This document discusses the validity and reliability of measurement instruments. It defines validity as the degree to which an instrument measures what it intends to measure. There are different types of validity discussed, including content validity, construct validity, and criterion validity. Reliability is defined as the consistency of measurement, or the degree to which an instrument produces stable and consistent results. Common methods for assessing reliability are test-retest reliability, parallel forms, and internal consistency. Formulas for calculating reliability coefficients like Cronbach's alpha and Kuder-Richardson 20 are also provided.
1) Validity refers to the extent to which a test measures what it claims to measure. There are different types of validity including content validity, construct validity, predictive validity, and concurrent validity.
2) Reliability is the consistency of a test and whether it would provide the same results over multiple administrations. Factors that influence reliability include the length of the test, score distribution, difficulty level, and objectivity.
3) There are different ways to measure validity and reliability including calculating correlation coefficients and using formulas like Pearson product-moment correlation, Kuder-Richardson, Cronbach's alpha, and point biserial correlation.
The document discusses the concept of reliability in measuring instruments. It defines reliability as the consistency of scores or measurements obtained from repeated administrations of a measuring tool. A reliable instrument will yield similar or stable results when used to measure the same object or individual in separate instances. The document outlines different types of reliability, including test-retest, parallel-forms, internal consistency, and inter-rater reliability. It provides examples and methods for estimating reliability, such as calculating correlations between test administrations, form scores, and item-total scores. Maintaining high reliability is important for ensuring measuring tools provide accurate and reproducible measurements.
This document discusses various methods of measurement and scaling used in research. It describes four main types of measurement scales: nominal, ordinal, interval, and ratio scales. It also discusses potential sources of error in measurement, ways to test the validity and reliability of measurement tools, and different types of scales including comparative scales like paired comparisons and non-comparative scales like Likert scales. Finally, it outlines the process of developing a new measurement tool, including concept development, indicator selection, and index formation.
1) This document provides an overview of statistical analysis techniques for regression modeling, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression analysis.
2) Steps are outlined for testing the validity and reliability of survey instruments, as well as performing descriptive statistics, correlation analysis, and various regression diagnostics.
3) Examples are provided applying these statistical techniques to sample data, including interpreting outputs from SPSS.
This document provides an overview of statistical analysis techniques for regression modeling, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression modeling. It discusses conducting validity testing using Pearson correlation and corrected item-total correlation. It also covers reliability testing using Cronbach's alpha, descriptive statistics such as measures of central tendency and dispersion, correlation analysis to examine relationships between variables, and multiple linear regression modeling. The document contains examples of conducting these analyses in SPSS and interpreting the results.
1) This document provides an overview of statistical analysis techniques for regression modeling, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression modeling procedures.
2) Statistical tests covered include validity, reliability, descriptive statistics such as mean and standard deviation, correlation analysis using Pearson's correlation coefficient, and regression modeling procedures like the F-test and t-test.
3) Examples are provided applying these techniques to sample data, demonstrating how to conduct the analyses in SPSS.
1) This document provides an overview of statistical analysis techniques for regression modeling, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression modeling procedures.
2) Statistical tests covered include validity, reliability, descriptive statistics such as mean and standard deviation, correlation analysis using Pearson's correlation coefficient, and regression modeling procedures like the F-test and t-test.
3) Examples are provided applying these techniques to sample data, including interpreting outputs from the SPSS statistical software package.
This document discusses reliability and validity, which are important concepts for measuring instruments. Reliability refers to the consistency and accuracy of measurements from an instrument. There are several types of validity, including face validity, content validity, criterion validity, predictive validity, and construct validity. Validity determines if an instrument truly measures what it intends to. Factors like unclear directions, inappropriate test items, and language difficulties can impact an instrument's validity and reliability. Reliability is assessed through methods like test-retest reliability, internal consistency, and equivalence measures between raters. Both reliability and validity are necessary for a measuring instrument to provide accurate and meaningful results.
1) Validity refers to the extent to which a test measures what it claims to measure. There are different types of validity including content validity, construct validity, predictive validity, and concurrent validity.
2) Reliability is the consistency of a test and whether it would provide the same results over multiple administrations. Factors that influence reliability include the length of the test, score distribution, difficulty level, and objectivity.
3) There are different ways to measure validity and reliability including calculating correlation coefficients and using formulas like Pearson product-moment correlation, Kuder-Richardson, Cronbach's alpha, and point biserial correlation.
The document discusses the concept of reliability in measuring instruments. It defines reliability as the consistency of scores or measurements obtained from repeated administrations of a measuring tool. A reliable instrument will yield similar or stable results when used to measure the same object or individual in separate instances. The document outlines different types of reliability, including test-retest, parallel-forms, internal consistency, and inter-rater reliability. It provides examples and methods for estimating reliability, such as calculating correlations between test administrations, form scores, and item-total scores. Maintaining high reliability is important for ensuring measuring tools provide accurate and reproducible measurements.
This document discusses various methods of measurement and scaling used in research. It describes four main types of measurement scales: nominal, ordinal, interval, and ratio scales. It also discusses potential sources of error in measurement, ways to test the validity and reliability of measurement tools, and different types of scales including comparative scales like paired comparisons and non-comparative scales like Likert scales. Finally, it outlines the process of developing a new measurement tool, including concept development, indicator selection, and index formation.
1) This document provides an overview of statistical analysis techniques for regression modeling, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression analysis.
2) Steps are outlined for testing the validity and reliability of survey instruments, as well as performing descriptive statistics, correlation analysis, and various regression diagnostics.
3) Examples are provided applying these statistical techniques to sample data, including interpreting outputs from SPSS.
This document provides an overview of statistical analysis techniques for regression modeling, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression modeling. It discusses conducting validity testing using Pearson correlation and corrected item-total correlation. It also covers reliability testing using Cronbach's alpha, descriptive statistics such as measures of central tendency and dispersion, correlation analysis to examine relationships between variables, and multiple linear regression modeling. The document contains examples of conducting these analyses in SPSS and interpreting the results.
1) This document provides an overview of statistical analysis techniques for regression modeling, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression modeling procedures.
2) Statistical tests covered include validity, reliability, descriptive statistics such as mean and standard deviation, correlation analysis using Pearson's correlation coefficient, and regression modeling procedures like the F-test and t-test.
3) Examples are provided applying these techniques to sample data, demonstrating how to conduct the analyses in SPSS.
1) This document provides an overview of statistical analysis techniques for regression modeling, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression modeling procedures.
2) Statistical tests covered include validity, reliability, descriptive statistics such as mean and standard deviation, correlation analysis using Pearson's correlation coefficient, and regression modeling procedures like the F-test and t-test.
3) Examples are provided applying these techniques to sample data, including interpreting outputs from the SPSS statistical software package.
This document discusses reliability and validity, which are important concepts for measuring instruments. Reliability refers to the consistency and accuracy of measurements from an instrument. There are several types of validity, including face validity, content validity, criterion validity, predictive validity, and construct validity. Validity determines if an instrument truly measures what it intends to. Factors like unclear directions, inappropriate test items, and language difficulties can impact an instrument's validity and reliability. Reliability is assessed through methods like test-retest reliability, internal consistency, and equivalence measures between raters. Both reliability and validity are necessary for a measuring instrument to provide accurate and meaningful results.
The document discusses various aspects of reliability and validity in psychological research. It defines reliability as consistency or repeatability of a measure. Several methods of assessing reliability are described, including test-retest reliability, internal consistency reliability (using split-half, Kuder-Richardson, and Cronbach's alpha tests), and parallel-forms reliability. Validity refers to how well a test measures what it is intended to measure. Different types of validity are covered, such as face validity, content validity, criterion-related (predictive and concurrent) validity, and construct validity.
The document describes the steps in conducting a complete regression analysis, including validity testing, reliability testing, descriptive statistics, correlation analysis, and various regression diagnostics. It provides examples of analyzing data from a study using SPSS to test the validity and reliability of survey instruments, summarize variables using descriptive statistics, examine correlations between variables, and ensure the assumptions of regression are met. The document serves as a guide for properly specifying, estimating, and evaluating a regression model.
Reliability refers to the consistency of test scores. A reliable test will produce similar results over multiple test administrations. There are several methods for determining reliability, including internal consistency, test-retest reliability, inter-rater reliability, and split-half reliability. Validity refers to how well a test measures what it intends to measure. Validity can be established through face validity, construct validity, content validity, and criterion validity. Both reliability and validity are important for a high quality test, as a test can be reliable without being valid.
The document discusses reliability and validity in research tools. It defines reliability as consistency of data collection and validity as measuring what is intended. It discusses different types of reliability - stability over time, equivalence of alternate forms, and internal consistency. It also discusses different types of validity - content, criterion, and construct validity. Factors like threats to groups, regression, time, and respondents' history can affect validity. Reliability ensures consistency while validity determines accuracy of what is measured.
Aminullah assagaf model regresi lengkap 10 agustus 2021_(sobel, path, outlier)Aminullah Assagaf
This document provides an overview of various statistical analyses that can be conducted when performing a complete regression model, including validity testing, reliability testing, descriptive statistics, correlation analysis, multicollinearity testing, autocorrelation testing, heteroscedasticity testing, normality testing, linearity testing, conceptual framework development, regression equation, F-statistic testing, t-statistic testing, coefficient of determination, path analysis, and Sobel testing. Examples are provided for validity testing, reliability testing, descriptive statistics, and correlation analysis using sample data.
Aminullah assagaf model regresi lengkap 10 agustus 2021_(sobel, path, outlier)Aminullah Assagaf
This document provides information on statistical analysis techniques used to analyze survey data, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression analysis. It discusses conducting validity and reliability tests on survey instruments before collecting data. It also covers descriptive statistics such as measures of central tendency and dispersion. Correlation analysis is used to determine the strength and direction of relationships between variables. Regression analysis involves developing a regression model and testing hypotheses about variables.
Lesson 11 Understanding Data and Ways to Systematically Collect Data.pptGerfelChan1
1. Measurement in research involves systematically assigning numbers to represent attributes of people, objects, or events. This allows for consistent interpretation when the same measurement process is used.
2. Reliability and validity are important concepts for enhancing accuracy in research. Reliability refers to consistency of responses, while validity refers to a measurement tool accurately measuring what it intends to.
3. There are different types of validity including face validity, content validity, criterion-related validity, and construct validity. Reliability can be assessed through stability, homogeneity, and equivalence tests using various statistical analyses and coefficients.
Aminullah assagaf model regresi lengkap (ada sobel & peth) 4 agst 2021Aminullah Assagaf
This document discusses various statistical tests that will be used in a research study, including: validity testing, reliability testing, descriptive statistics, correlation analysis, and regression analysis. Validity and reliability testing are used to ensure the measurement instrument is accurately measuring the intended constructs. Descriptive statistics will be used to summarize the basic characteristics of the data. Correlation analysis and regression analysis will be used to determine the relationship between variables and develop predictive models.
Assessing convergent and discriminant validity in the
ADHD-R IV rating scale:
User-written commands for Average Variance Extracted
(AVE), Composite Reliability (CR), and
Heterotrait-Monotrait ratio of correlations (HTMT).
Test validity refers to validating the appropriate use of a test score for a specific context or purpose. Validity is determined by studying test results in the intended setting of use, as a test may be suitable for one purpose but not another. Validity is a matter of degree rather than an absolute quality, and establishing validity requires empirical evidence and theoretical justification that the intended inferences from test scores are adequate and appropriate.
Measurement theory is the formal and logical theory of necessary and sufficient conditions of attributing measures to objects or events. There are different types of measurement scales including nominal, ordinal, interval, and ratio scales, which provide increasing levels of accurate information about the characteristics being measured. The goals of measurement are accuracy and precision, while validity, reliability, precision, and accuracy are terms used to evaluate the quality of a measurement. Measurement errors can be either systematic, occurring consistently in one direction, or random, varying unpredictably.
This document discusses various statistical tools used in decision making, including regression analysis, confidence intervals, comparison tests, and analysis of variance. It provides examples of how regression analysis can be used to determine correlations and unknown parameters. It also explains how confidence intervals are calculated and used to determine how reliable a sample statistic is in estimating an unknown population parameter. Comparison tests are outlined as a method to determine if one process or supplier is better than another.
ESTABLISHING THE VALIDITY AND RELIABILITY [Autosaved].pptxChedingArnigo1
The document discusses the importance of establishing validity and reliability of research instruments. It defines various types of validity like face validity, content validity, criterion validity, and construct validity. It also explains ways to assess reliability, which include test-retest reliability, split-half method, and internal consistency. Establishing validity and reliability of instruments ensures accurate and consistent measurement.
This document provides an overview of mechanical measurement and metrology. It defines key terms like hysteresis, linearity, resolution, and drift. It discusses the need for measurement, static performance characteristics of instruments like repeatability and accuracy. It also describes the components of a generalized measurement system including the primary sensing element, variable conversion element, data processing element and more. Finally, it covers topics like errors in measurement, objectives of measurement and metrology, and elements that can affect a measuring system.
Ag Extn.504 :- RESEARCH METHODS IN BEHAVIOURAL SCIENCE Pradip Limbani
This document discusses measurement, validity, reliability, and levels of measurement. It provides definitions and methods for testing validity and reliability. There are four levels of measurement - nominal, ordinal, interval, and ratio - which determine the appropriate statistical analyses. Validity is tested through content, predictive, concurrent, and construct validity. Reliability refers to consistency and is estimated through test-retest and internal consistency methods. Measurement is important for research as it allows for goal setting and evaluating outcomes.
This document discusses validation in the pharmaceutical industry. It defines validation as confirming that a process, procedure, or system achieves the desired results. There are various types of validation including process, equipment, facility, analytical method, and computer system validation. Process validation involves establishing scientific evidence that a process is capable of consistently producing quality products. The key stages of process validation are process design, process qualification, and continued process verification. Common validation parameters discussed include accuracy, precision, specificity, linearity, range, limit of detection, limit of quantification, ruggedness, and robustness.
This document provides an overview of structural equation modeling (SEM) techniques. It discusses key SEM concepts like latent constructs, measurement models, structural models, and model estimation. The document also covers advanced SEM topics such as comparing covariance-based and variance-based approaches, handling formative versus reflective measures, and analyzing moderators and mediators. Overall, the document serves as a guide for researchers to understand and apply quantitative data analysis methods using SEM.
There are two main types of attitudinal scales: rating scales and ranking scales. Rating scales measure responses regarding an object using categories, while ranking scales elicit preferences by comparing objects. Ten common rating scales are described, including Likert, semantic differential, and numerical scales. Ranking scales include paired comparisons and forced choice methods. Goodness of measures is ensured through item analysis, reliability, and validity testing. Reliability examines consistency over time through methods like test-retest and internal consistency. Validity assesses measuring the intended concept using techniques such as content, criterion, and construct validity.
Reliability refers to the consistency and repeatability of measurements or test results. There are several types of reliability: test-retest, parallel forms, split-half, and internal consistency. Factors that can affect reliability include test length and homogeneity, item difficulty and discrimination, instructions, selection of items, and environmental conditions during testing. Reliability is important and can be improved by creating clear measurement directions and expanding the sample of test items.
The document discusses various aspects of reliability and validity in psychological research. It defines reliability as consistency or repeatability of a measure. Several methods of assessing reliability are described, including test-retest reliability, internal consistency reliability (using split-half, Kuder-Richardson, and Cronbach's alpha tests), and parallel-forms reliability. Validity refers to how well a test measures what it is intended to measure. Different types of validity are covered, such as face validity, content validity, criterion-related (predictive and concurrent) validity, and construct validity.
The document describes the steps in conducting a complete regression analysis, including validity testing, reliability testing, descriptive statistics, correlation analysis, and various regression diagnostics. It provides examples of analyzing data from a study using SPSS to test the validity and reliability of survey instruments, summarize variables using descriptive statistics, examine correlations between variables, and ensure the assumptions of regression are met. The document serves as a guide for properly specifying, estimating, and evaluating a regression model.
Reliability refers to the consistency of test scores. A reliable test will produce similar results over multiple test administrations. There are several methods for determining reliability, including internal consistency, test-retest reliability, inter-rater reliability, and split-half reliability. Validity refers to how well a test measures what it intends to measure. Validity can be established through face validity, construct validity, content validity, and criterion validity. Both reliability and validity are important for a high quality test, as a test can be reliable without being valid.
The document discusses reliability and validity in research tools. It defines reliability as consistency of data collection and validity as measuring what is intended. It discusses different types of reliability - stability over time, equivalence of alternate forms, and internal consistency. It also discusses different types of validity - content, criterion, and construct validity. Factors like threats to groups, regression, time, and respondents' history can affect validity. Reliability ensures consistency while validity determines accuracy of what is measured.
Aminullah assagaf model regresi lengkap 10 agustus 2021_(sobel, path, outlier)Aminullah Assagaf
This document provides an overview of various statistical analyses that can be conducted when performing a complete regression model, including validity testing, reliability testing, descriptive statistics, correlation analysis, multicollinearity testing, autocorrelation testing, heteroscedasticity testing, normality testing, linearity testing, conceptual framework development, regression equation, F-statistic testing, t-statistic testing, coefficient of determination, path analysis, and Sobel testing. Examples are provided for validity testing, reliability testing, descriptive statistics, and correlation analysis using sample data.
Aminullah assagaf model regresi lengkap 10 agustus 2021_(sobel, path, outlier)Aminullah Assagaf
This document provides information on statistical analysis techniques used to analyze survey data, including validity testing, reliability testing, descriptive statistics, correlation analysis, and regression analysis. It discusses conducting validity and reliability tests on survey instruments before collecting data. It also covers descriptive statistics such as measures of central tendency and dispersion. Correlation analysis is used to determine the strength and direction of relationships between variables. Regression analysis involves developing a regression model and testing hypotheses about variables.
Lesson 11 Understanding Data and Ways to Systematically Collect Data.pptGerfelChan1
1. Measurement in research involves systematically assigning numbers to represent attributes of people, objects, or events. This allows for consistent interpretation when the same measurement process is used.
2. Reliability and validity are important concepts for enhancing accuracy in research. Reliability refers to consistency of responses, while validity refers to a measurement tool accurately measuring what it intends to.
3. There are different types of validity including face validity, content validity, criterion-related validity, and construct validity. Reliability can be assessed through stability, homogeneity, and equivalence tests using various statistical analyses and coefficients.
Aminullah assagaf model regresi lengkap (ada sobel & peth) 4 agst 2021Aminullah Assagaf
This document discusses various statistical tests that will be used in a research study, including: validity testing, reliability testing, descriptive statistics, correlation analysis, and regression analysis. Validity and reliability testing are used to ensure the measurement instrument is accurately measuring the intended constructs. Descriptive statistics will be used to summarize the basic characteristics of the data. Correlation analysis and regression analysis will be used to determine the relationship between variables and develop predictive models.
Assessing convergent and discriminant validity in the
ADHD-R IV rating scale:
User-written commands for Average Variance Extracted
(AVE), Composite Reliability (CR), and
Heterotrait-Monotrait ratio of correlations (HTMT).
Test validity refers to validating the appropriate use of a test score for a specific context or purpose. Validity is determined by studying test results in the intended setting of use, as a test may be suitable for one purpose but not another. Validity is a matter of degree rather than an absolute quality, and establishing validity requires empirical evidence and theoretical justification that the intended inferences from test scores are adequate and appropriate.
Measurement theory is the formal and logical theory of necessary and sufficient conditions of attributing measures to objects or events. There are different types of measurement scales including nominal, ordinal, interval, and ratio scales, which provide increasing levels of accurate information about the characteristics being measured. The goals of measurement are accuracy and precision, while validity, reliability, precision, and accuracy are terms used to evaluate the quality of a measurement. Measurement errors can be either systematic, occurring consistently in one direction, or random, varying unpredictably.
This document discusses various statistical tools used in decision making, including regression analysis, confidence intervals, comparison tests, and analysis of variance. It provides examples of how regression analysis can be used to determine correlations and unknown parameters. It also explains how confidence intervals are calculated and used to determine how reliable a sample statistic is in estimating an unknown population parameter. Comparison tests are outlined as a method to determine if one process or supplier is better than another.
ESTABLISHING THE VALIDITY AND RELIABILITY [Autosaved].pptxChedingArnigo1
The document discusses the importance of establishing validity and reliability of research instruments. It defines various types of validity like face validity, content validity, criterion validity, and construct validity. It also explains ways to assess reliability, which include test-retest reliability, split-half method, and internal consistency. Establishing validity and reliability of instruments ensures accurate and consistent measurement.
This document provides an overview of mechanical measurement and metrology. It defines key terms like hysteresis, linearity, resolution, and drift. It discusses the need for measurement, static performance characteristics of instruments like repeatability and accuracy. It also describes the components of a generalized measurement system including the primary sensing element, variable conversion element, data processing element and more. Finally, it covers topics like errors in measurement, objectives of measurement and metrology, and elements that can affect a measuring system.
Ag Extn.504 :- RESEARCH METHODS IN BEHAVIOURAL SCIENCE Pradip Limbani
This document discusses measurement, validity, reliability, and levels of measurement. It provides definitions and methods for testing validity and reliability. There are four levels of measurement - nominal, ordinal, interval, and ratio - which determine the appropriate statistical analyses. Validity is tested through content, predictive, concurrent, and construct validity. Reliability refers to consistency and is estimated through test-retest and internal consistency methods. Measurement is important for research as it allows for goal setting and evaluating outcomes.
This document discusses validation in the pharmaceutical industry. It defines validation as confirming that a process, procedure, or system achieves the desired results. There are various types of validation including process, equipment, facility, analytical method, and computer system validation. Process validation involves establishing scientific evidence that a process is capable of consistently producing quality products. The key stages of process validation are process design, process qualification, and continued process verification. Common validation parameters discussed include accuracy, precision, specificity, linearity, range, limit of detection, limit of quantification, ruggedness, and robustness.
This document provides an overview of structural equation modeling (SEM) techniques. It discusses key SEM concepts like latent constructs, measurement models, structural models, and model estimation. The document also covers advanced SEM topics such as comparing covariance-based and variance-based approaches, handling formative versus reflective measures, and analyzing moderators and mediators. Overall, the document serves as a guide for researchers to understand and apply quantitative data analysis methods using SEM.
There are two main types of attitudinal scales: rating scales and ranking scales. Rating scales measure responses regarding an object using categories, while ranking scales elicit preferences by comparing objects. Ten common rating scales are described, including Likert, semantic differential, and numerical scales. Ranking scales include paired comparisons and forced choice methods. Goodness of measures is ensured through item analysis, reliability, and validity testing. Reliability examines consistency over time through methods like test-retest and internal consistency. Validity assesses measuring the intended concept using techniques such as content, criterion, and construct validity.
Reliability refers to the consistency and repeatability of measurements or test results. There are several types of reliability: test-retest, parallel forms, split-half, and internal consistency. Factors that can affect reliability include test length and homogeneity, item difficulty and discrimination, instructions, selection of items, and environmental conditions during testing. Reliability is important and can be improved by creating clear measurement directions and expanding the sample of test items.
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3. INDICE PRESENTACIÓN
1
2
TEMA 1: VALIDEZ Y CONFIABILIDAD DE UN
INSTRUMENTO DE INVESTIGACIÓN
TEMA 2: FACTORES QUE AFECTAN
LA VALIDEZY CONFIABILIDAD DE
UN INSTRUMENTO
4. “Si solicitamos a una persona que conoce sobre validación de instrumentos, tanto del
punto de vista cuantitativo como cualitativo, que nos ayude a evaluar si los ítems que
hemos redactado son correctos, esta persona es un especialista en evaluar
cuestionarios, pero no es un experto en el tema de las costumbres que tienen a la
hora del parto las mujeres; por lo tanto, este profesional nos podrá servir como juez
pero no como experto”
JUEZ EXPERTO
5. El ser humano siempre a tenido la inquietud de saber la
medida de algo. por ejemplo de un edificio, de un objeto, de
personas entre otros. Medir siempre ha sido una necesidad
del ser humano y la investigación científica no esta ajena a
estos parámetros de medición, tanto en las ciencias sociales
como en la ciencias naturales.
Medición consiste en hacer una comparación de un valor o
una magnitud frente a un patrón de medida. Según Carmines
y Zeller, (1991). puede definir a la medición como “el
proceso de vincular conceptos abstractos con indicadores
empíricos”, el cual se realiza mediante un plan explícito y
organizado para clasificar (y con frecuencia cuantificar) los
datos disponibles (los indicadores), en términos del
concepto que el investigador tiene en mente
CONCEPTO DE MEDICIÓN
6. Los instrumentos deben ser:
Kerlinger (1979)
CONFIABLE Ó FIABLE VÁLIDO
Grado de aplicación repetida al
mismo sujeto produce iguales
resultados
Grado en que un instrumento
realmente mide la variable que se
pretenda medir
8. Grado en que un instrumento refleja un dominio específico de
contenido de lo que se mide.
Validez de Contenido
Ejemplo:
Una prueba de operaciones aritméticas no es válida si solo
incluye la suma y la resta y excluye la multiplicación y la
división.
La validéz cualitativa por juicio de experto y cuantitativa con
la Técnica Aiken o Prueba binomial
Según plantea (De Arquer, M. I. 2011) se debe considerar que la
validez de contenido cualitativamente es una cuestión de
juicio, se estima de manera subjetiva o intersubjetiva
empleando, usualmente, el denominado Juicio de Expertos.
12. Categoría N
Proporció
n
observad
a
Prop. de
prueba
Sig.
exacta
(bilateral)
Juez1 Grupo 1 Si 28 .90 .50 .000
Grupo 2 No 3 .10
Total 31 1.00
Juez2 Grupo 1 Si 27 .87 .50 .000
Grupo 2 No 4 .13
Total 31 1.00
Juez3 Grupo 1 Si 26 .84 .50 .000
Grupo 2 No 5 .16
Total 31 1.00
Juez4 Grupo 1 No 4 .13 .50 .000
Grupo 2 Si 27 .87
Total 31 1.00
Juez5 Grupo 1 Si 28 .90 .50 .000
Grupo 2 No 3 .10
Total 31 1.00
Prueba binomial con
SPSS
P promedio = 0.000
P promedio < 0.05,
La Prueba Binomial indica que el
instrumento de observación es valido
13. Validez de Criterio
establece la validez de un instrumento de medición comparándola con algún
criterio externo.
Este criterio es estándar con el que se juzga la validez del instrumento .
Entre los resultados del instrumento de la medición se relaciona más al criterio, la
validez del criterio será mayor.
Según Hernández, et al (2014), la validez de criterio de un instrumento
de medición se establece al comparar sus resultados con los de algún
criterio externo que pretende medir lo mismo.
Si el criterio se fija:
validez concurrente (presente) validez predictiva
(futuro)
14. LA CONFIABILIDAD
La confiabilidad se refiere al grado de estabilidad que al medir
presenta un determinado instrumento. Esto en el sentido de que si
aplicamos repetidamente un instrumento al mismo sujeto u objeto
en iguales condiciones y en tiempos próximos debe producir
iguales resultados. Una balanza o un termómetro serán confiables si
al pesar o al medir en dos ocasiones seguidas, se obtienen los
mismos resultados.
Según Hernández, et al (2014, p. 200), La
confiabilidad de un instrumento de medición se
refiere al grado en que su aplicación repetida al
mismo individuo u objeto produce resultados
iguales.
15. TIPOS DE CONFIABILIDAD
1.- Medida de Estabilidad
También llamada confiabilidad test - retest. Un mismo instrumento
es aplicado dos o más veces a un mismo grupo de personas en
condiciones similares. Si la correlación entre los resultados de las
diferentes aplicaciones es altamente positiva, el instrumento se
considera confiable.
16. 2) Método de formas alternativas o paralelas.
En este esquema no se administra el mismo instrumento de
medición, sino dos o más versiones equivalentes de éste. Las
versiones (casi siempre dos) son similares en contenido,
instrucciones, duración y otras características, y se administran a un
mismo grupo de personas simultáneamente o dentro de un periodo
corto. El instrumento es confiable si la correlación entre los
resultados de ambas administraciones es positiva de manera
significativa (Rodríguez,2006b). Citado por Hernández, et al (2014,
p. 294),
17. 3) Método de mitades partidas (Split-halves).
El método de mitades partidas necesita sólo una aplicación de la
medición. Específicamente el conjunto total de ítems o reactivos se
divide en dos mitades equivalentes y se comparan las puntuaciones
o los resultados de ambas. Si el instrumento es confiable, las
puntuaciones de las mitades deben estar correlacionadas. En este
caso se pueden utilizar:
b) Coeficiente de Guttman
𝑟𝑡𝑡 = 2 1 −
𝑆𝑎
2+ 𝑆𝑏
2
𝑆𝑡
2
18. 4) Medidas de coherencia o consistencia interna.
Miden la homogeneidad de los ítems. Requiere una sola administración del
instrumento de medición. No necesita dividir los ítems del instrumento. Se
aplica la medición y se calcula el coeficiente de confiabilidad, Hernández, et
al (2014).
a) Alfa de Cronbach. Se usa cuando los ítems tienen alternativas
policotómicas como las escalas Likert; la cual puede tomar valores entre
0 y 1, donde 0 significa confiablidad nula y 1 representa confiabilidad
total, el coeficiente 𝛼 de Cronbach se puede calcular con la siguiente
fórmula:
19. ALUMNO
ÍTEMS PUNTUACIÓN
TOTAL
1 2 3 4 5 6 7 8 9 10
CESAR 5 1 2 3 4 5 1 2 3 4 30
EDUARDO 4 4 4 4 4 4 4 4 4 4 40
MARÍA 5 5 5 4 5 4 4 4 4 4 44
ANA 3 3 3 3 3 3 3 3 3 4 31
SOFÍA 2 2 2 2 2 2 2 2 2 2 20
RITA 5 1 2 3 4 5 1 2 3 4 30
TOTAL
24 16 18 19 22 23 15 17 19 22
Varianza
Total 71.9
PROMEDIO 4.00 2.67 3.00 3.17 3.67 3.83 2.50 2.83 3.17 3.67
VARIANZA
INDIVIDUAL
2 3 2 1 1 1 2 1 1 1
S_suma
Var. Ind
13
Varianza
muestral = Varianza
k=Número de
ítems
10
Alfa de Cronbach
Alfa de Cronbach 0.9107
Si
2 = varianza de las
puntuaciones en cada ítem por
todos los examinados.
St
2 = varianza total de las
puntuaciones de filas de todos
los examinados.
K = número de ítems
El coeficiente Alfa de Cronbach
0,91 indica que el instrumento
tiene alta confiabilidad o
excelente confiabilidad
20. Resumen de procesamiento de casos
N %
Casos Válido 12 100,0
Excluidoa 0 ,0
Total 12 100,0
a. La eliminación por lista se basa en todas las variables
del procedimiento.
Estadísticas de fiabilidad
Alfa de Cronbach N de elementos
,882 20