International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document discusses correlational research designs. Correlational studies can show relationships between two variables to indicate cause and effect or predict future outcomes. There are three main types of correlational studies: observational research, survey research, and archival research. Correlational research allows analysis of relationships among many variables and provides correlation coefficients to measure direction and degree of relationships. Interpreting correlations involves scattergrams, correlation coefficients from -1 to 1, and determining explained variance through r-squared values. However, correlation does not necessarily prove causation as third variables could be the true cause.
Correlational research describes the linear relationship between two or more variables without attributing cause and effect. The correlation coefficient is used to measure the strength of this relationship on a scale from -1 to 1. Positive correlations indicate variables increase or decrease together, while negative correlations mean they change in opposite directions. Scatterplots visually depict the correlation by showing how paired values of different variables relate on a graph. The Pearson's r formula is commonly used to calculate correlation coefficients from sample data.
The document discusses correlational research, which examines the relationship between two or more variables without manipulating them. Correlational research involves collecting empirical data on variables from the same group of subjects. It determines if variables covary or occur together but does not prove causation. Positive correlations indicate variables increase together, while negative correlations mean they increase in opposite directions. Correlational research is often exploratory and can be used to identify variables for later experimental research.
Correlational research - Research Methodology - Manu Melwin Joymanumelwin
A correlation is simply defined as a relationship between two variables. The whole purpose of using correlations in research is to figure out which variables are connected.
Correlational research investigates the connection between two or more variables that are already present in a group. The aim is to identify if changes in one variable result in changes in another. There are three types: positive correlation where increases in one variable produce increases in another; negative correlation where increases in one produce decreases in another; and no correlation where the variables are independent. Common data collection methods include naturalistic observation, archival data, and surveys. Correlational research is non-experimental, backward-looking, and dynamic as relationships can change over time. The correlation coefficient indicates the strength and direction of relationships between variables.
This document discusses different types of research designs used in psychology, including correlational research, quasi-experimental research, and problems to look for in research studies. It provides examples of each type of research design. Correlational research seeks to establish relationships between variables without manipulation. Quasi-experimental research blends correlation and experimental approaches by examining interactions between individual differences and manipulations. Problems to look for include confounds, nonrandom sampling, failure to replicate, and lack of comparison groups.
Correlational research involves collecting data to determine the relationship between two or more variables. The degree of relationship is expressed as a correlation coefficient ranging from -1 to 1. A correlation does not imply causation. Correlational research can be used for relationship studies to examine how variables are related, or for prediction studies to predict scores on one variable based on others. Common mistakes include choosing illogical variables and small sample sizes that produce unreliable results.
This document discusses correlational research, which investigates the relationship between two continuous variables through statistical analysis. It provides an example of correlating IQ scores and student achievement to determine if higher IQ scores predict better achievement. The research would aim to establish a correlation between the variables and test the hypothesis that a correlation exists against the null hypothesis that there is no correlation. Scatter diagrams are also discussed as a way to visually depict the direction and strength of any correlation between two variables.
This document discusses correlational research designs. Correlational studies can show relationships between two variables to indicate cause and effect or predict future outcomes. There are three main types of correlational studies: observational research, survey research, and archival research. Correlational research allows analysis of relationships among many variables and provides correlation coefficients to measure direction and degree of relationships. Interpreting correlations involves scattergrams, correlation coefficients from -1 to 1, and determining explained variance through r-squared values. However, correlation does not necessarily prove causation as third variables could be the true cause.
Correlational research describes the linear relationship between two or more variables without attributing cause and effect. The correlation coefficient is used to measure the strength of this relationship on a scale from -1 to 1. Positive correlations indicate variables increase or decrease together, while negative correlations mean they change in opposite directions. Scatterplots visually depict the correlation by showing how paired values of different variables relate on a graph. The Pearson's r formula is commonly used to calculate correlation coefficients from sample data.
The document discusses correlational research, which examines the relationship between two or more variables without manipulating them. Correlational research involves collecting empirical data on variables from the same group of subjects. It determines if variables covary or occur together but does not prove causation. Positive correlations indicate variables increase together, while negative correlations mean they increase in opposite directions. Correlational research is often exploratory and can be used to identify variables for later experimental research.
Correlational research - Research Methodology - Manu Melwin Joymanumelwin
A correlation is simply defined as a relationship between two variables. The whole purpose of using correlations in research is to figure out which variables are connected.
Correlational research investigates the connection between two or more variables that are already present in a group. The aim is to identify if changes in one variable result in changes in another. There are three types: positive correlation where increases in one variable produce increases in another; negative correlation where increases in one produce decreases in another; and no correlation where the variables are independent. Common data collection methods include naturalistic observation, archival data, and surveys. Correlational research is non-experimental, backward-looking, and dynamic as relationships can change over time. The correlation coefficient indicates the strength and direction of relationships between variables.
This document discusses different types of research designs used in psychology, including correlational research, quasi-experimental research, and problems to look for in research studies. It provides examples of each type of research design. Correlational research seeks to establish relationships between variables without manipulation. Quasi-experimental research blends correlation and experimental approaches by examining interactions between individual differences and manipulations. Problems to look for include confounds, nonrandom sampling, failure to replicate, and lack of comparison groups.
Correlational research involves collecting data to determine the relationship between two or more variables. The degree of relationship is expressed as a correlation coefficient ranging from -1 to 1. A correlation does not imply causation. Correlational research can be used for relationship studies to examine how variables are related, or for prediction studies to predict scores on one variable based on others. Common mistakes include choosing illogical variables and small sample sizes that produce unreliable results.
This document discusses correlational research, which investigates the relationship between two continuous variables through statistical analysis. It provides an example of correlating IQ scores and student achievement to determine if higher IQ scores predict better achievement. The research would aim to establish a correlation between the variables and test the hypothesis that a correlation exists against the null hypothesis that there is no correlation. Scatter diagrams are also discussed as a way to visually depict the direction and strength of any correlation between two variables.
Research Methodology (Correlational Research) By Emeral & SarahEmeral Djunas
This document discusses correlational research design. Correlational research examines relationships between two or more variables by measuring them for a group of individuals and calculating a correlation coefficient. There are two main types: explanatory design which collects data at one time point to examine relationships between variables, and prediction design which collects data at two time points to predict outcomes based on predictor variables. Key aspects include using scatterplots to display variable relationships and determining the direction, form, and strength of relationships based on correlation coefficients. Common statistical analyses for correlational research include Pearson, Spearman, point-biserial, and multiple regression.
A Structural Equation Modelling of Entrepreneurial Education and Entrepreneu...inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document discusses the revised DEMATEL (Decision Making Trial and Evaluation Laboratory) method and provides examples of its application. It summarizes two examples where revised DEMATEL was used: (1) to analyze the causal relationships between seven criteria for improving hospital service quality based on patient surveys, and (2) to determine the weights of seven dimensions for evaluating innovation support systems in Taiwanese higher education based on expert surveys. It concludes that revised DEMATEL can produce results very similar or equal to the original DEMATEL method across all examples, except where the original method was infeasible. The closeness of the revised method's results depends on the epsilon value chosen.
Correlational designs allow researchers to examine potential relationships between two or more variables by collecting data on all variables at the same point in time without manipulating any variables. The development of correlational designs began in the late 19th century with pioneers like Karl Pearson and Yule developing correlation formulas and solutions. Key aspects of correlational designs include using statistical analyses like correlation coefficients, partial correlations, and multiple regression to analyze predictor and criterion variables and identify direction, form, and strength of associations between variables based on scatter plots and matrices. Conducting high quality correlational research requires adequate sample sizes, appropriate statistical tests, and clear interpretation and presentation of results.
This study investigated how specific, difficult goals impact attention and performance on a sustained attention task (SART) compared to vague "do your best" goals. 19 undergraduate students were assigned to either a specific goal condition or a "do your best" condition for the SART. Results showed that specific goals led to higher accuracy on target trials, indicating greater attentional focus, but did not affect performance on non-target trials or commitment to the task. The findings suggest that specific goals help maintain attention on goal-relevant stimuli during sustained attention tasks.
The document discusses correlational research methods, which examine relationships between two measured variables without manipulating variables. Correlational research can describe the direction (positive/negative), form (linear/nonlinear), and strength (magnitude) of relationships. Statistical analyses like Pearson correlation and regression can analyze correlational data. While correlational research allows observation of many variables, it cannot prove causation, as relationships may be due to other unmeasured variables.
Correlational research studies relationships between two or more variables without manipulating them. It can be used to predict outcomes and explain behaviors. Correlational studies describe relationships through correlation coefficients and scatterplots. More complex techniques include multiple regression, discriminant analysis, factor analysis, path analysis, and structural modeling. Correlational research aims to understand relationships, not prove causation. Threats to internal validity like subject characteristics, history, and testing must be controlled.
ABSTRACT : This paper critically examined a broad view of Structural Equation Model (SEM) with a view
of pointing out direction on how researchers can employ this model to future researches, with specific focus on
several traditional multivariate procedures like factor analysis, discriminant analysis, path analysis. This study
employed a descriptive survey and historical research design. Data was computed viaDescriptive Statistics,
Correlation Coefficient, Reliability. The study concluded that Novice researchers must take care of assumptions
and concepts of Structure Equation Modeling, while building a model to check the proposed hypothesis. SEM is
more or less an evolving technique in the research, which is expanding to new fields. Moreover, it is providing
new insights to researchers for conducting longitudinal investigations.
.
Correlation research examines the relationships between two or more non-manipulated variables without changing any variables. It can be used to predict scores on one variable based on scores of another predictor variable. Common techniques include explanatory design to look for associations between variables and prediction design to identify predictors of outcomes. Tools to analyze correlations include scatter plots, correlation coefficients, and regression analysis.
Correlational research designs examine relationships between two or more variables without manipulating any variables. They are used to describe and measure the degree of association between variables or sets of scores. There are two main types of correlational designs: explanatory/explanation designs which examine associations between variables, and prediction designs which identify predictor variables that can anticipate outcomes. Key aspects of correlational research include scatterplots, correlation coefficients, significance testing, and multiple variable techniques like partial correlation and multiple regression.
The document provides an overview of how to conduct a meta-analysis in psychology. It discusses that meta-analysis aims to quantitatively integrate the results of empirical studies on a given topic. There are typically 6 steps to conducting a meta-analysis, including defining research questions, conducting a literature search, coding studies, calculating effect sizes, statistical analysis and interpretation, and publication. It also provides an example of a meta-analysis that developed a comprehensive model of environmental behavior determinants based on combining several theories, using a meta-analytical structural equation modeling method.
Rule-Based Mamdani-Type Fuzzy Modeling of Perceived Stress, And Cortisol Resp...IJERA Editor
In this paper, Two Mamdani type fuzzy models (four inputs–one output and two inputs–one output) were developed to test the hypothesis that high job demands and low job control (job strain) are associated with elevated free cortisol levels early in the working day and with reduced variability across the day and to evaluate the contribution of anger expression to this pattern. The models were derived from multiple data sources including One hundred five school teachers (41 men and 64 women) classified 12 months earlier as high (N = 48) or low (N = 57) in job strain according to the demand/control model sampled saliva at 2-hour intervals from 8:00 to 8:30 hours to 22:00 to 22:30 hours on a working day. The quality of the model was determined by comparing predicted and actual fuzzy classification and defuzzification of the predicted outputs to get crisp values for correlating estimates with published values. A modified form of the Hamming distance measure is proposed to compare predicted and actual fuzzy classification. An entropy measure is used to describe the ambiguity associated with the predicted fuzzy outputs. The four input model predicted over 70% of the test data within one-half of a fuzzy class of the published data. The two input model predicted over 40% of the test data within one-half of a fuzzy class of the published data. Comparison of the models show that the four input model exhibited less entropy than the two input model.
Correlational research investigates potential relationships between variables without manipulating them. Researchers seek associations between variables to help explain behaviors or predict outcomes. If a strong enough relationship exists between two variables, scores on one can be used to predict the other. Correlational techniques include multiple regression, coefficient of multiple correlation, and factor analysis. Researchers must consider threats to internal validity like subject characteristics, instrumentation, and data collector bias.
Determinant of Teacher Performance
This research is focused on knowing whether there is an influence of principal's leadership, and organizational commitment on the performance of SMP Negeri teachers in Salatiga City. The main objective is to find and analyze the magnitude of the effect of each variable so that the factors causing the low performance of SMP Negeri teachers in Salatiga City can be identified so that the findings of this study are expected to be taken into consideration and reference in determining policies in an effort that leads to
improvement. Teacher performance. This research is quantitative research with a survey method. The sampling technique used is the probability sampling technique. Data analysis used linear regression analysis techniques for testing the research model. The result of the research is that the principal's leadership and organizational commitment have the most significant influence on teacher performance.
EEG correlates of maths anxiety: twin studyIlya Zakharov
The document discusses theories of mathematics anxiety (MA) and presents preliminary results from an ongoing twin study investigating MA. It summarizes three theories explaining how MA impacts working memory and performance. The twin study involves 25 twin pairs who complete cognitive tests and an EEG experiment with different cue types (arithmetic, algebraic, lexical). Preliminary results show stable ERP patterns across identical twins for different cue types and differences in P2 and P3b components when comparing high and low MA participants, with higher amplitudes for low MA. The results provide evidence that emotional expectations can reduce susceptibility to fearful stimuli by acting as a cognitive regulation strategy.
Correlational research examines relationships between two or more variables without manipulating them. It investigates whether changes in one variable are associated with changes in another. Correlational studies describe relationships using a correlation coefficient and can be used to predict scores on one variable based on scores on another. Common correlational techniques include scatterplots, regression analysis, and factor analysis. Threats to internal validity like subject characteristics, mortality, history, and instrumentation must be controlled.
This study examined how goals, empowerment, and communication relate to virtual worker performance. It surveyed 113 virtual workers and found that:
- Higher goal setting was associated with better perceived performance
- Greater empowerment correlated with increased perceived performance
- More communication linked to higher perceived performance
- Simultaneously improving goals, empowerment and communication significantly boosted perceived performance
However, the study was limited by using self-reported measures and a specific population. Further research with controlled experiments is needed to establish causation.
designed by
jennifer l
media arts and design undergrad
these slides were created for SIFE Simon Fraser (burnaby, bc, canada) to showcase our effects to promote environmental sustainability in the Greater Vancouver community at the SIFE Regionals Competitions 2010 in Calgary.
Economic factors led to declines in coal production and increases in natural gas and imports. The 1984 miners' strike reduced government support for the coal industry, forcing imports of cheaper foreign coal. Privatization of electricity and gas companies in the 1980s motivated profit-seeking over national interests. Environmental concerns over air pollution and the 1986 Chernobyl disaster raised issues with coal and nuclear power. Political factors included EU emissions reductions requirements and the 1997 Kyoto Summit agreement for the UK to lower CO2 emissions, pushing renewable energy development.
Inspiring introduction into sustainable lifestyle. The material is targeted for students above 15 years.
What is sustainable lifestyle? Why is it needed and what can I do for it?
Exercise and various links for further reading are also included.
Research Methodology (Correlational Research) By Emeral & SarahEmeral Djunas
This document discusses correlational research design. Correlational research examines relationships between two or more variables by measuring them for a group of individuals and calculating a correlation coefficient. There are two main types: explanatory design which collects data at one time point to examine relationships between variables, and prediction design which collects data at two time points to predict outcomes based on predictor variables. Key aspects include using scatterplots to display variable relationships and determining the direction, form, and strength of relationships based on correlation coefficients. Common statistical analyses for correlational research include Pearson, Spearman, point-biserial, and multiple regression.
A Structural Equation Modelling of Entrepreneurial Education and Entrepreneu...inventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document discusses the revised DEMATEL (Decision Making Trial and Evaluation Laboratory) method and provides examples of its application. It summarizes two examples where revised DEMATEL was used: (1) to analyze the causal relationships between seven criteria for improving hospital service quality based on patient surveys, and (2) to determine the weights of seven dimensions for evaluating innovation support systems in Taiwanese higher education based on expert surveys. It concludes that revised DEMATEL can produce results very similar or equal to the original DEMATEL method across all examples, except where the original method was infeasible. The closeness of the revised method's results depends on the epsilon value chosen.
Correlational designs allow researchers to examine potential relationships between two or more variables by collecting data on all variables at the same point in time without manipulating any variables. The development of correlational designs began in the late 19th century with pioneers like Karl Pearson and Yule developing correlation formulas and solutions. Key aspects of correlational designs include using statistical analyses like correlation coefficients, partial correlations, and multiple regression to analyze predictor and criterion variables and identify direction, form, and strength of associations between variables based on scatter plots and matrices. Conducting high quality correlational research requires adequate sample sizes, appropriate statistical tests, and clear interpretation and presentation of results.
This study investigated how specific, difficult goals impact attention and performance on a sustained attention task (SART) compared to vague "do your best" goals. 19 undergraduate students were assigned to either a specific goal condition or a "do your best" condition for the SART. Results showed that specific goals led to higher accuracy on target trials, indicating greater attentional focus, but did not affect performance on non-target trials or commitment to the task. The findings suggest that specific goals help maintain attention on goal-relevant stimuli during sustained attention tasks.
The document discusses correlational research methods, which examine relationships between two measured variables without manipulating variables. Correlational research can describe the direction (positive/negative), form (linear/nonlinear), and strength (magnitude) of relationships. Statistical analyses like Pearson correlation and regression can analyze correlational data. While correlational research allows observation of many variables, it cannot prove causation, as relationships may be due to other unmeasured variables.
Correlational research studies relationships between two or more variables without manipulating them. It can be used to predict outcomes and explain behaviors. Correlational studies describe relationships through correlation coefficients and scatterplots. More complex techniques include multiple regression, discriminant analysis, factor analysis, path analysis, and structural modeling. Correlational research aims to understand relationships, not prove causation. Threats to internal validity like subject characteristics, history, and testing must be controlled.
ABSTRACT : This paper critically examined a broad view of Structural Equation Model (SEM) with a view
of pointing out direction on how researchers can employ this model to future researches, with specific focus on
several traditional multivariate procedures like factor analysis, discriminant analysis, path analysis. This study
employed a descriptive survey and historical research design. Data was computed viaDescriptive Statistics,
Correlation Coefficient, Reliability. The study concluded that Novice researchers must take care of assumptions
and concepts of Structure Equation Modeling, while building a model to check the proposed hypothesis. SEM is
more or less an evolving technique in the research, which is expanding to new fields. Moreover, it is providing
new insights to researchers for conducting longitudinal investigations.
.
Correlation research examines the relationships between two or more non-manipulated variables without changing any variables. It can be used to predict scores on one variable based on scores of another predictor variable. Common techniques include explanatory design to look for associations between variables and prediction design to identify predictors of outcomes. Tools to analyze correlations include scatter plots, correlation coefficients, and regression analysis.
Correlational research designs examine relationships between two or more variables without manipulating any variables. They are used to describe and measure the degree of association between variables or sets of scores. There are two main types of correlational designs: explanatory/explanation designs which examine associations between variables, and prediction designs which identify predictor variables that can anticipate outcomes. Key aspects of correlational research include scatterplots, correlation coefficients, significance testing, and multiple variable techniques like partial correlation and multiple regression.
The document provides an overview of how to conduct a meta-analysis in psychology. It discusses that meta-analysis aims to quantitatively integrate the results of empirical studies on a given topic. There are typically 6 steps to conducting a meta-analysis, including defining research questions, conducting a literature search, coding studies, calculating effect sizes, statistical analysis and interpretation, and publication. It also provides an example of a meta-analysis that developed a comprehensive model of environmental behavior determinants based on combining several theories, using a meta-analytical structural equation modeling method.
Rule-Based Mamdani-Type Fuzzy Modeling of Perceived Stress, And Cortisol Resp...IJERA Editor
In this paper, Two Mamdani type fuzzy models (four inputs–one output and two inputs–one output) were developed to test the hypothesis that high job demands and low job control (job strain) are associated with elevated free cortisol levels early in the working day and with reduced variability across the day and to evaluate the contribution of anger expression to this pattern. The models were derived from multiple data sources including One hundred five school teachers (41 men and 64 women) classified 12 months earlier as high (N = 48) or low (N = 57) in job strain according to the demand/control model sampled saliva at 2-hour intervals from 8:00 to 8:30 hours to 22:00 to 22:30 hours on a working day. The quality of the model was determined by comparing predicted and actual fuzzy classification and defuzzification of the predicted outputs to get crisp values for correlating estimates with published values. A modified form of the Hamming distance measure is proposed to compare predicted and actual fuzzy classification. An entropy measure is used to describe the ambiguity associated with the predicted fuzzy outputs. The four input model predicted over 70% of the test data within one-half of a fuzzy class of the published data. The two input model predicted over 40% of the test data within one-half of a fuzzy class of the published data. Comparison of the models show that the four input model exhibited less entropy than the two input model.
Correlational research investigates potential relationships between variables without manipulating them. Researchers seek associations between variables to help explain behaviors or predict outcomes. If a strong enough relationship exists between two variables, scores on one can be used to predict the other. Correlational techniques include multiple regression, coefficient of multiple correlation, and factor analysis. Researchers must consider threats to internal validity like subject characteristics, instrumentation, and data collector bias.
Determinant of Teacher Performance
This research is focused on knowing whether there is an influence of principal's leadership, and organizational commitment on the performance of SMP Negeri teachers in Salatiga City. The main objective is to find and analyze the magnitude of the effect of each variable so that the factors causing the low performance of SMP Negeri teachers in Salatiga City can be identified so that the findings of this study are expected to be taken into consideration and reference in determining policies in an effort that leads to
improvement. Teacher performance. This research is quantitative research with a survey method. The sampling technique used is the probability sampling technique. Data analysis used linear regression analysis techniques for testing the research model. The result of the research is that the principal's leadership and organizational commitment have the most significant influence on teacher performance.
EEG correlates of maths anxiety: twin studyIlya Zakharov
The document discusses theories of mathematics anxiety (MA) and presents preliminary results from an ongoing twin study investigating MA. It summarizes three theories explaining how MA impacts working memory and performance. The twin study involves 25 twin pairs who complete cognitive tests and an EEG experiment with different cue types (arithmetic, algebraic, lexical). Preliminary results show stable ERP patterns across identical twins for different cue types and differences in P2 and P3b components when comparing high and low MA participants, with higher amplitudes for low MA. The results provide evidence that emotional expectations can reduce susceptibility to fearful stimuli by acting as a cognitive regulation strategy.
Correlational research examines relationships between two or more variables without manipulating them. It investigates whether changes in one variable are associated with changes in another. Correlational studies describe relationships using a correlation coefficient and can be used to predict scores on one variable based on scores on another. Common correlational techniques include scatterplots, regression analysis, and factor analysis. Threats to internal validity like subject characteristics, mortality, history, and instrumentation must be controlled.
This study examined how goals, empowerment, and communication relate to virtual worker performance. It surveyed 113 virtual workers and found that:
- Higher goal setting was associated with better perceived performance
- Greater empowerment correlated with increased perceived performance
- More communication linked to higher perceived performance
- Simultaneously improving goals, empowerment and communication significantly boosted perceived performance
However, the study was limited by using self-reported measures and a specific population. Further research with controlled experiments is needed to establish causation.
designed by
jennifer l
media arts and design undergrad
these slides were created for SIFE Simon Fraser (burnaby, bc, canada) to showcase our effects to promote environmental sustainability in the Greater Vancouver community at the SIFE Regionals Competitions 2010 in Calgary.
Economic factors led to declines in coal production and increases in natural gas and imports. The 1984 miners' strike reduced government support for the coal industry, forcing imports of cheaper foreign coal. Privatization of electricity and gas companies in the 1980s motivated profit-seeking over national interests. Environmental concerns over air pollution and the 1986 Chernobyl disaster raised issues with coal and nuclear power. Political factors included EU emissions reductions requirements and the 1997 Kyoto Summit agreement for the UK to lower CO2 emissions, pushing renewable energy development.
Inspiring introduction into sustainable lifestyle. The material is targeted for students above 15 years.
What is sustainable lifestyle? Why is it needed and what can I do for it?
Exercise and various links for further reading are also included.
Pack2Sustain @ Pet Industry Sustainability Coalition webinar: Innovations in ...Pack2Sustain, LLC
Jay Edwards of Pack2Sustain shared these pages with the Pet Industry Sustainability Coalition webinar audience on 14-January, 2014. The webinar was the first in a series of three sessions designed to inform the pet industry concerning packaging opportunities and best practices.
For more on the PISC's efforts, see this link: http://lnkd.in/bwQsN5K
This document provides an introduction and overview of the Creafutur First Annual Outlook report on sustainability and business opportunities. The report aims to analyze social and market trends, case studies, and consumer insights to identify both short-term and longer-term business opportunities related to sustainability. It uses a variety of analytical tools and studies trends, successful business models, and consumer perspectives to understand the increasing demand for sustainability and how businesses can capitalize on opportunities in different sectors over different time horizons.
Ospiti per frog
Fabio Sergio • Executive Creative Director • Milano
Chiara Diana • Creative Director • Milano
Roberta Tassi • Senior Design Researcher • Milano
Moderatore: Valerio Castelli
Backpack PLUS: a comprehensive toolkit to empower community health workers.
Un progetto in collaborazione con UNICEF, MDG Health Alliance e Save the Children
Here are the statements coloured as instructed:
Red: Volcanoes and earthquakes are most likely to occur in areas where the plates collide.
Green: These plates move at about the same rate as our fingernails grow!
The red statement with a fact about earthquakes and volcanoes is: "Volcanoes and earthquakes are most likely to occur in areas where the plates collide."
The green statement is: "These plates move at about the same rate as our fingernails grow!"
Greening Your Library: Save Money and the EnvironmentALATechSource
This document outlines ways for libraries to implement green practices to save money and help the environment. It discusses how libraries can be "doers", "publicizers", and "catalysts" of green initiatives. As doers, libraries can adopt green operational practices like using ebooks, aggregated databases, and energy efficient lighting. They can publicize green information through workshops, curriculum integration, and summer reading programs. Libraries can also act as catalysts by providing access to sustainability information, developing special collections, and partnering with local businesses and government on green economic development. The goal is for libraries to demonstrate and teach green practices while engaging their communities.
The document presents the findings of the Monitoring and Evaluation (M&E) of the Science, Environmental and Agricultural Life Skills (SEAL) programme implemented by VVOB Cambodia in 2012. Data was collected through observations, logbooks, interviews, and focus groups.
The M&E report assessed progress towards improving learning outcomes for pupils, increasing teachers' understanding of integrating technology, pedagogy and content knowledge, and strengthening teacher training centers. Key findings include increased use of student-centered approaches by teachers and more developed teaching resources, though challenges remain in content and methodology skills. The report concludes by identifying lessons learned and informing planning for 2013.
The document summarizes the state of the flexible packaging industry in the United States. It finds that flexible packaging continues to grow at 3% annually due to opportunities in retail factors like stand-up pouches and environmental factors including more sustainable materials. Flexible packaging reduces waste and uses less energy throughout its lifecycle compared to alternatives. The future of flexible packaging is promising as sustainability becomes more important to consumers and retailers.
Packaging design serves three main roles: protection of contents, transportation and distribution of products, and communication with consumers. It combines graphic and structural design elements. As designers, we have a responsibility to create effective and sustainable packaging designs that minimize environmental impact. The document outlines various tools and considerations for packaging design, including naming, branding, materials, messaging, and innovation.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Impact of Perceived Fairness on Performance Appraisal System for Academic Sta...IJSRP Journal
This study investigates the employees’ perception of fairness in the performance appraisal system for academic staff of the General Sir Jhon Kotelawala Defence University.
This study examined the influence of principal leadership and organizational commitment on teacher performance in Salatiga City, Indonesia. The study used a survey methodology and collected data from 161 public junior high school teachers. Linear regression analysis was used to analyze the data. The results showed that both principal leadership and organizational commitment had a significant positive influence on teacher performance, with principal leadership having a slightly greater influence. When considered together, principal leadership and organizational commitment also had a joint significant positive influence on teacher performance. The study concluded that principal leadership and organizational commitment are important determinants of teacher performance.
The study examined the effects of team cognition on complex engineering tasks. It analyzed shared mental models within and between teams through repeated measures ANOVA. The results showed that task-related shared mental models increased over time within teams but not between teams, while team-related shared mental models increased both within and between teams. The study provides insights into team cognition dynamics but could be improved by addressing potential biases.
IRJET- An Empirical Study on the Relationship Between Meditation, Emotion...IRJET Journal
The document presents a study that examines the relationship between meditation, emotional intelligence, and subjective well-being through structural equation modeling. The study assessed 727 meditators in Erode District, India and found that meditation positively impacts emotional intelligence and the five dimensions of subjective well-being, including achievement, charisma, diplomacy, progressiveness, and optimism. The study concluded that increasing emotional intelligence through meditation enhances subjective well-being.
Running head DATA ANALYSIS PLAN 1DATA ANALYSIS PLAN.docxtodd271
Running head: DATA ANALYSIS PLAN
1
DATA ANALYSIS PLAN
6
Data Analysis Plan
Columbia Southern University
PUH 6301 Public Health Research
February 25, 2020
Data Analysis Plan
Checking for Data Accuracy
Data accuracy checking will incorporate various measures for efficacy. The first method will include using reliable data sources. The data sources are critical to successful data collection as well as further analysis. Therefore, I will ensure the credibility and reliability of the systems as well as personnel responsible for information and data generation. Another significant measure will be aligning the key parameters and factors. It entails analyzing and sifting through the features that contribute to data communication, by figuring out the most relevant parameters that are needed for the performance report of the specific operations or developing the feasibility (Cole & Trinh, 2017). Then, I will design a set of essential and basic parameters and formulate a plan for the data collection.
Equally, maintaining neutrality is essential for checking data accuracy since claims and exaggerations create a negative balance to data sets. Therefore, by ascertaining that data is neutral, it becomes easy to justify the completeness of data. Importantly, I will use computerized and automated programs. There is always room for more mistakes as well as a human error with the use of manual mechanisms during information recording and data entry (Cole & Trinh, 2017). Besides, there can be higher risks of inaccuracy and compromised data entries based on personal favors and biases that wholly affect data results and inferences, leading to loss of portability and efficacy of data accuracy and analysis. However, the data collection through automated and smart systems makes it easier for focusing on parameters and factors, while the system records accurate data and real-time in a perfect manner.
Level of Measurement
The important level of measurement for my research project is the nominal level of measurement. The measurement is essential to the research since it uses elements such as letters, words, numbers, and alpha-numeric (Ekinci, 2017). In the research, the hypothesis is establishing the difference in performance between private and public schools. Specifically, the hypothesis is “private schools perform better than public schools.” Therefore, one of the elements will be a comparison of performance by gender. In this case, female students will be classified as F and male students will be classified as M. The nominal level of measurement is equally essential in this research since it only possess the description of the character meaning the unique label for identifying values to subjects. In this case, it is used to identify male and female students and utilizes a one-on-one correlation between the objects and letters assigned. Therefore, the letters are merely for identifying the gender of the students and not their capabilities in the learning .
This document summarizes a research study on the relationship between employee engagement, employee performance, and core self-evaluation in the health sector of Punjab, Pakistan. The study found:
1) Core self-evaluation fully mediated the relationship between employee engagement and task performance, as well as the relationship between employee engagement and organizational citizenship behavior directed at individuals.
2) Core self-evaluation partially mediated the relationship between employee engagement and organizational citizenship behavior directed at the organization.
3) Structural equation modeling indicated a good fit between the proposed mediation models and the data. Regression and mediation tests supported all hypotheses, showing core self-evaluation's mediating role in improving employee performance through engagement.
This proposal aims to examine the relationship between work-life balance, quality of work life, and job performance among secondary teachers. It hypothesizes that there is a significant relationship between work-life balance and job performance, and between quality of work life and job performance. The study will survey secondary teachers in Hinatuan West District, Philippines about their work-life balance, quality of work experiences, and job performance. Statistical analysis will determine the status of each variable and the relationships between them. Insights from this research could help enhance teacher well-being and effectiveness.
What do Student Evaluations of Teaching Really Measure?Denise Wilson
H) It's anybody's guess (who knows?)
The document summarizes multiple studies that call into question what exactly SETs measure. While SETs were originally intended to measure teaching quality, the research presented indicates that SETs lack construct and outcome validity and the statistics used are often inaccurate. Therefore, the conclusion is that it is unclear and unknown what specifically SETs actually measure.
Aantekeningen uit referenties van Biostatistics.docxdugkosasan
Here are simplified explanations of some common nonparametric statistical tests:
- Chi-square test - Used to see if there is a relationship between two categorical variables, like comparing observed and expected counts in different groups.
- Binomial test - Checks if the probability of success is different between two groups, like comparing if a new medicine helps more patients than an old one.
- Fisher's exact test - Similar to chi-square but for comparing small groups where expected counts may be low.
- McNemar's test - For before-and-after data from the same individuals, like comparing preferences before and after trying a new product.
- Mann-Whitney U test - Compares rankings between two groups
The document discusses analyzing assessment data from a nursing course. It addresses reliability, trends in raw scores, range of scores, standard error of measurement, and individual item analysis. Sample test statistics are used to determine if student learning occurred. The analysis shows the test was reliable. Scores followed a normal distribution, indicating learning took place. Steps are identified to improve learning for students with lower scores.
ScenarioStatistical significance is found in a study, but the ef.docxanhlodge
Scenario
Statistical significance is found in a study, but the effect in reality is very small (i.e., there was a very minor difference in attitude between men and women). Were the results meaningful?
An independent samples t test was conducted to determine whether differences exist between men and women on cultural competency scores. The samples consisted of 663 women and 650 men taken from a convenience sample of public, private, and non-profit organizations. Each participant was administered an instrument that measured his or her current levels of cultural competency. The cultural competency score ranges from 0 to 10, with higher scores indicating higher levels of cultural competency. The descriptive statistics indicate women have higher levels of cultural competency ( M = 9.2, SD = 3.2) than men ( M = 8.9, SD = 2.1). The results were significant t (1311) = 2.0, p <.05, indicating that women are more culturally competent than are men. These results tell us that gender-specific interventions targeted toward men may assist in bolstering cultural competency.
Instructions: Critically evaluate the scenario you selected based upon the following points:
Critically evaluate the sample size.
Critically evaluate the statements for meaningfulness.
Critically evaluate the statements for statistical significance.
Based on your evaluation, provide an explanation of the implications for social change.
I attached the scenario to this message
add at least two references and citations
Number of Pages: 1 Page
Statistical significance is found in a study, but the effect in reality is very small (i.e., there was a very minor difference in attitude between men and women). Were the results meaningful?
According to The American Statistical Association's Statement on the Use of P Values (2016), Statistical reasoning should not be replaced by P-values. Once significance is noted and the effect is very small, the researcher should not conclude significance just because the P-value suggested so. Other tests should be performed which include construction of confidence intervals. If after several tests significance is still being seen, then the researcher can conclude significance otherwise, he/she should opt for other approaches for the research such as increasing the sample size.
Sample Sizes
Sample sizes used were 663 for women and 650 for men. The data was collected from three points that is public, private and non-profit organizations. Though the sample seems good enough, the entire population figure is not provided. Another problem is that, sample sizes from the three locations were not disclosed which probably means that the samples were all added up and used in the analysis. Sample sizes from each location should be carefully determined based on the population of each location. Since Convenience sampling is error bound, the samples shouldn’t be summed up and used in the analysis. Each location should be examined separately. Thi.
Data science notes for ASDS calicut 2.pptxswapnaraghav
Data science involves both statistics and practical hacking skills. It is the engineering of data - applying tools and theoretical understanding to data in a practical way. Statistical modeling is the process of using mathematical models to analyze and understand data in order to make general predictions. There are several statistical modeling techniques including linear regression, classification, resampling, non-linear models, tree-based methods, and neural networks. Unsupervised learning identifies patterns in data without pre-existing categories by techniques like clustering. Time series forecasting predicts future values based on patterns in historical time series data.
The document discusses a study on the effect of principal leadership on the achievement motivation of Hindu religion teachers in Tabanan Regency, Bali, Indonesia. The study found that principal leadership has a significant positive influence on teacher achievement motivation. Specifically, principals who demonstrated good leadership skills through clear guidance, monitoring, evaluation, and motivation tended to have teachers with higher achievement motivation. The study also found that most principals and teachers surveyed exhibited good to very good levels of leadership and achievement motivation, respectively. Therefore, strong principal leadership can enhance teacher motivation and ultimately improve performance.
The document proposes a new framework called Quasi Framework to detect disengagement in online learning. It analyzes log file data from an online learning system to identify attributes related to disengagement. The framework merges log file information with student database information and uses it to predict disengagement. Experimental results on a real student dataset show the Quasi Framework achieves higher accuracy than an existing system called iHelp, particularly for predicting disengaged students. The study suggests considering both reading and assessment attributes are important for accurate disengagement detection.
Biruk Chala MBA Thesis Presentation Slide (1).pdfBirukChala2
This thesis examines the effect of leadership style on employee performance at the Addis Ababa City Administration Health Bureau in Ethiopia. The study aims to determine the influence of different leadership styles (transformational, transactional, laissez-faire, and autocratic) on employee performance. A survey was conducted of 201 employees to understand their perceptions of leadership style and performance. The results found that transactional leadership had the most positive effect on performance, while autocratic leadership had a negative effect. It was also found that employees desired more involvement in decision-making. The study recommends that the Bureau adopt a stronger transactional leadership approach to improve employee motivation and organizational goals.
This This is extra information from the .docxherthalearmont
This
This is extra information from the course syllabus for you to know.
I got zero’s in this assignment
Here is the first one that you helped me with…. See Week 5 Attachment
Simple Linear, Multiple, Or Logistic Correlation/Regression Proposal
This written assignment is based on the work conducted in the “Correlation and Regression” discussion forum. Based on this initial work, feedback received, and additional research, students should submit a basic research proposal that calls for the use of a simple linear, multiple, or logistic correlation/regression.
The paper should be APA formatted as a research proposal, and contain approximately 990-1320 words of content. Include a title page, and a reference page that includes any resources utilized.
Please include the following in the research proposal:
1. Introduction (1-2 paragraphs)
· Present the research question of interest.
· Explain how the chosen statistical test applies to this research question.
· Provide the statistical notation and written explanations for the null and alternative hypotheses.
2. Methods (1 paragraph)
· Participants
· List how many participants will be selected.
· Identify who will be the participants and their major demographic characteristics (e.g., sex, age, etc.).
· Explain how participants will be selected for the study.
3. Procedures (1-2 paragraphs)
· Identify the variables in the study.
· Describe each variable’s scale of measurement (nominal, ordinal, interval, or ratio) and characteristics (i.e., discrete vs. continuous, qualitative vs. categorical, etc.).
· Provide an operational definition for each variable, explaining how the variables will be measured.
4. Results (2-3 paragraphs)
· Describe the statistical test that will be conducted. Be sure to include why the test was chosen and why it is appropriate for this study. Include in the discussion the necessary assumptions that should be met for the chosen test and how these will be addressed.
· Identify the information that will be obtained from the results of this test and what will be needed to draw conclusions regarding the hypotheses. Be sure to include a discussion of applicable critical and calculated values, p levels, confidence intervals, effect sizes, post-hoc tests, and/or tables.
Discussion (1 paragraph)
· Identify any expected biases, assumptions, or faults with the proposed study and the use of the identified statistical test.
· Explain what conclusions can and cannot be made for this study, and using this statistical test.
· Describe the practical significance or importance of the results.
1
Running head: Simple Linear Correlation
7
Simple Linear Correlation Statistical Test:
Employee Empowerment & Service Quality
Introduction
This research proposal will seek to answer the research question that states, “What is the effect of employee empowerment on service quality in an organization?”. This is a question of interest as many organiz ...
Factor analysis is a statistical technique used to reduce a large number of variables into fewer factors. It analyzes the relationship between observable variables and how they are affected by smaller sets of unobservable variables. The main goal is to summarize information from many variables into a few factors. There are three main types: exploratory factor analysis identifies underlying factors without predefined structure, confirmatory factor analysis confirms predetermined factor structures, and structural equation modeling tests hypothesized relationships between variables and factors.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
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HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
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Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
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- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
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- Practical examples and best practices to implement right away
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UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 6
C0252014021
1. International Journal of Mathematics and Statistics Invention (IJMSI)
E-ISSN: 2321 – 4767 P-ISSN: 2321 - 4759
www.ijmsi.org Volume 2 Issue 5 || May 2014 || PP-14-21
www.ijmsi.org 14 | P a g e
Effect of Sample Size on Normality and Power of Test of
Generalized Structured Component Analysis (GSCA) On Likert
Scale Data
Andi Julizar1
, Waego Hadi Nugroho1
, Solimun1
1
(Department of Statistics, Brawijaya University, Indonesia)
ABSTRACT : In a research of psychometrics, researchers often used variables as attitudes or behaviors, which
cannot be observed or measured directly or are referred to as latent variables. Likert scales can be used to
obtain the values of such variables. GSCA, as one of the preferred analyses tool to analyze the relationship of
latent variables, is claimed that it can be used without data normality assumption to be met and with small
sample size. This paper aims to know the effect of sample size on normality and the Power of test when we use
Likert scales data, which are collected by questionnaires to test the significant factors affecting teacher’s
performance with some predictors and a moderator. Then the data are grouped into 3 data sets with each data
set represents a small, medium and large sample size. After running normality test, we find that as the sample
size increase, the data are not necessarily closer to the normal distribution due to data’s discrete characteristic.
But after conducting analysis with GSCA, we find that for the small sample size, GSCA’s Power is still adequate
to make decision of parameter estimation although the Power is greater for large sample size. As for teacher’s
performance, the professionalism, consumptive behavior and work motivation are significantly affecting the
performance.
KEYWORDS : GSCA, Likert scale, Normality, Power, Teacher’s performance
I. INTRODUCTION
In a research of psychometrics, researchers often used variables such as attitudes or behaviors. Such
variables cannot be observed or measured directly or are referred to as latent variables. Thus, the indicators are
used to obtain the variables’ values, like using Likert scales, which is the sum (or average) of responses from
Likert items or scores [1]. Generally, a relationship of variables can be described and analyzed with
Regression/Path Analysis. But if we use latent variables, the more preferred analyses are Structural Equation
Models (SEM), Partial Least Squares (PLS), or Generalized Structured Component Analysis (GSCA) [2].
GSCA is claimed that it doesn’t need data normality assumption to be met and doesn’t require a large sample
size [3], but the abnormality of the data can affect Power [4] and so does sample size [5].The Power of test is a
probability to reject the null hypothesis (H0) when H0 is false. The value depends on the significance level (α),
sample size (n), and effect size (ES). The importance of Power Analysis comes from the fact that most of
empirical researches on the social and behavioral science begin with formulating and testing with hope of
rejecting H0 as a confirmation to the fact of the phenomenon under study [6]. The Power Analysis can be
prospective (a priori) or retrospective (post hoc) one. Prospective analysis is used to determine the sample size
in order to achieve the target Power, while retrospective analysis calculates Power by sample size (n) and effect
size (ES). In this paper, we do retrospective analysis to find out the analysis’ Power. The relationship between α,
n and ES toward Power are positive (Figure 1) where the increase of ES, n and α makes the value of Power
increase too [7]. The ES characterizes the model’s goodness of fit, so the model fit index can be considered as
ES [8].
When sample size increase, the standard error will decrease as in formula:
n
s
SE (1)
It means the more representative the sample will be of the overall population because the statistic will
approach the actual value. Therefore, the effect size of the test is greater and so is the Power.
2. Effect of Sample Size to the Normality…
www.ijmsi.org 15 | P a g e
Figure 1:The Relationship of α, n and ES toward Power [7]
Data with normal distribution will produce greater Power than the data that did not follow a normal
distribution. Given the normality of the data is also influenced by n, so the formula to obtain the value of Power
is:
(2)
where s is square root of model’s variance.
As for the numerical example for this study, we will analyze factors affecting teacher’s performance
with professionalism attitude, consumer behavior and work motivation as predictors and certification receiver
status is a moderator. Since 2007, Indonesian government gives a benefit for teachers whom already passed
qualification to be professional through certification program based on government’s law number 20 year 2003
about National Education System section 39–44. Among of the purposes of this benefit is to make teachers to be
more professional and prosper so they can improve their performance thus qualified students will be produced
[9]. But people noticed that some teachers behave more consumptive than ever by buying insignificant things
which far from improving their performance. This behavior can lead the decreasing of performance due to the
stress they get [10]. Some researches has been concluded that the prosperity and work motivation significantly
affect the performance. As the government has targeting the increasing number of vocational high school
(SMK) to make Indonesian students to be more skilled, more have insights, and more able to adapt to work
system, the teacher’s performance is really needed to fulfill what government wants.
II. RESEARCH METHOD
A primary data was collected through questionnaires for 4 latent variables to measure professionalism,
consumptive behavior, work motivation and teacher’s performance to 40 teachers as respondents from
vocational high schools in Banjarmasin, Indonesia. The sampling method was stratified random sampling where
each stratum consists of 20 teachers and represents certified teachers and non-certified teachers, respectively.
The validity and reliability test of the instrument left us only indicators that are valid and make the instruments
reliable. The indicators for professionalism variable are teaching skills, mastery learning media, good
personality, good role model and mastering curriculum. These indicators based on materials of Education and
Training of Teachers Profession program. The indicators of consumptive behavior variable are tempted of gift,
feeling confidence by buying expensive stuff and buying staff for the sake of appearance. These indicators based
on consumptive behavior definition [11]. The indicators of work motivation variable are sense of financial
security needs, physiological needs, sense of award needs and self-actualization needs. These indicators based
on Maslow theory [12]. The indicators of teacher’s performance variable are the ability to design evaluation
tool, suitability media learning, having a learning strategy, ending study effectively, having a lesson plan,
having a systematic materials, having assessment method and good use of language. These indicators based on
government’s regulation [13]. The indicators mentioned above are sorted by the most important indicator based
on respondents’ answers. Later, the data are grouped into 3 data sets with sample size of 20, 30 and 40 which
each data set represents small, medium and large sample size. The central limit theorem states that the larger the
sample size, the statistic will follow a normal distribution. Generally, the sample size more than 30 is considered
as large one. Each data set is resampled 5 times in order to get 5 generated data which resulting the mean of
Kolmogorov-Smirnov Z (KS Z) from data normality test and Power, so the values are less biased. Then multi-
group analysis with GSCA is conducted since the use of moderator with 2 categories, which are teachers with
certificate and teachers without.
To test whether certificate receiver status (D) is a moderator for each path, we use Z-test with formula:
3. Effect of Sample Size to the Normality…
www.ijmsi.org 16 | P a g e
(3)
(4)
where G1, G2, b and SE(joint) are group 1, group 2, path coefficient and joint standard error for two coefficients
respectively [14].
Figure 2 below describes the hypothetical relationship between latent variables.
Figure 2: Research Model
III. ANALYSIS OF DATA
3.1. Description of Data
To see the variation of observation value for each variable, we can use coefficient of variation (CV)
with formula:
(5)
where s and is standard deviation and mean scores of a variable respectively.
Table 1. Mean scores and coefficient of variation for each variable
Variable Mean scores
Coefficient of
Variation
Professionalism 4.1 0.10
Consumptive Behavior 1.9 0.34
Work Motivation 3.95 0.17
Performance 4.08 0.11
As we can see from Table 1, the dispersion of observation value for all variables is small, which
indicates that the teacher’s attitude or behavior is relatively the same. Except for consumptive behavior, which
the CV is rather large, this indicates that the teacher’s consumptive behavior is more heterogenic.
3.2. The Effect on the Normality
Table 2 shows the mean of KS Z from each data set. We can see that as the sample size increase, the
KS Z also increase, which means the difference between observation values distribution and theoretical normal
distribution is getting larger. Hence, the bigger the probability to reject null hypothesis which states the data
follow a normal distribution.
Professionalism
Attitude (X1)
Work
Motivation
(X3)
Consumptive
Behavior (X2)
Teacher’s
Performance
(Y)
Certificate Receiver
Status (D)
4. Effect of Sample Size to the Normality…
www.ijmsi.org 17 | P a g e
Table 2.The mean of KS Z for each data set
Variable
Data Set (Sample size)
1 (20) 2 (30) 3 (40)
Professionalism 1.08 1.32 1.50
Consumptive Behaviour 0.79 1.06 1.31
Work Motivation 0.77 0.95 1.05
Teacher’s Performance 0.71 0.95 1.04
The increasing of KS Z as sample size increase is caused by the increasing of skewness and kurtosis
coefficient as Table 3 describes.
Table 3.The skewness (S) and kurtosis (K) coefficient for each data set
Variable
Data Set (Sample size)
1 (20) 2 (30) 3 (40)
S K S K S K
Professionalism -0.13 0.61 -0.74 1.13 -0.18 1.13
Consumptive Behaviour 0.46 0.25 0.51 0.43 0.63 0.17
Work Motivation -0.66 0.11 -0.57 0.05 -0.80 0.64
Teacher’s Performance 0.36 -0.57 0.61 -0.08 0.43 -0.49
This means Likert scales data do not guarantee that the larger sample size will make the data closer to
the normal distribution. Consequently, even with large sample size, the Power is not necessarily greater.
3.3. The Effect on the Power
From Table 4, the mean of Power is 0.593, 0.618, and 0.777 for small, medium and large sample size
respectively. There is a convention that the trade-off between α and β is 1 to 4 [15]. Thus, if we use α = 0.1, then
β = 0.4 then the adequacy value for Power is 1 – 0.4 = 0.6. Therefore, all sample size have adequate Power since
all of the Power is equal to or bigger than 0.6.
Table 4.The mean of Power for each data set
Data set
(Sample size)
Resampling
Data
FIT SRMR Power
Mean of
Power
1
(20)
1 0.417 0.123 0.758
0.593
2 0.422 0.125 0.755
3 0.429 0.204 0.470
4 0.445 0.216 0.461
5 0.482 0.206 0.523
2
(30)
1 0.410 0.205 0.548
0.618
2 0.492 0.194 0.695
3 0.393 0.201 0.535
4 0.464 0.196 0.648
5 0.441 0.182 0.664
3
(40)
1 0.429 0.217 0.625
0.777
2 0.453 0.174 0.823
3 0.431 0.173 0.788
4 0.422 0.160 0.834
5 0.467 0.181 0.816
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Table 5 also shows that the Likert scales data provide coefficient of variation of response variable (Y),
which is calculated using Equation (5), for each sample size is the same based on result of analysis of variance
with bootstrap method since there are only 5 observations. So it can be interpreted that the Likert scales data
with small, medium or large sample size is quite homogeny. The same of variables’ coefficient of variation
makes analyzing the Likert scales data with GSCA doesn’t require the large sample size. So even with the small
sample size, GSCA will still provide an adequate Power, although (from Table 4) the larger sample size the
greater the Power will be.
Table 5.Coefficient of Variation of Y and its mean for each sample size
Data set
(Sample size)
Resampling SD of Y Mean of Y CV of Y
1
(20)
1 3.502 31.95 0.110
2 3.631 32.15 0.113
3 3.100 32.35 0.096
4 3.252 33.45 0.097
5 3.210 31.75 0.101
2
(30)
1 3.277 31.50 0.104
2 3.087 32.30 0.096
3 3.462 32.53 0.106
4 3.024 32.60 0.093
5 3.645 32.43 0.112
3
(40)
1 3.393 32.85 0.103
2 3.354 32.33 0.104
3 2.599 31.75 0.082
4 3.733 32.60 0.115
5 3.714 32.73 0.113
3.4. The Numerical Example
The GeSCA software package was employed to estimate the path coefficients from each data set and
resulting t-ratio from parameter estimation. Table 6 and 7 show the mean of the t-ratio for each data set for
group 1 (teachers with certificate) and group 2 (teachers without certificate) respectively.
From those two tables, the sample size of 40 (which is large) gives more significant paths at 5% level.
This means the large sample size has greater probability to produce greater t-ratio than small and medium
sample size. The t-ratio can also be used to measure ES by calculating the variation percentage of variables that
can be explained, by the formula [6]:
(6)
where r2
is the ES and df is the degree of freedom.
So, the greater the t-ratio, the greater the effect size will be. Hence, according to Equation (2), the
greater the Power will be obtained. Therefore, we can say that data set with large sample size has the probability
to have greater Power.
Table 6. The mean of t-ratio of parameter estimation for each data set of Group 1 (teachers with certificate)
Path n = 20 n = 30 n = 40
Professionalism --> Work Motivation 3.66* 14.27* 4.95*
Professionalism --> Teacher’s Performance 2.55* 28.52* 9.70*
Consumptive Behavior --> Work Motivation 2.27* 0.95 4.70*
Consumptive Behavior --> Teacher’s Performance 1.76 2.02 3.16*
Work Motivation --> Teacher’s Performance 1.41 4.14* 2.51*
*) Significant at 5% level
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Table 7. The mean of t-ratio of parameter estimation for each data set of Group 2 (teachers without certificate)
Path n = 20 n = 30 n = 40
Professionalism -> Work Motivation 1.32 5.03* 14.91*
Professionalism -> Teacher’s Performance 1.6 4.16* 8.43*
Consumptive Behavior -> Work Motivation 2.19* 1.41 0.26
Consumptive Behavior -> Teacher’s Performance 10.52* 1.93 3.39*
Work Motivation -> Teacher’s Performance 1.22 3.96* 0.07
*) Significant at 5% level
As explained above where data with sample size of 40 give more analysis’ Power, then multi-group
analysis with GSCA is conducted using the original data, which have sample size of 40, to find out the
significant factors that affect the teacher’s performance. And the results for both groups are presented in Table 8
which all estimates are significant in group 1 and 3 estimates are significant in group 2.
Table 8. Path coefficients estimation for each group
Path Estimate SE T
Group 1 (Teachers with certificate)
Professionalism -> Work Motivation -0.421 0.044 9.51*
Professionalism -> Teacher’s Performance 0.667 0.182 3.67*
Consumptive Behavior -> Work Motivation 0.154 0.033 4.72*
Consumptive Behavior -> Teacher’s Performance 0.111 0.050 2.22*
Work Motivation -> Teacher’s Performance 0.077 0.449 0.17
Group 2 (Teachers without certificate)
Professionalism -> Work Motivation 0.148 0.220 0.67
Professionalism -> Teacher’s Performance 0.540 0.315 1.72
Consumptive Behavior -> Work Motivation 0.257 0.002 112.83*
Consumptive Behavior -> Teacher’s Performance 0.242 0.002 111.3*
Work Motivation -> Teacher’s Performance 0.406 0.132 3.08*
*) Significant at 5% level
Using Equation (3) and (4) we will determine whether certification receiver status is moderator, and the
results are as in Table 9. The certification receiver status is said to be a moderator if the coefficient from G1 or
G2 is significant or both coefficients are significant with Z is also significant [14].
Table 9.Test result for Certification Receiver Status (D) as a moderator for each path
Path
Coefficient
Z Moderator? Remark
G1 G2
Professionalism --> Work Motivation -0.42* 0.15 - Yes Effect on Group 1 is weaker
Professionalism --> Teacher’s Performance 0.68* 0.54 - Yes Effect on Group 1 is stronger
Consumptive Behavior --> Work Motivation 0.15* 0.26* -3.12 Yes Effect on Group 2 is stronger
Consumptive Behavior --> Teacher’s Performance 0.11* 0.24* -2.62 Yes Effect on Group 2 is stronger
Work Motivation --> Teacher’s Performance 0.08 0.41* - Yes Effect on Group 2 is stronger
*) Significant at 5% level
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From Table 9 we can decide that certification receiver status (D) weakens the effect from
professionalism to work motivation since the significant path (from G1) has the minus sign (-). Professionalism
significantly affects teacher’s performance where the effect of certified teachers is stronger. Consumptive
behavior significantly affects work motivation but D doesn’t strengthen nor weaken the effect, while
consumptive behavior also significantly affects teacher’s performance where the effect of certified teachers is
stronger. And work motivation significantly affects teacher’s performance where the effect of non-certified
teachers is stronger.
IV. DISCUSSION OF NUMERICAL EXAMPLE’S RESULT
The more professional attitude a teacher has, the stronger his/her work motivation, but the effect from
certified teachers is weaker than non-certified ones. This could be caused by the certified teachers are already
feeling satisfied of what they got (certification allowance) so their work motivation is not as strong as the non-
certified ones. On the contrary, the non-certified teachers want to show that they are also eligible to become a
certified teacher. Certified teachers with professional attitude have a greater positive impact than non-certified
teachers to the performance improvement. This is due to the certified teachers already have participated in the
Education and Training of Teachers Profession program, which is intended to train teachers to be able to work
effectively, which are able to achieve the desired target within time specified by using appropriate method and
effort. Consumptive behavior significantly affects work motivation where the effect of non-certified teachers is
greater than the certified ones since the income of non-certified teachers is smaller. To fulfill the desire to shop,
the non-certified teachers will have a greater work motivation by engaging in additional tasks and school’s
activities in order to earn additional income.
Consumptive behavior can raise the stress which leads to poor performance [10]. It is not proven since
Table 8 mentioned that the Consumptive Behavior is in accordance with teacher performance. Or the more
consumptive behavior a teacher has, the better his/her performance. This phenomenon can be explained through
interviews with some respondents that their consumptive behavior still has not reached the point of "crazy
shopping (shopaholic)". In fact, according to them, the consumptive desires can be a driving force in the work
that led to improved performance especially on non-certified teachers who have less income than certified ones
where they are more motivated to show their performance. From respondents’ answers, the physiological and
financial security needs are the highest needs that motivate teachers to work. For certified teachers, these needs
have been met so that the effect of their work motivation is weaker than non-certified ones. To fulfill these
needs, the non-certified teachers are more motivated to improve their performance in order to have the
opportunity to participate in teacher’s certification program.
V. CONCLUSIONS AND RECOMMENDATIONS
Likert scales data do not guarantee the larger sample size will make data closer to a normal distribution
because of the discrete characteristic of the data; instead the data can be more skewed or ramped. Nevertheless,
GSCA still gives adequate Power of Test even with small sample size. This is due to the Likert scales data have
small coefficient of variation of all variables for small, medium and large sample size which means the
observation values are quite homogeny. But in general, the large sample size has greater probability to produce
greater Power since it gives greater effect size through greater t-ratio for testing the significance of the
coefficients. And since professionalism, consumptive behavior and work motivation significantly affect the
teacher’s performance, then to improve the performance, the guidance is needed to make teachers become more
professional and to have more work motivation, especially for non-certified teachers which have less impact
then the certified ones. Guidance can be in the form of education and training programs, which can be facilitated
by government and/or by school. Yet the teachers still need to be reminded to not keep buying things out of
necessity since it can jeopardize the performance if they fail to manage their financial condition.
REFERENCES
[1] J. S. Uebersax, Dispelling the Confusion, [Online], http://www.john-uebersax.com/stat.likert.htm, 2006.
[2] M. Tenenhaus, Component-based Structural Equation Modelling, [Online], http://www.hec.fr/heccontent/download/4797/
115324/version/2/file/CR887TENENHAUS.pdf, 2008.
[3] H. Hwang, and Y. Takane, Generalized Structured Component Analysis, Psychometrika,, 69(1), 2004, 81-99.
[4] M. Mendes, The effects of Non-Normality on Type III Error for Comparing Independent Means, Journal of Applied Quantitative
Methods, 2(4), 2007, 444-454.
[5] H. Luo, Generation of Non-Normal Data – A Study of Fleishman’s Power Method, Working Paper, Department of Statistics,
Uppsala University, 2011.
[6] J. Cohen, Statistical Power Analysis, Current Directions in Psychological Science, 1(3), 1992, 98-101, Sage Publication, Inc.
[7] H. M. Park, Hypothesis Testing and Statistical Power of a Test, Working Paper, University Information Technology Services
(UITS) Center for Statistical Power and Mathematical Computing, Indiana University, 2010.
8. Effect of Sample Size to the Normality…
www.ijmsi.org 21 | P a g e
[8] B. Thompson, A Suggested Revision to the Forthcoming 5th Edition of the APA Publication Manual – Effect Size Section,
[Online], http://people.cehd.tamu.edu/~bthompson/apaeffec.htm, 2000
[9] A. Syahza. Dampak Kebijakan Sertifikasi Terhadap Kinerja Guru SMP-SMA,
[Online],http://almasdi.staff.unri.ac.id/2013/01/22/dampak-kebijakan-sertifikasi-terhadap-kinerja-guru-di-daerah-riau/, 2013
[10] T. N. H. Santoso, Akibat dari Belanja Berlebihan, [Online],http://www.jengker.com/akibat-dari-belanja-berlebihan.html, 2012
[11] Sumartono, Terperangkap dalam Iklan – Meneropong Imbas Pesan Iklan Televisi (Bandung: Alfabeta, 2012).
[12] S. Reksohadiprojo and T. H. Handoko, Organisasi Perusahaan – Teori Struktur dan Perilaku, 2nd Ed. (Yogyakarta: BPFE,
1992).
[13] Regulation of the Minister of State for Administrative Reform and Bureaucratic Reform No. 16 of 2009 about the Teacher’s
Functional Master and His/Hers Credit Figures.
[14] Solimun,Generalized Structured Component Analysis (GSCA) – Penguatan Metodologi Penelitian (Malang: Brawijaya
University, 2013).
[15] P. D. Ellis, The Essential Guide to Effect Sizes – Statistical Power, Meta-Analysis, and the Interpretation of Research
Result(United Kingdom: Cambridge University Press, 2010).