Psychological properties can be measured quantitatively through systematic tools like psychological tests. Measurement involves assigning numbers to objects or people in a way that represents specific attributes. There are different types of measurement scales including nominal, ordinal, interval, and ratio scales. Validity and reliability are important concepts in evaluating psychological tests, where validity refers to a test measuring what it intends to and reliability is consistency of scores. Variability, correlation, and prediction are key statistical concepts involving the spread, relationship, and forecasting of test scores.
The document discusses different types of errors that can occur in measurement. It describes gross errors, systematic errors like instrumental errors and environmental errors, and random errors. It also defines key terms used to analyze errors like limit of reading, greatest possible error, and discusses analyzing measurement data using statistical methods like the mean, standard deviation, variance and histograms. Measurement errors can occur due to issues like parallax, calibration, limits of the measuring device, and are analyzed statistically.
The document discusses measurement errors and standards. It defines key terms like instruments, measurements, standards, and different types of errors. It explains absolute and relative errors, accuracy, precision and resolution. It discusses sources of errors like gross errors, systematic errors from instruments and environment, and random errors. Finally, it categorizes measurement standards into international, primary, secondary and working standards based on their accuracy and purpose.
general measuring systems , basic concept of measurement ,importance of measurement ,errors in measurement ,calibration of instrument,biomedical medical sensors and measurementation,instrumentation and measurementation ,static and dynamic characteristics of measurement,block diagram of general measuring system.how to avoid errors in measurement.
Measurement involves uncertainty from errors and imprecision. An error is the difference between measured and expected values, while uncertainty summarizes the error. Random errors arise from unpredictable fluctuations, and systematic errors are reproducible biases. Accuracy refers to closeness to the true value, while precision reflects consistency of measurements. Uncertainty is quantified from instrument resolution, repeated measurements, or comparison to a standard value. It is calculated and reported with the measurement result.
This document discusses the concepts of reliability and validity in measurement. Reliability refers to the consistency of a measurement and is assessed through stability and equivalence. Stability looks at consistency over repeated measurements using test-retest reliability and parallel forms. Equivalence examines consistency between two equivalent test forms using split-half reliability. Validity refers to how accurately an instrument measures a construct and is assessed through predictive validity, concurrent validity, and content validity.
There are two main types of errors in measurement: systematic errors, which always produce results in the same direction, and random errors, which occur unpredictably due to various factors. The accuracy of a measurement indicates how close it is to the accepted value, while the precision refers to the agreement between multiple measurements of the same quantity. Taking the average of repeated measurements reduces the impact of random errors, but the uncertainty in any measurement must be reported using plus-and-minus values to indicate the possible variance.
The document discusses the concepts of validity and reliability in research. It defines validity as the degree of accuracy and appropriateness of a study in measuring what it intends to measure. There are three main types of validity: content validity, face validity, and criterion validity. Reliability refers to the consistency and stability of results over time. The four main types of reliability are equivalency, stability, internal consistency, and interrater reliability. Basic statistical concepts like the mean, variance, and standard deviation are also covered.
This document discusses the concept of validity in psychological testing and research. It provides definitions of validity from authoritative sources like the American Psychological Association. It distinguishes between different types of validity like construct validity, content validity, criterion validity, predictive validity, concurrent validity, and experimental validity, which includes statistical conclusion validity, internal validity, external validity, and ecological validity. The relationships between these types of validity are explored in depth through multiple examples and implications. The document emphasizes that validity concerns the appropriate interpretation and use of test scores rather than a test itself. It is intended as a guide on validity for Dr. GHIAS UL HAQ from SARHAD UNIVERSITY OF INFORMATION TECHNOLOGY, PESHAWAR.
The document discusses different types of errors that can occur in measurement. It describes gross errors, systematic errors like instrumental errors and environmental errors, and random errors. It also defines key terms used to analyze errors like limit of reading, greatest possible error, and discusses analyzing measurement data using statistical methods like the mean, standard deviation, variance and histograms. Measurement errors can occur due to issues like parallax, calibration, limits of the measuring device, and are analyzed statistically.
The document discusses measurement errors and standards. It defines key terms like instruments, measurements, standards, and different types of errors. It explains absolute and relative errors, accuracy, precision and resolution. It discusses sources of errors like gross errors, systematic errors from instruments and environment, and random errors. Finally, it categorizes measurement standards into international, primary, secondary and working standards based on their accuracy and purpose.
general measuring systems , basic concept of measurement ,importance of measurement ,errors in measurement ,calibration of instrument,biomedical medical sensors and measurementation,instrumentation and measurementation ,static and dynamic characteristics of measurement,block diagram of general measuring system.how to avoid errors in measurement.
Measurement involves uncertainty from errors and imprecision. An error is the difference between measured and expected values, while uncertainty summarizes the error. Random errors arise from unpredictable fluctuations, and systematic errors are reproducible biases. Accuracy refers to closeness to the true value, while precision reflects consistency of measurements. Uncertainty is quantified from instrument resolution, repeated measurements, or comparison to a standard value. It is calculated and reported with the measurement result.
This document discusses the concepts of reliability and validity in measurement. Reliability refers to the consistency of a measurement and is assessed through stability and equivalence. Stability looks at consistency over repeated measurements using test-retest reliability and parallel forms. Equivalence examines consistency between two equivalent test forms using split-half reliability. Validity refers to how accurately an instrument measures a construct and is assessed through predictive validity, concurrent validity, and content validity.
There are two main types of errors in measurement: systematic errors, which always produce results in the same direction, and random errors, which occur unpredictably due to various factors. The accuracy of a measurement indicates how close it is to the accepted value, while the precision refers to the agreement between multiple measurements of the same quantity. Taking the average of repeated measurements reduces the impact of random errors, but the uncertainty in any measurement must be reported using plus-and-minus values to indicate the possible variance.
The document discusses the concepts of validity and reliability in research. It defines validity as the degree of accuracy and appropriateness of a study in measuring what it intends to measure. There are three main types of validity: content validity, face validity, and criterion validity. Reliability refers to the consistency and stability of results over time. The four main types of reliability are equivalency, stability, internal consistency, and interrater reliability. Basic statistical concepts like the mean, variance, and standard deviation are also covered.
This document discusses the concept of validity in psychological testing and research. It provides definitions of validity from authoritative sources like the American Psychological Association. It distinguishes between different types of validity like construct validity, content validity, criterion validity, predictive validity, concurrent validity, and experimental validity, which includes statistical conclusion validity, internal validity, external validity, and ecological validity. The relationships between these types of validity are explored in depth through multiple examples and implications. The document emphasizes that validity concerns the appropriate interpretation and use of test scores rather than a test itself. It is intended as a guide on validity for Dr. GHIAS UL HAQ from SARHAD UNIVERSITY OF INFORMATION TECHNOLOGY, PESHAWAR.
A standardized test is any test where all test takers answer the same questions in a consistent manner that is scored uniformly. There are two main types - norm-referenced tests compare performance to others, while criterion-referenced tests assess performance against a set of objectives. Standardized tests can measure achievement, aptitude, or be used for college admissions. Scores are reported using raw scores, percentiles, or stanines.
This short SlideShare presentation explores a basic overview of test reliability and test validity. Validity is the degree to which a test measures what it is supposed to measure. Reliability is the degree to which a test consistently measures whatever it measures. Examples are given as well as a slide on considerations for writing test questions that demand higher-order thinking.
This document discusses errors in measurement and different types of errors. It explains that there are five main elements that can cause errors: standards, work pieces, instruments, persons, and environment. There are three types of errors: systematic errors, which occur due to imperfections and are of fixed magnitude; random errors, which occur irregularly; and statistical analysis can be used to analyze random errors through calculations of mean, range, deviation, and standard deviation. Systematic errors include instrumental errors from faulty instruments, environmental errors from external conditions, and observational errors from human factors like parallax.
The document discusses key qualities of measurement devices: validity, reliability, practicality, and backwash effect. It defines each quality and provides examples. Validity refers to what a test measures, and includes content, construct, criterion-related, concurrent, and predictive validity. Reliability is how consistent measurements are, including equivalency, stability, internal, and inter-rater reliability. Practicality means a test is easy to construct, administer, score and interpret. Backwash effect is a test's influence on teaching and learning.
Measures Of Central Tendency And Variabilitytoledo98
Measures of central tendency include the mean, median and mode which provide a single number to represent all values in a group. Measures of variability describe the spread or shape of data points in a group and indicate whether the data is homogeneous or heterogeneous. These tools in descriptive statistics help analyze and describe arrays of data.
Topic: What is Reliability and its Types?
Student Name: Kanwal Naz
Class: B.Ed 1.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
1) The document discusses various standards and units of measurement including fundamental and derived units.
2) It describes different types of standards including international, primary, secondary, working, current, voltage, resistance, capacitance, and time/frequency standards.
3) The key points are that standards define units of measurement and are classified based on their level of accuracy and use from international to working standards used in laboratories.
Classical Test Theory and Item Response Theorysaira kazim
Classical test theory and item response theory are two statistical theories for analyzing test performance and relating it to examinee abilities. Classical test theory defines test scores, true scores, and error scores at the test level, assumes weak assumptions about test data, and specifies a linear relationship between ability and item performance. Item response theory defines item characteristic functions at the item level, assumes stronger assumptions about test data, and specifies a nonlinear relationship between ability and item performance.
It talks about the different types of validity in assessment.
* Face Validity
* Content Validity
* Predictive Validity
* Concurrent Validity
* Construct Validity
This document outlines general principles of assessment:
1. Clearly specifying the learning goals is a priority in assessment.
2. Assessment procedures should be selected based on their relevance to what is being measured.
3. Comprehensive assessment requires a variety of procedures like multiple choice, essays, projects, and observations to measure different learning outcomes.
A good measuring tools is one which can secure valid evidence of desired change of behaviour .
It is not synonymous with paper or pencil tests.
It evaluates one specific performance by rating behaviour as it progresses and to sum up many casual observations over a period of time.
This document discusses key concepts related to validity and reliability in measurement devices. It defines validity as measuring what the device is intended to measure, and reliability as consistency of measurement. The document outlines several types of validity including content, construct, criterion (concurrent and predictive), and face validity. It also discusses reliability in terms of equivalency, stability, internal consistency, and interrater reliability. Validity and reliability are closely related but a test can be reliable without being valid. The document also notes sources of error in measurements and the backwash effect of test design on teaching.
Measures of central tendency and dispersionRajaKrishnan M
This document provides an overview of measures of central tendency and dispersion that are commonly used in descriptive statistics. It defines and provides examples of the mean, median, mode, range, and standard deviation. It also discusses frequency tables, histograms, and the shapes that distributions can take. The goal of descriptive statistics is to summarize and describe data through techniques like these in order to understand the characteristics of variables.
This document discusses the concept of validity in psychological testing. It defines validity as the degree to which a test measures what it claims to measure. There are three main types of validity: content validity, which concerns how well a test represents the content area it aims to measure; criterion-related validity, which compares test scores to external criteria; and construct validity, which evaluates how well a test measures hypothetical constructs. Validity is influenced by factors like test length and the range of abilities in the sample population. A test must demonstrate validity to ensure the inferences made from its results are appropriate and meaningful.
Construct validity assesses how accurately theories and ideas have been translated into procedures and measures. It is established by defining terms, proving control over variables, and supporting theory with empirical evidence. Content validity ensures test items adequately represent the domain being measured. It is achieved through thorough literature review, constructing procedures accordingly, and expert evaluation. Both validity types are important for drawing conclusive results from studies, though establishing validity can be challenging for complex studies with many interacting variables.
This presentation is about the objectivity of tests, It presents the definition of objective tests, and its meaning.
It reflects upon the objectivity of scoring, types of objective tests, merits and demerits about the same.
Reliability refers to the consistency of test scores across different administrations of the test. There are two aspects of reliability - reliability of scores over time and reliability of scoring. Reliability of scoring is easier to achieve for objectively scored tests but is important for subjectively scored tests like essays. There are two types of scorer reliability: intra-rater reliability which measures consistency of a single rater's scores and inter-rater reliability which measures consistency between multiple raters' scores. Reliability can be quantified using reliability coefficients which measure the correlation between scores from different test administrations or halves of a test. A test cannot be valid unless it is also reliable, but a reliable test may not be valid as there
Correlation measures the strength and direction of association between two variables. It ranges from -1 to 1, where 0 indicates no association and values closer to 1 or -1 indicate stronger positive or negative associations, respectively. The document provides examples of positive correlation between variables such as height and weight that increase together, and negative correlation where variables such as TV time and grades move in opposite directions. Formulas and different types of correlation including partial and multiple correlation are also defined in the document.
The document discusses four levels of measurement (nominal, ordinal, interval, and ratio) and how they relate to different types of data. It then covers topics in descriptive statistics including frequency distributions, measures of central tendency (mode, median, mean), and measures of dispersion (variance and standard deviation). Key points are that nominal variables use the mode, ordinal variables use the median, and interval/ratio variables use the mean. Variance and standard deviation quantify how spread out values are from the mean.
ch-4-measures-of-variability-11 2.ppt for nursingwindri3
This document discusses measures of variability used in statistics. It defines variability as the spread or dispersion of scores. The key measures of variability discussed are the range, variance, and standard deviation. The range is the difference between the highest and lowest scores. The variance is the average of the squared deviations from the mean and represents how far the scores deviate from the mean. The standard deviation is the square root of the variance and represents how much scores typically deviate from the mean. Larger standard deviations indicate greater variability in the scores.
A standardized test is any test where all test takers answer the same questions in a consistent manner that is scored uniformly. There are two main types - norm-referenced tests compare performance to others, while criterion-referenced tests assess performance against a set of objectives. Standardized tests can measure achievement, aptitude, or be used for college admissions. Scores are reported using raw scores, percentiles, or stanines.
This short SlideShare presentation explores a basic overview of test reliability and test validity. Validity is the degree to which a test measures what it is supposed to measure. Reliability is the degree to which a test consistently measures whatever it measures. Examples are given as well as a slide on considerations for writing test questions that demand higher-order thinking.
This document discusses errors in measurement and different types of errors. It explains that there are five main elements that can cause errors: standards, work pieces, instruments, persons, and environment. There are three types of errors: systematic errors, which occur due to imperfections and are of fixed magnitude; random errors, which occur irregularly; and statistical analysis can be used to analyze random errors through calculations of mean, range, deviation, and standard deviation. Systematic errors include instrumental errors from faulty instruments, environmental errors from external conditions, and observational errors from human factors like parallax.
The document discusses key qualities of measurement devices: validity, reliability, practicality, and backwash effect. It defines each quality and provides examples. Validity refers to what a test measures, and includes content, construct, criterion-related, concurrent, and predictive validity. Reliability is how consistent measurements are, including equivalency, stability, internal, and inter-rater reliability. Practicality means a test is easy to construct, administer, score and interpret. Backwash effect is a test's influence on teaching and learning.
Measures Of Central Tendency And Variabilitytoledo98
Measures of central tendency include the mean, median and mode which provide a single number to represent all values in a group. Measures of variability describe the spread or shape of data points in a group and indicate whether the data is homogeneous or heterogeneous. These tools in descriptive statistics help analyze and describe arrays of data.
Topic: What is Reliability and its Types?
Student Name: Kanwal Naz
Class: B.Ed 1.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
1) The document discusses various standards and units of measurement including fundamental and derived units.
2) It describes different types of standards including international, primary, secondary, working, current, voltage, resistance, capacitance, and time/frequency standards.
3) The key points are that standards define units of measurement and are classified based on their level of accuracy and use from international to working standards used in laboratories.
Classical Test Theory and Item Response Theorysaira kazim
Classical test theory and item response theory are two statistical theories for analyzing test performance and relating it to examinee abilities. Classical test theory defines test scores, true scores, and error scores at the test level, assumes weak assumptions about test data, and specifies a linear relationship between ability and item performance. Item response theory defines item characteristic functions at the item level, assumes stronger assumptions about test data, and specifies a nonlinear relationship between ability and item performance.
It talks about the different types of validity in assessment.
* Face Validity
* Content Validity
* Predictive Validity
* Concurrent Validity
* Construct Validity
This document outlines general principles of assessment:
1. Clearly specifying the learning goals is a priority in assessment.
2. Assessment procedures should be selected based on their relevance to what is being measured.
3. Comprehensive assessment requires a variety of procedures like multiple choice, essays, projects, and observations to measure different learning outcomes.
A good measuring tools is one which can secure valid evidence of desired change of behaviour .
It is not synonymous with paper or pencil tests.
It evaluates one specific performance by rating behaviour as it progresses and to sum up many casual observations over a period of time.
This document discusses key concepts related to validity and reliability in measurement devices. It defines validity as measuring what the device is intended to measure, and reliability as consistency of measurement. The document outlines several types of validity including content, construct, criterion (concurrent and predictive), and face validity. It also discusses reliability in terms of equivalency, stability, internal consistency, and interrater reliability. Validity and reliability are closely related but a test can be reliable without being valid. The document also notes sources of error in measurements and the backwash effect of test design on teaching.
Measures of central tendency and dispersionRajaKrishnan M
This document provides an overview of measures of central tendency and dispersion that are commonly used in descriptive statistics. It defines and provides examples of the mean, median, mode, range, and standard deviation. It also discusses frequency tables, histograms, and the shapes that distributions can take. The goal of descriptive statistics is to summarize and describe data through techniques like these in order to understand the characteristics of variables.
This document discusses the concept of validity in psychological testing. It defines validity as the degree to which a test measures what it claims to measure. There are three main types of validity: content validity, which concerns how well a test represents the content area it aims to measure; criterion-related validity, which compares test scores to external criteria; and construct validity, which evaluates how well a test measures hypothetical constructs. Validity is influenced by factors like test length and the range of abilities in the sample population. A test must demonstrate validity to ensure the inferences made from its results are appropriate and meaningful.
Construct validity assesses how accurately theories and ideas have been translated into procedures and measures. It is established by defining terms, proving control over variables, and supporting theory with empirical evidence. Content validity ensures test items adequately represent the domain being measured. It is achieved through thorough literature review, constructing procedures accordingly, and expert evaluation. Both validity types are important for drawing conclusive results from studies, though establishing validity can be challenging for complex studies with many interacting variables.
This presentation is about the objectivity of tests, It presents the definition of objective tests, and its meaning.
It reflects upon the objectivity of scoring, types of objective tests, merits and demerits about the same.
Reliability refers to the consistency of test scores across different administrations of the test. There are two aspects of reliability - reliability of scores over time and reliability of scoring. Reliability of scoring is easier to achieve for objectively scored tests but is important for subjectively scored tests like essays. There are two types of scorer reliability: intra-rater reliability which measures consistency of a single rater's scores and inter-rater reliability which measures consistency between multiple raters' scores. Reliability can be quantified using reliability coefficients which measure the correlation between scores from different test administrations or halves of a test. A test cannot be valid unless it is also reliable, but a reliable test may not be valid as there
Correlation measures the strength and direction of association between two variables. It ranges from -1 to 1, where 0 indicates no association and values closer to 1 or -1 indicate stronger positive or negative associations, respectively. The document provides examples of positive correlation between variables such as height and weight that increase together, and negative correlation where variables such as TV time and grades move in opposite directions. Formulas and different types of correlation including partial and multiple correlation are also defined in the document.
The document discusses four levels of measurement (nominal, ordinal, interval, and ratio) and how they relate to different types of data. It then covers topics in descriptive statistics including frequency distributions, measures of central tendency (mode, median, mean), and measures of dispersion (variance and standard deviation). Key points are that nominal variables use the mode, ordinal variables use the median, and interval/ratio variables use the mean. Variance and standard deviation quantify how spread out values are from the mean.
ch-4-measures-of-variability-11 2.ppt for nursingwindri3
This document discusses measures of variability used in statistics. It defines variability as the spread or dispersion of scores. The key measures of variability discussed are the range, variance, and standard deviation. The range is the difference between the highest and lowest scores. The variance is the average of the squared deviations from the mean and represents how far the scores deviate from the mean. The standard deviation is the square root of the variance and represents how much scores typically deviate from the mean. Larger standard deviations indicate greater variability in the scores.
This document discusses measures of variability used in statistics. It defines variability as the spread or dispersion of scores. The key measures of variability discussed are the range, variance, and standard deviation. The range is the difference between the highest and lowest scores. The variance is the average of the squared deviations from the mean and represents how far the scores deviate from the mean. The standard deviation is the square root of the variance and represents how much scores typically deviate from the mean. Larger standard deviations indicate greater variability in the scores.
Lecture. Introduction to Statistics (Measures of Dispersion).pptxNabeelAli89
1) The document discusses various measures of dispersion used to quantify how spread out or varied a set of data values are from the average.
2) There are two types of dispersion - absolute dispersion measures how varied data values are in the original units, while relative dispersion compares variability between datasets with different units.
3) Common measures of absolute dispersion include range, variance, and standard deviation. Range is the difference between highest and lowest values, while variance and standard deviation take into account how far all values are from the mean.
The document discusses variability and measures of variability. It defines variability as a quantitative measure of how spread out or clustered scores are in a distribution. The standard deviation is introduced as the most commonly used measure of variability, as it takes into account all scores in the distribution and provides the average distance of scores from the mean. Properties of the standard deviation are examined, such as how it does not change when a constant is added to all scores but does change when all scores are multiplied by a constant.
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Chapter 6: Normal Probability Distribution
6.3: Sampling Distributions and Estimators
1. The document discusses various measures of dispersion used to quantify how spread out or variable a data set is. It describes measures such as range, mean deviation, variance, and standard deviation.
2. It also discusses relative measures of dispersion like the coefficient of variation, which allows comparison of variability between data sets with different units or averages. The coefficient of variation expresses variability as a percentage of the mean.
3. Additional concepts covered include skewness, which refers to the asymmetry of a distribution, and kurtosis, which measures the peakedness of a distribution compared to a normal distribution. Positive or negative skewness and leptokurtic, mesokurtic, or platykurtic k
The document discusses measures of variability in data, including variance and standard deviation. It provides examples calculating variance and standard deviation from a sample of fish lengths. Variance is the estimated average of the squared deviations from the mean. Standard deviation is the square root of the variance, representing the average distance from the mean. For a sample, the sample variance uses n-1 in the denominator rather than n. Skewness is also discussed as a measure of the symmetry of a distribution, with negative skewness indicating left-skewed data and positive skewness indicating right-skewed data.
The document discusses descriptive statistics such as the mean, median, variance and standard deviation. It uses examples of birth weight data from 5 newborn babies to illustrate how to calculate these statistics. The mean birth weight was 3.68 kg. The standard deviation describes how much the weights vary around the mean. But the sample mean itself has some error since it is based on a subset of data. This error is quantified by the standard error of the mean, which depends on the sample size and standard deviation.
Ee184405 statistika dan stokastik statistik deskriptif 2 numerikyusufbf
Statistika adalah suatu bidang ilmu yang mempelajari cara-cara mengumpulkan data untuk selanjutnya dapat dideskripsikan dan diolah, kemudian melakukan induksi/inferensi dalam rangka membuat kesimpulan, agar dapat ditentukan keputusan yang akan diambil berdasarkan data yang dimiliki.
DATA =============> PROSES STATISTIK ===========> INFORMASI
Statistik Deskriptif adalah suatu cara menggambarkan persoalan yang berdasarkan data yang dimiliki yakni dengan cara menata data tersebut sedemikian rupa agar karakteristik data dapat dipahami dengan mudah sehingga berguna untuk keperluan selanjutnya.
This document discusses key concepts in one-dimensional motion physics including displacement, distance, velocity, speed, and average velocity. It provides examples and problems to illustrate the differences between scalar and vector quantities as well as distance and displacement. Graphs are used to represent motion data and calculate instantaneous and average velocities from slopes of the position-time graphs at different time intervals. Students are prompted to practice examples, self-assess their understanding, and complete a lab assignment.
1) Measures of dispersion provide additional information about a frequency distribution beyond measures of central tendency alone by quantifying how scattered or spread out the data values are.
2) Common measures of dispersion include the range (difference between highest and lowest values), variance (average of squared distances from the mean), and standard deviation (square root of the variance).
3) The variance and standard deviation can be calculated for both population and sample data, with sample calculations using deviations from the sample mean, while population calculations use the population mean.
This document provides information on measures of central tendency and dispersion. It discusses the mean, median, and mode as the three main measures of central tendency. It provides formulas and examples for calculating the mean, median, and mode for both ungrouped and grouped data. The document also covers measures of dispersion including range, semi-interquartile range, variance, standard deviation, and coefficient of variation. It provides formulas and examples for calculating each of these measures. Finally, the document briefly discusses chi-square tests, Pearson's correlation, and using scatterplots to examine relationships between variables.
The document presents information on statistical methods and quality budgeting procedures. It discusses the five steps of quality - say what you do, do what you say, record what you do, review what you do, and restart the process. The budget is divided according to these steps, first describing measures of central tendency like mean, median and mode. It then covers measuring dispersion through tools like range, variance and standard deviation. The document reviews the processes and asks if quality is achieved or not.
This document discusses moments, skewness, kurtosis, and several statistical distributions including binomial, Poisson, hypergeometric, and chi-square distributions. It defines key terms such as moment ratios, central moments, theorems, skewness, kurtosis, and correlation. Properties and applications of the binomial, Poisson, and hypergeometric distributions are provided. Finally, the document discusses the chi-square test for goodness of fit and independence.
This document discusses measures of central tendency (mean, median, mode) and measures of spread (range, variance, standard deviation). It provides formulas and examples to calculate each measure. It also presents two problems, asking to calculate and compare various descriptive statistics for different data sets, such as milk yields from two cow herds and weaning weights of lambs from two breeds. A third problem asks to analyze and compare price data for rice from two markets.
This document discusses non-parametric tests and how to use them to compare groups when assumptions of parametric tests are violated. It explains that non-parametric tests like the Wilcoxon and Kruskal-Wallis tests can be used when samples are small or data is not normally distributed. The Kruskal-Wallis test allows comparison of more than two groups by ranking all data and comparing mean ranks between groups. An example compares student grades under different teaching methods using both Kruskal-Wallis and ANOVA tests.
The document discusses the sampling distribution of means. It states that as sample size increases, the distribution of sample means approaches a normal distribution according to the Central Limit Theorem. The mean of the sampling distribution equals the population mean, and the standard deviation of the sampling distribution is the population standard deviation divided by the square root of the sample size. An example is provided to demonstrate calculating the mean and variance of a sampling distribution using a hypothetical population and different sample sizes.
The document discusses linear inequalities in one variable. It defines a linear inequality in one variable as an inequality that can be written in the form ax + b > c, where a, b, and c are real numbers. It notes that the > symbol can be replaced by ≥, <, or ≤. The document provides examples and steps for transforming linear inequalities into standard form where the leading coefficient a is positive and the inequality is written as ax + b > 0. It emphasizes using properties of inequalities and multiplying by -1 when a is negative.
The document discusses t-tests, which are used to compare means between groups. It describes the assumptions of t-tests, the different types of t-tests including independent samples t-tests and dependent samples t-tests, and the steps to conduct t-tests by hand and using SPSS. It provides examples of conducting one-sample t-tests, independent samples t-tests, and dependent samples t-tests, including interpreting the results. It also discusses how to increase statistical power by increasing the difference between means, decreasing variance, increasing sample size, and increasing the alpha level.
This short story is about a king who complained about the rough roads hurting his feet after a long trip. He ordered his people to cover all the roads in the country with leather to fix the problem. However, one wise servant suggested simply cutting a small piece of leather to cover the king's feet instead of spending huge resources to cover all the roads. The king realized this was a better solution. The moral is that it is better to change yourself rather than trying to change the entire world.
This document discusses routing in IP networks. It begins by introducing routing and routing protocols. Routers use routing protocols to decide the best path between networks based on metrics like link costs and current congestion. It then provides an example of router and network configurations with link costs. The document discusses routing tables, which contain the next hop for each destination network. It also covers different types of routing like fixed, adaptive, flooding and random routing. Adaptive routing aims to dynamically change paths in response to failures or congestion but faces challenges. The document classifies adaptive routing strategies and algorithms like distance-vector, link-state, and path-vector routing. It concludes by explaining the Dijkstra's and Bellman-Ford least cost
The document provides information about the Myers-Briggs Type Indicator personality assessment. It describes the main dimensions of extraversion/introversion, sensing/intuition, thinking/feeling, and judging/perceiving. It also outlines the 16 personality types that result from combinations of those dimensions, including common work environments and occupational trends for each type. Key differences in how types approach relationships, work, and organizational values are summarized.
This document discusses various approaches to leadership and team development. It covers the styles approach including authoritarian, democratic, and laissez-faire styles. It also discusses trait approach, situational approach, functional approach, and transformational approach. For the situational approach, it explains the three key factors of relationship behavior, task behavior, and maturity that determine the most effective leadership style.
This document discusses strategic management and the need for strategic thinking in the 21st century. It notes that companies must operate with strategic competitiveness and sustained competitive advantages in order to survive amid discontinuing change, shrinking business and technology lifecycles, and shifting consumer preferences and demographics. It also discusses analyzing the external environment, including industries and competition, as well as developing strategic plans through vision, mission, objectives and crafting strategies. The strategic management process is characterized as ongoing, iterative and cross-functional.
This document discusses the importance of reliability and validity in psychological measurement. Reliability refers to the consistency and repeatability of measurements. It is influenced by measurement error from factors like a participant's mood or fatigue. Validity indicates how well a measure assesses the intended construct. There are several types of validity including face validity, construct validity, convergent validity, discriminant validity, and criterion-related validity. Reliability is necessary for validity and can be estimated using methods like test-retest reliability, internal consistency reliability, and inter-rater reliability. Validity compares a measure to other related and unrelated constructs to determine if it is measuring what it intends to measure.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
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2. Can Psychological Properties Be
Measured?
A common complaint: Psychological
variables can’t be measured.
We regularly make judgments about
who is shy and who isn’t; who is
attractive and who isn’t; who is smart
and who is not.
3. Quantitative
Implicit in these statements is the notion
that some people are more shy, for
example, than others
This kind of statement is inherently
quantitative.
Quantitative: It is subject to numerical
qualification.
If it can be numerically qualified, it can
be measured.
4. Measurement
• The process of assigning numbers to objects in such a
way that specific properties of the objects are faithfully
represented by specific properties of the numbers.
• Psychological tests do not attempt to measure the total
person, but only a specific set of attributes.
5. Measurement (cont.)
•Measurement is used to capture some “construct”
- For example, if research is needed on the construct of
“depression”, it is likely that some systematic
measurement tool will be needed to assess depression.
6. Individual Differences
• The cornerstone of psychological measurement - that
there are real, relatively stable differences between
people.
• This means that people differ in measurable ways in
their behavior and that the differences persist over a
sufficiently long time.
•Researchers are interested in assigning individuals
numbers that will reflect their differences.
• Psychological tests are designed to measure specific
attributes, not the whole person.
•These differences may be large or small.
8. Types of Measurement Scales
Nominal Scales - there must be distinct classes but these classes
have no quantitative properties. Therefore, no comparison can be made
in terms of one being category being higher than the other
For example - there are two classes for the variable gender -- males and
females. There are no quantitative properties for this variable or these
classes and, therefore, gender is a nominal variable.
Other Examples:
country of origin
biological sex (male or female)
animal or non-animal
married vs. single
9. Nominal Scale
Sometimes numbers are used to designate
category membership
Example:
Country of Origin
1 = United States 3 = Canada
2 = Mexico 4 = Other
However, in this case, it is important to keep in
mind that the numbers do not have intrinsic
10. Types of Measurement Scales
Ordinal Scales - there are distinct classes but these
classes have a natural ordering or ranking. The
differences can be ordered on the basis of magnitude.
For example - final position of horses in a
thoroughbred race is an ordinal variable. The horses
finish first, second, third, fourth, and so on. The
difference between first and second is not necessarily
equivalent to the difference between second and third,
or between third and fourth.
11. Ordinal Scales
Does not assume that the intervals between numbers are equal
Example:
finishing place in a race (first place, second place)
1st place 2nd place 3rd place 4th place
1 hour 2 hours 3 hours 4 hours 5 hours 6 hours 7 hours 8 hours
12. Types of Measurement Scales (cont.)
Interval Scales - it is possible to compare differences in magnitude,
but importantly the zero point does not have a natural meaning. It
captures the properties of nominal and ordinal scales -- used by most
psychological tests.
Designates an equal-interval ordering - The distance between, for
example, a 1 and a 2 is the same as the distance between a 4 and a 5
Example - celsius temperature is an interval variable. It is meaningful to
say that 25 degrees celsius is 3 degrees hotter than 22 degrees celsius,
and that 17 degrees celsius is the same amount hotter (3 degrees) than 14
degrees celsius. Notice, however, that 0 degrees celsius does not have a
natural meaning. That is, 0 degrees celsius does not mean the absence of
heat!
13. Types of Measurement Scales (cont.)
Ratio Scales - captures the properties of the other types of
scales, but also contains a true zero, which represents the
absence of the quality being measured..
For example - heart beats per minute has a very natural zero
point. Zero means no heart beats. Weight (in grams) is also a
ratio variable. Again, the zero value is meaningful, zero
grams means the absence of weight.
Example:
the number of intimate relationships a person has had
0 quite literally means none
a person who has had 4 relationships has had twice as
many as someone who has had 2
14. Types of Measurement Scales (cont.)
• Each of these scales have different properties (i.e.,
difference, magnitude, equal intervals, or a true zero point)
and allows for different interpretations
• The scales are listed in hierarchical order. Nominal scales
have the fewest measurement properties and ratio having the
most properties including the properties of all the scales
beneath it on the hierarchy.
• The goal is to be able to identify the type of measurement
scale, and to understand proper use and interpretation of the
scale.
15. Evaluating Psychological Tests
The evaluation of psychological tests centers on the test’s:
Reliability - has to do with the consistency of the instrument.
A reliable test is one that yields consistent scores when a
person takes the test two alternate forms of the test or when an
individual takes the same test on two or more different
occasions.
Validity - has to do with the ability to measure what it is
supposed to measure and the extent to which it predicts
outcomes.
16. Why Statistics?
Statistics are important because they give us a method for
answering questions about meaning of those numbers.
Three statistical concepts are central to psychological
measurement:
Variability - measure of the extent to which test scores differ.
Correlation - relationship between scores
Prediction - forecast relationships
17. Variability
• Variability is the foundation of psychological testing
• Variability refers to the spread of the scores within a
distribution.
•Tests depends on variability across individuals --- if there
was no variability then we could not make decisions about
people.
• The greater the amount of variability there is among
individuals, the more accurately we can make the
distinctions among them.
18. Variability
There are four major measures of variability:
1. Range - difference between the highest and lowest scores
For Example: If the highest score was 60 & lowest was 40 = range of 20
2. Interquartile Range - difference between the 75th and 25th
percentile.
3. Variance - the degree of spread within the distribution (the
larger the spread, the larger the variance). It is the sum of the
squared differences from the mean of each score, divided by
the number of scores
4. Standard Deviation - a measure of how the average score
deviates or spreads away from the mean.
19. Standard Deviation
Standard deviation is
a measure of spread
affected by the size of each data value
a commonly calculated and used statistic
equal to
var iance
typically about 2/3 of data values lie within one
standard deviation of the mean.
20. Example – using individual data values
Question: Six masses were weighed as 4, 6, 6, 7, 9 and 10 kg
Find the mean, variance and standard deviation of these weights.
Answer: mean x=
∑x =
4 + 6 + 6 + 7 + 9 + 10
=
42
= 7 kg
n 6 6
Variance is the
average square
distance from
the mean
1 2 3 4 5 6 7 8 9 10 weight kg
x
21. Example – using individual data values
Question: Six masses were weighed as 4, 6, 6, 7, 9 and 10 kg
Find the mean, variance and standard deviation of these weights.
Answer: mean x=
∑x =
4 + 6 + 6 + 7 + 9 + 10
=
42
= 7 kg
n 6 6
Method 1 Variance σ 2 = ∑ ( x−µ )2
n
2 ( 4 − 7 ) 2 + ( 6 − 7 ) 2 + ( 6 − 7 ) 2 + ( 7 − 7 ) 2 + ( 9 − 7 ) 2 + (10 − 7 ) 2
Variance is the σ =
6
average square
distance from
the mean
1 2 3 4 5 6 7 8 9 10 weight kg
x
22. Question: Six masses were weighed as 4, 6, 6, 7, 9 and 10 kg
Find the mean, variance and standard deviation of these weights.
Answer: mean x=
∑x =
4 + 6 + 6 + 7 + 9 + 10
=
42
= 7 kg
n 6 6
Method 1 Variance σ 2 = ∑ ( x−µ )2
n
2 ( 4 − 7 ) 2 + ( 6 − 7 ) 2 + ( 6 − 7 ) 2 + ( 7 − 7 ) 2 + ( 9 − 7 ) 2 + (10 − 7 ) 2
Variance is the σ =
6
average square
distance from 2 ( −3) 2 + ( −1) 2 + ( −1) 2 + ( 0 ) 2 + ( 2 ) 2 + ( 3) 2 24
σ = = = 4 kg2
the mean 6 6
1 2 3 4 5 6 7 8 9 10 weight kg
x
23. Question: Six masses were weighed as 4, 6, 6, 7, 9 and 10 kg
Find the mean, variance and standard deviation of these weights.
Answer: mean x=
∑x =
4 + 6 + 6 + 7 + 9 + 10
=
42
= 7 kg
n 6 6
Method 1 Variance σ 2 = ∑ ( x−µ )2
n
2 ( 4 − 7 ) 2 + ( 6 − 7 ) 2 + ( 6 − 7 ) 2 + ( 7 − 7 ) 2 + ( 9 − 7 ) 2 + (10 − 7 ) 2
σ =
6
2 ( −3) 2 + ( −1) 2 + ( −1) 2 + ( 0 ) 2 + ( 2 ) 2 + ( 3) 2 24
σ = = = 4 kg2
6 6
standard deviation σ= var iance = 4 = 2 kg
1 2 3 4 5 6 7 8 9 10 weight kg
x
24. Normal Distribution Curve
• Many human variables fall on a normal or close to normal curve
including IQ, height, weight, lifespan, and shoe size.
• Theoretically, the normal curve is bell shaped with the highest
point at its center. The curve is perfectly symmetrical, with no
skewness (i.e., where symmetry is absent). If you fold it in half at the
mean, both sides are exactly the same.
•From the center, the curve tapers on both sides approaching the X
axis. However, it never touches the X axis. In theory, the
distribution of the normal curve ranges from negative infinity to
positive infinity.
•Because of this, we can estimate how many people will compare on
specific variables. This is done by knowing the mean and standard
deviation.
25. Scatter Plots
• An easy way to examine the data given is by scatter plot. When we plot the
points from the given set of data onto a rectangular coordinate system, we have a
scatter plot.
• Is often employed to identify potential associations between two variables, where
one may be considered to be an explanatory variable (such as years of education)
and another may be considered a response variable
26. Relational/Correlational Research
Relational Research …
• Attempts to determine how two or more variables are related to
each other.
•Is used in situations where a researcher is interested in
determining whether the values of one variable increase (or
decrease) as values of another variable increase. Correlation does
NOT imply causation!
•For example, a researcher might be wondering whether there is a
relationship between number of hours studied and exam grades.
The interest is in whether exam grades increase as number of study
hours increase.
27. Use and Meaning of Correlation Coefficients
• Value can range from -1.00 to +1.00
• An r = 0.00 indicates the absence of a linear relationship.
• An r = +1.00 or an r = - 1.00 indicates a “perfect” relationship between the
variables.
•A positive correlation means that high scores on one variable tend to go with
high scores on the other variable, and that low scores on one variable tend to go
with low scores on the other variable.
•A negative correlation means that high scores on one variable tend to go with
low scores on the other variable.
•The further the value of r is away from 0 and the closer to +1 or -1, the
stronger the relationship between the variables.
28. Coefficients of Determination
•By squaring the correlation coefficient, you get the amount of variance
accounted for between the two data sets. This is called the coefficient of
determination.
• A correlation of .90 would represent 81% of the variance between the two sets
of data (.90 X .90 = .81)
• A perfect correlation of 1.00 represents 100% of the variance. If you know
one variable, you can predict the other variable 100% of the time
(1.00 X 1.00 = 1.00)
•A correlation of .30 represents only 9% of the variance, strongly suggesting
that other factors are involved (.30 X .30 = .09)
29. Factor Analysis
Is a statistical technique used to analyze patterns of
correlations among different measures.
The principal goal of factor analysis is to reduce the
numbers of dimensions needed to describe data derived
from a large number of data.
It is accomplished by a series of mathematical calculations,
designed to extract patterns of intercorrelations among a
set of variables.
30. Prediction/Linear Regression
• Linear regression attempts to model the relationship between
two variables by fitting a linear equation to observed data. One
variable is considered to be an explanatory variable, and the
other is considered to be a dependent variable.
Formula : Y = a + bX ---------- Where X is the independent
variable, Y is the dependent variable, a is the intercept and b is
the slope of the line.
• Before attempting to fit a linear model to observed data, a
modeler should first determine whether or not there is a
relationship between the variables of interest
31. Prediction/Linear Regression
• The method was first used to examine the relationship
between the heights of fathers and sons. The two were related,
of course, but the slope is less than 1.0. A tall father tended to
have sons shorter than himself; a short father tended to have
sons taller than himself. The height of sons regressed to the
mean. The term "regression" is now used for many sorts of
curve fitting.
• Linear regression analyzes the relationship between two
variables, X and Y. For each subject (or experimental unit),
you know both X and Y and you want to find the best straight
line through the data.
32. Steps in Test Construction
1. Concept Development
2. Domain Identification
3. Item Construction
4. Item Analysis: Item Difficulty & Item
Discrimination
5. Reliability: Test-Retest, Parallel Form, Slit-half,
Rational Equivalence (Internal-consistency)
6. Validity: Content, Construct, Criterion
7. Norms