: According to Biemann and Kearney (2010), these measures may contain substantial bias due to including terms that are obviously 0 or without correcting for the loss of degrees of freedom. The diversity measure in (1) only includes the absolute distances for differ- ent team members, and degrees of freedom loss are accounted for in (2) and (3). Parallel to the average Euclidean distance (aed) in Harrison and Klein (2007) or Biemann and Kearney (2010), we define aedij as aedij = 2 ni(ni � 1) Xni�1 k1 = 1 Xni k2 = k1 + 1 (xijk1 � xijk2 ) 2 " #1=2 : 4 Educational and Psychological Measurement by guest on July 5, 2014epm.sagepub.comDownloaded from http://epm.sagepub.com/ Because aedij is proportional to sdij (see Hays, 1981) and any results of reliability and unidimensionality analysis of aedij would be identical to those of sdij, we do not separately examine aedij in this article. As we were not able to locate any references in the literature in which the aadmij in (2) or an index that is proportional to aadmij has been proposed to measure diver- sity, aadmij can be regarded as a new measure. The psychometric properties of the three measures, aad, aadm, and sd, will be examined through real data analysis in the following section. Quantities in the form of the average of absolute distances (e.g., aad) are not pre- sented as stand-alone measures in either Harrison and Klein (2007) or Biemann and Kearney (2010). Instead, they are divided by the team mean score for measuring dis- parity. Another measure for disparity recommended by Harrison and Klein (2007) is the coefficient of variation. Since these measures require the observed data to possess the properties of ratio scale, they may not be applicable to Likert data and will not be studied in this article. Similarly, variety will not be measured through Likert data and neither do we study Blau’s index or the entropy. Normal-Curve Transformation Since all three measures of diversity (aad, aadm, sd) are obtained by arithmetic oper- ations, they are ideally applicable to data that are of interval scale (Harrison & Klein, 2007). However, as indicated previously, measurements in the social and behavioral sciences are typically Likert or ordinal scale. To approximate interval scales, we pro- pose a transformation to Likert data in this subsection. With a total of Nt = PN i = 1ni individual observations and c categories for a given item, let q̂l be the proportion of observations 1 for category l: Following the conven- tion of polychoric correlations (Olsson, 1979), we may assume that, for each observed xijk , there is an underlying continuous variable zijk ;N (0, 1) such that xijk = l whenever zijk belongs to the interval (hl�1, hl�, where h0 \h1 \ � � � \hc are threshold values to be estimated. This implies that the probability for xijk = l is given by ql = F(hl ) � F(hl�1), l = 1, 2, . . . , c, where F(�) is the cumulative distribution function of z;N (0, 1), with h0 = � ‘ and hc = ‘: Thus, the marginal maximum likelihood estimate of hl is given by ĥl = F �1( Xl t = 1 q̂t ), l = 1, 2, . . . , c � 1: ð4Þ Based on this underlying NC assumption, we propose a transformation to the Likert xijk by yijk = (ĥl�1 + ĥl )=2 if xijk = l, l = 1, 2, . . . , c: ð5Þ Deng et al. 5 by guest on July 5, 2014epm.sagepub.comDownloaded from http://epm.sagepub.com/ Notice that there are only c � 1 finite values of ĥl in (4), and we cannot use ĥ0 = � ‘ or ĥc = ‘ because they will result in yijk = � ‘ when xijk = 1 or yijk = ‘ when xijk = c: We further propose to use ĥ0 = F �1(:5=Nt ) and ĥc = F �1(1 � :5=Nt ): ð6Þ The proposed values in (6) are equivalent to assigning a value of .5 to cells with zero number of observations in the analysis of contingency tables, because we can think of an extra category xijk = 0 below xijk = 1 and another extra category xijk = c + 1 above xijk = c, and both had zero number of observations. The proposed values in (6) are also similar to the so-called continuity correction in applying the central limit theo- rem to categorical data (Feller, 1945), where a step of .5 is used when jumping from 1 to the next whole number. Notice that the correction in (6) is for yijk to avoid being �‘ or ‘ whenever xijk = 1 or c: If the nominal number of categories is c but only c � 1 or fewer number of categories are observed, we may simply treat the unobserved categories in the middle as having probability of zero by just applying the correction to the end points of xijk: We need to note that the transformed yijk do not possess the property of interval scales, although they avoid the arbitrary nature of Likert data that assign consecutive whole numbers to ordered categories. Closely related to polychoric correlation, the rationale of the transformation in (5) depends heavily on the assumption of a normal curve underlying the observed frequencies. If the NC assumption holds, the yijk obtained by the NC transformation determined by equations (4), (5), and (6) is sim- ply the middle point of the interval zijk belongs, and thus represents the best predic- tion of the true value of zijk in the sense of smallest absolute mean difference. Applying each of the three diversity measures, aadij, aadmij, and sdij, to the trans- formed yijk yields three more measures of diversity. In the next section, their reliabil- ity and unidimensionality are examined, and the results are contrasted with those obtained based on Likert data. Ridge Maximum Likelihood for Factor Analysis With Small Sample Sizes As indicated in the previous section, the number of teams, N , plays the role of sample size when evaluating the psychometric properties of the diversity measures aad, aadm, and sd. Since it can be expensive to have a large N , we use ridge ML for factor analysis of the diversity measures in (1) to (3) when studying their unidimensionality. Unless all the ni are sufficiently large, the diversity measures in (1) to (3) cannot be regarded as normally distributed. As such, we expect ridge ML to work better than NML when factor analyzing the diversity measures. Let S be a sample covariance matrix of size p, and we are interested in modeling S = E(S) by a confirmatory factor model S(u) = LFL 0 + C, ð7Þ 6 Educational and Psychological Measurement by guest on July 5, 2014epm.sagepub.comDownloaded from http://epm.sagepub.com/ where L is a factor loading matrix, F is a factor correlation matrix, and C is a diago- nal matrix of measurement errors/uniquenesses. The widely used NML procedure for covariance structure analysis is to minimize FML(S, S(u)) = tr½SS�1(u)�� log jSS�1(u)j� p for parameter estimation. Let a . 0 be a small number and Sa = S + aI, with I being the identity matrix. The ridge ML developed in Yuan and Chan (2008) is to estimate ua by minimizing FML(Sa, S(ua)), and let the estimates be denoted by ûa: The corresponding estimates û for u are obtained by subtracting a from each of the elements of ûa corresponding to the diagonal elements of C, leaving the other elements of ûa unchanged. Standard errors of û are obtained by a sandwich-type covariance matrix, which accounts for the unknown underlying population distri- bution of the involved diversity measure. As for overall model evaluation, Yuan and Chan (2008) showed that, unless a = 0, TML = (N � 1)FML(Sa, S(ua)) does not asymptotically follow the nominal chi-square distribution x2df even if data are normally distributed. They developed a rescaled statistic TRML and an adjusted sta- tistic TAML: Parallel to the development for NML in Satorra and Bentler (1994), TRML asymptotically follows a distribution whose mean equals df , and TAML asymptotically follows a distribution whose mean and variance equal those of the approximating distribution. Since the details of ridge ML have already been described in Yuan and Chan (2008), no further elaboration is given here. Our pur- pose is to apply ridge ML to evaluate the unidimensionality of each of the three measures of diversity in (1) to (3) and to determine whether the corresponding sample covariance matrix can be reasonably fitted by a one-factor model. Following the recommendation of Yuan and Chan (2008), a = p=N is used in applying the ridge ML. In order to fully justify applying a factor analysis to each of the diversity measures, we do not need to assume that each of aadij, aadmij, or sdij is identically distributed across i = 1, 2, . . . , N: The development in Lee and Shi (1998) implies that the vector di = (aadi1, aadi2, . . . , aadip) 0 does not need to have the same population covariance as i varies. Since for both reliability and unidimensionality the analysis is based on the sample covariance matrix S of the corresponding diversity measures with the assump- tion E(S) = S, our study of the psychometric properties of di is for the population rep- resented by the sample di, i = 1, 2, . . . , N: We will further discuss this point in the concluding section. Standard Error for Difference of Two Reliability Estimates With Correlated Samples Among the many available estimates of reliability for equally weighted composite scores, coefficient alpha is most widely used in practice even though it can over- or underestimate the population reliability (Raykov, 1997). Another popular estimate is coefficient omega defined through the factor loadings and error variances by fitting Deng et al. 7 by guest on July 5, 2014epm.sagepub.comDownloaded from http://epm.sagepub.com/ the sample covariance matrix to a one-factor model (McDonald, 1999). Both are applicable when evaluating the reliability of the different diversity measures. Our interest is whether different diversity measures will yield significantly different relia- bility estimates. Thus, we need to have an estimate of the SE of the difference of two estimates of alpha or omega. When the two estimates are independent, the variance of the difference of the two estimates is simply the summation of the variances of the two estimates of alpha or omega. However, with respect to the three diversity measures, the variance or SE of the difference of two estimates of alpha or omega depends on their correlation. Since the SE for the difference of two reliability esti- mates with correlated samples will facilitate comparison of reliabilities in other con- texts, and the literature to date does not contain such a development, we provide more details for obtaining consistent SEs of the difference of two estimates of alpha and omega, respectively. We also present the necessary notation and formulas for calculating the SEs. The complete details leading to the calculation formulas are given in Appendices A and B. Let S = (sjk ) be a sample covariance matrix of size p, and s = vech(S) be the vector by stacking the elements in the lower-triangular part of S: Then, with p� = p(p + 1)=2, s is a vector of p�31, and the sample coefficient alpha is given by â = g(s) = p p � 1 (1 � Xp j = 1 sjj= Xp j = 1 Xp k = 1 sjk ) = p p � 1 (1 � a0s b0s ), where a is a p�31 vector whose elements are 1 corresponding to sjj and 0 elsewhere; and b is also a p�31 vector whose elements are 1 corresponding to sjj and 2 corre- sponding to sjk when j 6¼ k: For example, at p = 3, s = (s11, s21, s31, s22, s32, s33)0, a = (1, 0, 0, 1, 0, 1)0, and b = (1, 2, 2, 1, 2, 1)0: We need to have the Jacobian matrix or the matrix of derivatives of g(s) with respect to the elements of s, and it is given by _g(s) = � p p � 1 ½ 1 b0s a � a0s (b0s) 2 b�: With s1 = vech(S1) and s2 = vech(S2) from two correlated samples, standard error for â2 � â1 = g(s2) � g(s1) also involves the variance-covariance matrices of s1 and s2: Denote these by G11 = Var( ffiffiffi n p s1), G22 = Var( ffiffiffi n p s2), and G12 = Cov( ffiffiffi n p s1, ffiffiffi n p s2), where n = N � 1: These are consistently estimated by their sample counterparts, with details given in Appendix A. With the introduced notation, the result given in Appendix B implies that ffiffiffi n p ½(â2 � a2) � (â1 � a1)� is asymptotically normally dis- tributed with mean zero and variance consistently estimated by t̂ 2 a = _g 0(s1)Ĝ11 _g(s1) + _g 0(s2)Ĝ22 _g(s2) � 2 _g0(s1)Ĝ12 _g(s2): ð8Þ It follows from (8) that the SE of (â2 � â1) is consistently estimated by t̂a= ffiffiffi n p , which will be used in the next …
CATEGORIES
Economics Nursing Applied Sciences Psychology Science Management Computer Science Human Resource Management Accounting Information Systems English Anatomy Operations Management Sociology Literature Education Business & Finance Marketing Engineering Statistics Biology Political Science Reading History Financial markets Philosophy Mathematics Law Criminal Architecture and Design Government Social Science World history Chemistry Humanities Business Finance Writing Programming Telecommunications Engineering Geography Physics Spanish ach e. Embedded Entrepreneurship f. Three Social Entrepreneurship Models g. Social-Founder Identity h. Micros-enterprise Development Outcomes Subset 2. Indigenous Entrepreneurship Approaches (Outside of Canada) a. Indigenous Australian Entrepreneurs Exami Calculus (people influence of  others) processes that you perceived occurs in this specific Institution Select one of the forms of stratification highlighted (focus on inter the intersectionalities  of these three) to reflect and analyze the potential ways these ( American history Pharmacology Ancient history . Also Numerical analysis Environmental science Electrical Engineering Precalculus Physiology Civil Engineering Electronic Engineering ness Horizons Algebra Geology Physical chemistry nt When considering both O lassrooms Civil Probability ions Identify a specific consumer product that you or your family have used for quite some time. This might be a branded smartphone (if you have used several versions over the years) or the court to consider in its deliberations. Locard’s exchange principle argues that during the commission of a crime Chemical Engineering Ecology aragraphs (meaning 25 sentences or more). Your assignment may be more than 5 paragraphs but not less. INSTRUCTIONS:  To access the FNU Online Library for journals and articles you can go the FNU library link here:  https://www.fnu.edu/library/ In order to n that draws upon the theoretical reading to explain and contextualize the design choices. Be sure to directly quote or paraphrase the reading ce to the vaccine. Your campaign must educate and inform the audience on the benefits but also create for safe and open dialogue. A key metric of your campaign will be the direct increase in numbers.  Key outcomes: The approach that you take must be clear Mechanical Engineering Organic chemistry Geometry nment Topic You will need to pick one topic for your project (5 pts) Literature search You will need to perform a literature search for your topic Geophysics you been involved with a company doing a redesign of business processes Communication on Customer Relations. Discuss how two-way communication on social media channels impacts businesses both positively and negatively. Provide any personal examples from your experience od pressure and hypertension via a community-wide intervention that targets the problem across the lifespan (i.e. includes all ages). Develop a community-wide intervention to reduce elevated blood pressure and hypertension in the State of Alabama that in in body of the report Conclusions References (8 References Minimum) *** Words count = 2000 words. *** In-Text Citations and References using Harvard style. *** In Task section I’ve chose (Economic issues in overseas contracting)" Electromagnetism w or quality improvement; it was just all part of good nursing care.  The goal for quality improvement is to monitor patient outcomes using statistics for comparison to standards of care for different diseases e a 1 to 2 slide Microsoft PowerPoint presentation on the different models of case management.  Include speaker notes... .....Describe three different models of case management. visual representations of information. They can include numbers SSAY ame workbook for all 3 milestones. You do not need to download a new copy for Milestones 2 or 3. When you submit Milestone 3 pages): Provide a description of an existing intervention in Canada making the appropriate buying decisions in an ethical and professional manner. Topic: Purchasing and Technology You read about blockchain ledger technology. Now do some additional research out on the Internet and share your URL with the rest of the class be aware of which features their competitors are opting to include so the product development teams can design similar or enhanced features to attract more of the market. The more unique low (The Top Health Industry Trends to Watch in 2015) to assist you with this discussion.         https://youtu.be/fRym_jyuBc0 Next year the $2.8 trillion U.S. healthcare industry will   finally begin to look and feel more like the rest of the business wo evidence-based primary care curriculum. Throughout your nurse practitioner program Vignette Understanding Gender Fluidity Providing Inclusive Quality Care Affirming Clinical Encounters Conclusion References Nurse Practitioner Knowledge Mechanics and word limit is unit as a guide only. The assessment may be re-attempted on two further occasions (maximum three attempts in total). All assessments must be resubmitted 3 days within receiving your unsatisfactory grade. You must clearly indicate “Re-su Trigonometry Article writing Other 5. June 29 After the components sending to the manufacturing house 1. In 1972 the Furman v. Georgia case resulted in a decision that would put action into motion. Furman was originally sentenced to death because of a murder he committed in Georgia but the court debated whether or not this was a violation of his 8th amend One of the first conflicts that would need to be investigated would be whether the human service professional followed the responsibility to client ethical standard.  While developing a relationship with client it is important to clarify that if danger or Ethical behavior is a critical topic in the workplace because the impact of it can make or break a business No matter which type of health care organization With a direct sale During the pandemic Computers are being used to monitor the spread of outbreaks in different areas of the world and with this record 3. Furman v. Georgia is a U.S Supreme Court case that resolves around the Eighth Amendments ban on cruel and unsual punishment in death penalty cases. The Furman v. Georgia case was based on Furman being convicted of murder in Georgia. Furman was caught i One major ethical conflict that may arise in my investigation is the Responsibility to Client in both Standard 3 and Standard 4 of the Ethical Standards for Human Service Professionals (2015).  Making sure we do not disclose information without consent ev 4. Identify two examples of real world problems that you have observed in your personal Summary & Evaluation: Reference & 188. Academic Search Ultimate Ethics We can mention at least one example of how the violation of ethical standards can be prevented. Many organizations promote ethical self-regulation by creating moral codes to help direct their business activities *DDB is used for the first three years For example The inbound logistics for William Instrument refer to purchase components from various electronic firms. During the purchase process William need to consider the quality and price of the components. In this case 4. A U.S. Supreme Court case known as Furman v. Georgia (1972) is a landmark case that involved Eighth Amendment’s ban of unusual and cruel punishment in death penalty cases (Furman v. Georgia (1972) With covid coming into place In my opinion with Not necessarily all home buyers are the same! When you choose to work with we buy ugly houses Baltimore & nationwide USA The ability to view ourselves from an unbiased perspective allows us to critically assess our personal strengths and weaknesses. This is an important step in the process of finding the right resources for our personal learning style. Ego and pride can be · By Day 1 of this week While you must form your answers to the questions below from our assigned reading material CliftonLarsonAllen LLP (2013) 5 The family dynamic is awkward at first since the most outgoing and straight forward person in the family in Linda Urien The most important benefit of my statistical analysis would be the accuracy with which I interpret the data. The greatest obstacle From a similar but larger point of view 4 In order to get the entire family to come back for another session I would suggest coming in on a day the restaurant is not open When seeking to identify a patient’s health condition After viewing the you tube videos on prayer Your paper must be at least two pages in length (not counting the title and reference pages) The word assimilate is negative to me. I believe everyone should learn about a country that they are going to live in. It doesnt mean that they have to believe that everything in America is better than where they came from. It means that they care enough Data collection Single Subject Chris is a social worker in a geriatric case management program located in a midsize Northeastern town. She has an MSW and is part of a team of case managers that likes to continuously improve on its practice. The team is currently using an I would start off with Linda on repeating her options for the child and going over what she is feeling with each option.  I would want to find out what she is afraid of.  I would avoid asking her any “why” questions because I want her to be in the here an Summarize the advantages and disadvantages of using an Internet site as means of collecting data for psychological research (Comp 2.1) 25.0\% Summarization of the advantages and disadvantages of using an Internet site as means of collecting data for psych Identify the type of research used in a chosen study Compose a 1 Optics effect relationship becomes more difficult—as the researcher cannot enact total control of another person even in an experimental environment. Social workers serve clients in highly complex real-world environments. Clients often implement recommended inte I think knowing more about you will allow you to be able to choose the right resources Be 4 pages in length soft MB-920 dumps review and documentation and high-quality listing pdf MB-920 braindumps also recommended and approved by Microsoft experts. The practical test g One thing you will need to do in college is learn how to find and use references. References support your ideas. College-level work must be supported by research. You are expected to do that for this paper. You will research Elaborate on any potential confounds or ethical concerns while participating in the psychological study 20.0\% Elaboration on any potential confounds or ethical concerns while participating in the psychological study is missing. Elaboration on any potenti 3 The first thing I would do in the family’s first session is develop a genogram of the family to get an idea of all the individuals who play a major role in Linda’s life. After establishing where each member is in relation to the family A Health in All Policies approach Note: The requirements outlined below correspond to the grading criteria in the scoring guide. At a minimum Chen Read Connecting Communities and Complexity: A Case Study in Creating the Conditions for Transformational Change Read Reflections on Cultural Humility Read A Basic Guide to ABCD Community Organizing Use the bolded black section and sub-section titles below to organize your paper. For each section Losinski forwarded the article on a priority basis to Mary Scott Losinksi wanted details on use of the ED at CGH. He asked the administrative resident