Life History Speech Essays

Life History Speech Essays Life History Speech Essay Life History Speech Essay Essay Topic: Notre Dame Life History Speech (Manuscript) INTRODUCTION Attention-Getting Statement: Some of us are not born with a silver spoon to eat from and must make the best of our surroundings. Did you ever look at how hard your parents worked for the little that they possessed? Is your job just a job that pay the bills yet offers little in the regards or self fulfillment. Credibility Statement: During the last thirty years I have had to make decisions which would ultimately decide my fate. One man has been there to answer the call for advice; my grandfather Walt. Connecting Statement: Most of us remember our grandparents as the ones who always loved us. Not parental love, unconditional Thesis Statement: My grandfather will not be written down in any history books; the men who fought by his side are now marked by a white cross Preview: I am now going to share with you the story of my stepfathers over-confidence as a youth, misfortunes throughout his life, and his eventual triumph. Transition: Ill now begin by describing the foundation of my stepfathers life, his youth. BODY Main Point #1: Growing up, Ed did not live in a typical household, unless you consider having a professional football star as your father typical. And if that were not bad enough, his uncle was also in the NFL at the time. Having such talent nearby molded his over-confidence in the sport of football. His parents met while his father played for the Pittsburgh Steelers and his mother was the football teams secretary. Soon after, Ed was born, practically with a football in his hand during the spring of 1967. As a youngster, he played in the backyard with his brother Matt, who would be born two years later. Ed would then go on to play on the mini football team for his hometown of Swoyersville. As he entered Wyoming Valley West High School, it was expected that he would play on their football team. He did, and after a successful four-year career as quarterback and tailback, the time had come to think about plans for college. Transition: Little did he know, that things would not go as planned, as Ill now explain. Main Point#2: Eds dream was to play for the University of Notre Dame; he cries every time he watches the move Rudy, and would naturally go on from there to play in the NFL. Being offered four scholarships, though none being from the school of his choice, Notre Dame would remain a dream. Instead of sitting around feeling sorry for himself, he would take what he and his parents thought was the best offer, and continue his career at the University of Delaware. He didnt get into the college that he wanted, but this never ruined his dream of the NFL, after all he had the football genes. He would then experience the biggest reality-check of his life with a serious back injury his freshman year. He would require surgery to repair a herniated disc, which would cause him to lose weight, speed, and size. By his sophomore year, he knew that Delaware was going to be the end of his football career. This was the finalization he feared that would take away all hope for a professional career. Transition: Let me now share how persistence helped him to obtain an enjoyable and satisfying life. Main Point #3: Ed was now at a crossroads in his life where he had to think about what he could do to stay in the sport of football without actually playing. He then decided if he could not play for a living, he would do the next best thing by coaching. Even though his fathers status couldnt get him into the NFL, his fathers connections would get him his first job. Beginning at Meyers High School as a part time substitute teacher and assistant coach, he moved onto land a permanent teaching position as well as head football coach for Wyoming Valley West. With the added responsibility of being a head coach comes a lot less time to spend with your family. This does not fly so well with my mother, but through it all, he still looks forward to getting up and going to work each and every day. Transition: In closing CONCLUSION Summary Statement: Today, I have shared with you the story of how one man rose to the top of his game in adolescence, met adversity in college, and ultimately gained personal success and happiness through perseverance. Concluding Remarks: After our interview, while still reflecting on all the highs and lows of his life, I then asked, Do you believe that everything happens for a reason? He quickly answered Yes, but its how you react to the missed plays in the game of life that makes you who you are.

Structural Equation Modeling

Structural Equation Modeling Structural equation modeling is an advanced statistical technique that has many layers and many complex concepts. Researchers who use structural equation modeling have a good understanding of basic statistics, regression analyses, and factor analyses. Building a structural equation model requires rigorous logic as well as a deep knowledge of the field’s theory and prior empirical evidence. This article provides a very general overview of structural equation modeling without digging into the intricacies involved. Structural equation modeling is a collection of statistical techniques that allow a set of relationships between one or more independent variables and one or more dependent variables to be examined. Both independent and dependent variables can be either continuous or discrete and can be either factors or measured variables. Structural equation modeling also goes by several other names: causal modeling, causal analysis, simultaneous equation modeling, analysis of covariance structures, path analysis, and confirmatory factor analysis. When exploratory factor analysis is combined with multiple regression analyses, the result is structural equation modeling (SEM). SEM allows questions to be answered that involve multiple regression analyses of factors. At the simplest level, the researcher posits a relationship between a single measured variable and other measured variables. The purpose of SEM is to attempt to explain â€Å"raw† correlations among directly observed variables. Path Diagrams Path diagrams are fundamental to SEM because they allow the researcher to diagram the hypothesized model, or set of relationships. These diagrams are helpful in clarifying the researcher’s ideas about the relationships among variables and can be directly translated into the equations needed for analysis. Path diagrams are made up of several principles: Measured variables are represented by squares or rectangles. Factors, which are made up of two or more indicators, are represented by circles or ovals. Relationships between variables are indicated by lines; lack of a line connecting the variables implies that no direct relationship is hypothesized. All lines have either one or two arrows. A line with one arrow represents a hypothesized direct relationship between two variables, and the variable with the arrow pointing toward it is the dependent variable. A line with an arrow at both ends indicates an unanalyzed relationship with no implied direction of effect. Research Questions Addressed by Structural Equation Modeling The main question asked by structural equation modeling is, â€Å"Does the model produce an estimated population covariance matrix that is consistent with the sample (observed) covariance matrix?† After this, there are several other questions that SEM can address. Adequacy of the model: Parameters are estimated to create an estimated population covariance matrix. If the model is good, the parameter estimates will produce an estimated matrix that is close to the sample covariance matrix. This is evaluated primarily with the chi-square test statistic and fit indices. Testing theory: Each theory, or model, generates its own covariance matrix. So which theory is best? Models representing competing theories in a specific research area are estimated, pitted against each other, and evaluated.Amount of variance in the variables accounted for by the factors: How much of the variance in the dependent variables is accounted for by the independent variables? This is answered through R-squared-type statistics. Reliability of the indicators: How reliable are each of the measured variables? SEM derives reliability of measured variables and internal consistency measures of reliability.Parameter estimates: SEM generates parameter estimates, or coefficients, f or each path in the model, which can be used to distinguish if one path is more or less important than other paths in predicting the outcome measure. Mediation: Does an independent variable affect a specific dependent variable or does the independent variable affect the dependent variable though a mediating variable? This is called a test of indirect effects. Group differences: Do two or more groups differ in their covariance matrices, regression coefficients, or means? Multiple group modeling can be done in SEM to test this. Longitudinal differences: Differences within and across people across time can also be examined. This time interval can be years, days, or even microseconds.Multilevel modeling: Here, independent variables are collected at different nested levels of measurement (for example, students nested within classrooms nested within schools) are used to predict dependent variables at the same or other levels of measurement. Weaknesses of Structural Equation Modeling Relative to alternative statistical procedures, structural equation modeling has several weaknesses: It requires a relatively large sample size (N of 150 or greater).It requires much more formal training in statistics to be able to effectively use SEM software programs.It requires well-specified measurement and conceptual model. SEM is theory driven, so one must have well-developed a priori models. References Tabachnick, B. G. and Fidell, L. S. (2001). Using Multivariate Statistics, Fourth Edition. Needham Heights, MA: Allyn and Bacon. Kercher, K. (Accessed November 2011). Introduction to SEM (Structural Equation Modeling). chrp.org/pdf/HSR061705.pdf