Confirmatory factor analysis of the Principal Self-Efficacy Survey (PSES).

Confirmatory factor analysis of the Principal Self-Efficacy Survey (PSES). ABSTRACT This article describes the development and constructs validity ofthe Principal Self-Efficacy Survey (PSES PSES Pretreatment Standards for Existing Sources (US EPA)PSES P-Bit Severely Errored SecondsPSES Product Safety Engineering Society (IEEE)). The item selection was basedon the theoretical framework proposed by Bandura ban`dur´an. 1. A traditional Ukrainian stringed musical instrument shaped like a lute, having many strings. . Fourteen-itemsassessing two factors Instructional Leadership (nine items) andManagement Skills (five items) and a demographic questionnaire comprisedthe PSES. Items were scored on a 1 to 4 Likert-type scale. Participantswere two hundred eighty-four principals. Construct validity construct validity,n the degree to which an experimentally-determined definition matches the theoretical definition. wassupported by confirmatory factor analysis In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis. It is used to assess the the number of factors and the loadings of variables. using AMOS Amos(ā`məs), prophetic book of the Bible. The majority of its oracles are chronologically earlier than those of the Bible's other prophetic books. His activity is dated c.760 B.C. 5.0. In conclusion,the PSES provides a promising measure of principal perceptions of theirability to effectively function in the areas of instructional leadershipand management. INTRODUCTION Bandura (1997) defines self-efficacy as: "... beliefs inone's capabilities to organize and execute the courses of actionrequired to produce given attainments" (p.3). According to Bandura,self efficacy influences, (1) the courses of action people choose topursue, (2) how much effort people will put forth in a given endeavor,(3) how long they will persevere in the face of obstacles and failure,(4) people's resilience to adversity, (5) whether someone'sthought patterns are self-hindering or self-aiding and (6) how muchstress and depression is experienced in coping with taxing environmentaldemands. The central role of self-efficacy in human agency makes it animportant and useful construct for empirical research. Becauseself-efficacy is a task-specific construct (Bandura, 1997), any attemptto measure self-efficacy should be contextually sensitive to the settingin which the behaviors occur. A rich and robust body of literaturedocuments the relationships between self-efficacy beliefs for teachersand students and their relationship to teaching and learning (e.g.,Pajares, 1996; Tschannen-Moran, Hoy, and Hoy, 1998). However, aliterature search for journal articles on principal self-efficacy andinstructional effectiveness produced no articles specific to the topic.Currently there is tremendous interest in the role of the principal inaffecting substantive, long-term improvement in schools. For example,the federal government, in The No Child Left Behind Act The No Child Left Behind Act of 2001 (Public Law 107-110), commonly known as NCLB (IPA: /ˈnɪkəlbiː/), is a United States federal law that was passed in the House of Representatives on May 23, 2001 has weighed inwith a mandate that principals in poorly performing schools shall bereplaced if improvement is not forthcoming. Given the central role that principals are expected to perform inmaintaining quality teaching and learning environments in schools, it isimportant to begin to conceptualize and operationalize measures ofprincipal self-efficacy. The following sections detail the developmentof the Principal Self-Efficacy Survey (PSES) along with its attendantpsychometric psy��cho��met��rics?n. (used with a sing. verb)The branch of psychology that deals with the design, administration, and interpretation of quantitative tests for the measurement of psychological variables such as intelligence, aptitude, and properties. ITEM GENERATION The generation of items for the PSES used the rational-empiricalapproach to instrument development (Burisch, 1984). The rationalcomponent drew upon the knowledge and experience of professionalsworking as principals and the research literature to suggest potentialitems. The empirical component selected or rejected items based on theirpsychometric properties. The scale configuration was based on thetheoretical framework proposed by Bandura. Fourteen-items assessing twofactors Instructional Leadership (nine items) and Management Skills(five items) and a demographic questionnaire comprised the PSES. Itemswere scored on a 1 to 4 Likert-type scale. ITEM SELECTION The 14 items were then checked for violations of normalcy throughthe SPSS A statistical package from SPSS, Inc., Chicago (www.spss.com) that runs on PCs, most mainframes and minis and is used extensively in marketing research. It provides over 50 statistical processes, including regression analysis, correlation and analysis of variance. Statistical Package Version 11.0 (SPSS Inc., 2001), explorefunction. Items would be considered for elimination if they had a skewvalue equal or greater than two and kurtosis KurtosisA statistical measure used to describe the distribution of observed data around the mean.Notes:Used generally in the statistical field, it describes trends in charts. value equal or greater thanseven. PARTICIPANTS Two hundred and eighty-four principals returned completed and validsurveys representing twelve states (5 in the southeast, 2 in theMidwest, 2 in the west, 2 in the northeast, and Alaska). There are 74elementary schools, 30 middle schools, and 31 high schools representedin this study. Sixty-six percent of the respondents are males. Ethnicrepresentation included 83% white, 14% black, and 1.4% other. Nearly 47%of the respondents indicated that they have a master's degree plus30 hours and approximately 10% of respondents have an earned doctorate.The majority of the responses (54%) came from rural schools, while 17%were from suburban schools and 25% were from urban schools RESULTS Because missing data appeared to be randomly scattered among thevariables, a full information maximum likelihood (FIML FIML Full Information Maximum LikelihoodFIML Football Is My Life (fantasy football league)) imputation IMPUTATION. The judgment by which we declare that an agent is the cause of his free action, or of the result of it, whether good or ill. Wolff, Sec. 3. wasperformed to estimate missing data. The factor structures were examinedusing a confirmatory factor analysis. A series of models were tested inthe following order: (a) a single-factor g model in which all items werefree to load on only one common factor; (b) an orthogonal two-factormodel Two-factor modelUsually, Fischer Black's zero-beta version of the capital asset pricing model. It may also refer to another type of model whereby expected returns are generated by any two factors. in which each factor was set to be independent of each other; (c)a correlated two-factor model in which the factors were to each other.The first two models were included to aid in the assessment of thecorrelated two- factor model. The models were examined by AMOS version (5.0) maximum likelihoodfactor analysis (Arbuckle, 2004). The models were evaluated by a varietyof fit measures that are classified as absolute, relative, parsimonious par��si��mo��ni��ous?adj.Excessively sparing or frugal.parsi��mo ,and population discrepancy. Absolute fit measures assess how well theproposed interrelationships among the variables match theinterrelationships among the actual interrelationships. The measure ofabsolute fit used in this study was the chi-square test because AMOSdoes not provide other absolute measures when missing data is estimatedwith the FIML imputation procedure. Measures of relative fit compare thehypothesized model to the null model. The relative fit measures employedin this study were the Comparative Fit Index (CFI CFIabbr.cost, freight, and insurance ) (Bentler, 1990), theTucker-Lewis Index (TLI (Transport Level Interface) A common interface for transport services (layer 4 of the OSI model). It provides a common language to a transport protocol and allows client/server applications to be used in different networking environments. ) (Bentler and Bonett, 1980). Measures ofparsimonious fit attempt to determine if the overall fit of the modelhas been accomplished by overfitting the data. The parsimonious fitmeasure in this study was the chi-square divided by the degrees offreedom. Lastly, population discrepancy measures are estimates from thesample coefficients to the population coefficients. The populationdiscrepancy measure in this study was the Root Mean Square Error ofApproximation (RMSEA) (Browne and Cudeck, 1993). Models were compared byexamining differences in values of chi-square to identify statisticallysignificant variations among the models. The fit indices for the threemodels are presented in Table 1. The chi-square test for differences revealed that the correlatedtwo-factor model is superior to the other models. The correlatedtwo-factor model yielded acceptably high goodness of fit Goodness of fit means how well a statistical model fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e. indices (i.e.,> .99) for both the CFI and the TLI. The RMSEA achieved a value of.049 indicating a close fit between the sample coefficients and theestimated population coefficients. The correlation between the twofactors is .69 demonstrating discriminate validity. The factor loadings are provided in Table 2. All items loadedstatistically significantly (p < .01) and demonstrated practicalsignificance with loadings greater than .40 on their respective factors. CONCLUSION This study provides empirical evidence that the PSESoperationalizes the latent constructs of instructional leadership andmanagement skills for principals. Individual items demonstratedconstruct validity, (i.e., the items were shown to measure theirrespective hypothetical construct and factor loadings were allsignificant, p < .01). The instructional leadership and managementconstructs are both considered essential to principal effectiveness andas such, the PSES provides a promising measure for furtheringunderstanding of self-beliefs of principals. Because this research was exploratory in nature, further researchis suggested to replicate the initial results. Also, future researchshould attempt to determine if the factor structure holds for variouslevels of the principalship (i.e., elementary, middle, and high school).Future research incorporating other important elements of principalself-efficacy beliefs (e.g., conflict resolution) would also besuggested. Finally, it would be important to understand principalself-efficacy for instructional effectiveness within the broader contextof constructs known to be important for creating and facilitating aneffective learning environment in schools. With this in mind, futurestudies should investigate the relationships between principalself-efficacy and other important constructs such as school culture,teacher self-efficacy, and student self-efficacy. APPENDIX A PRINCIPAL SELF-EFFICACY SURVEY PRINCIPAL SURVEY INSTRUCTIONS This administrator survey asks you to make a series of judgmentsabout your experiences as a head administrator for a school. You areasked to read the following items and rate the strength of your beliefsin your abilities to attain the following outcomes. These items shouldbe answered from your perspective as a school principal working toproduce an effective teaching and learning environment. You are toindicate the degree to which you agree or disagree with each statementby darkening the appropriate oval. Scale 1=Very Weak Beliefs in My Abilities (VW) 2=Weak Beliefs in My Abilities (W) 3=Strong Beliefs in My Abilities (S) 4=Very Strong Beliefs in My Abilities (VS) STATEMENTS: My beliefs in my abilities to ... 1. influence teachers to utilize effective teaching and learningpractices are 2. provide effective modeling for teachers regarding effectiveteaching and learning practices are 3. use research on teaching and learning to guide strategicplanning for accomplishment of school goals are 4. plan effective activities and experiences which facilitateteachers' beliefs in their abilities to provide effective teachingand learning activities to their students are 5. use data collected from teacher observations to informschool-wide efforts for improving teaching and learning are 6. regularly perform effective observations of teachers are 7. stay abreast of current best practices for facilitatingeffective teaching and learning are 8. communicate needs and goals necessary to enhance effectiveinstructional effectiveness to faculty are 9. provide experiences that foster and facilitate high levels ofteacher motivation towards teaching and learning are 10. protect instructional time so that effective teaching andlearning can take place 11. facilitate an atmosphere that provides fair and consistentdiscipline for all students are 12. maintain healthy school/community relations are 13. maintain a school-wide atmosphere that is conducive to teachingand learning are 14. buffer teacher from unnecessary paperwork REFERENCES Arbuckle, J.L. (1999). Amos 4.0 User's Guide. Chicago: SmallWaters Corporation. Bandura, A. (1997). Self-efficacy: the exercise of control. NewYork New York, state, United StatesNew York,Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of : Freeman. Bandura, A. (2001). Guide for constructing self-efficacy scales(Revised). Available from Frank Pajares, Emory University. Bentler, P.M. (1990). Comparative fit indexes in structural models.Psychological Bulletin, 107, 238-246. Bentler, P.M. and Bonett, D.G. (1980). Significance tests andgoodness of fit in the analysis of covariance CovarianceA measure of the degree to which returns on two risky assets move in tandem. A positive covariance means that asset returns move together. A negative covariance means returns vary inversely. structures. PsychologicalBulletin, 88, 588-606. Browne, M.W. and Cudeck, R. (1993). Alternative ways of assessingmodel fit. In Bollen, K.A. and Long, J.S. [Eds.] Testing structuralequation models. Newbury Park, California The community of Newbury Park, California is located in the western portion of the City of Thousand Oaks and Casa Conejo, an unincorporated area of southern Ventura County. : Sage, 136-62. Burisch, M. (1984). Approaches to personality inventoryconstruction: a comparison of merits. American Psychologist, 39(3)214-227. Pajares, F. (1996). Self-efficacy beliefs in academic settings.Review of Educational Research, 4 (66) 4. 543 - 578. Tschannen-Moran, M.., Hoy, A.W., & Hoy, W. K. (1998). Teacherefficacy: Its meaning and measure. Review of Educational Research. 68(2)202 - 248. R. Wade Smith, Louisiana State University Louisiana State University and Agricultural and Mechanical College, generally known as Louisiana State University or LSU, is a public, coeducational university located in Baton Rouge, Louisiana and the main campus of the Louisiana State University System. A. J. Guarino, Auburn UniversityTable 1: Fit Indices for the Three ModelsFactor [chi square]Model [chi square] df / df CFI TLI RMSEASingle (g) 180.37 * 77 2.34 .993 .991 .069Orthogonal 218.60 * 77 2.84 .991 .987 .081Correlated 127.1 * 76 1.67 .997 .995 .049p < .05.Table 2. Item factor loadingsFactor Loadings of the Principal Efficacy SurveyItem Instructional Leadership Management SkillsQ1 .69Q2 .62Q3 .59Q4 .65Q5 .66Q6 .64Q7 .59Q8 .65Q9 .61Q10 .66Q11 .77Q12 .47Q13 .58Q14 .44