The document provides guidance on conducting factor analysis and principal components analysis. It discusses sample size requirements, tests to check the suitability of the data for factor analysis, determining the number of factors to retain, interpreting the factor solution, computing factor scores, and assessing the reliability of factors using Cronbach's alpha.
This document provides information about statistical tests and data analysis presented by Dr. Muhammedirfan H. Momin. It discusses the different types of statistical data, such as qualitative vs quantitative and continuous vs discrete data. It also covers topics like sample data sets, frequency distributions, risk factors for diseases, hypothesis testing, and tests for comparing proportions and means. Specific statistical tests discussed include the z-test and how to calculate test statistics and compare them to critical values to determine statistical significance. Examples are provided to illustrate how to perform these tests to analyze differences between data sets.
This document summarizes an SPSS workshop held on September 6-7, 2014 at the Faculty of Science, UM. It discusses various SPSS procedures like entering and cleaning data, checking for missing values, frequencies, descriptive statistics, reliability analysis, factor analysis, t-tests, ANOVA, and linear regression. Frequency tables are presented to analyze gender distribution and responses to motivation questions. Reliability analysis and factor analysis are conducted to assess scales. T-tests are used to compare depression, satisfaction, productivity, supervisor support, and coworker support between groups. ANOVA tests for differences in these variables between multiple ethnic groups.
The document discusses a one-sample t-test used to compare sample data to a standard value. It provides an example comparing intelligence scores of university students to the average score of 100. The sample of 6 students had a mean of 120. Running a one-tailed t-test in SPSS, the results showed the mean score was significantly higher than 100 with t(5)=3.15, p=.02. This allows the inference that the population mean intelligence at the university is greater than the standard score of 100.
This document provides an introduction to medical statistics. It discusses why statistics are used in medical research, which is to collect, summarize, analyze and draw conclusions from sample data. It then describes different types of data (categorical, numerical) and statistical methods used to describe data, including percentages, mean, median, mode, standard deviation. Methods to test confidence such as confidence intervals and p-values are also introduced. Key concepts like null hypotheses and what confidence intervals say about significance are explained through examples.
This chapter discusses the importance of psychological science and the scientific method. It explains key concepts like the need to think critically and avoid biases. Researchers use various methods like case studies, surveys, experiments and statistical analysis to study behavior and mental processes in a rigorous, objective manner. The scientific approach allows psychologists to develop and test theories to better understand human thought and action.
The document discusses two-sample hypothesis tests, including tests for differences between two population means and two population proportions. It provides examples of hypothesis tests comparing means and proportions from two independent samples, including the steps to set up null and alternative hypotheses, determine the appropriate test statistic, identify the rejection region, and make a conclusion. It also discusses tests for paired or dependent samples.
The document describes a study that used a paired t-test to compare various parameters of food quality and customer experience between Dunkin Donuts and McDonald's in Cyber City, Gurgaon. A survey was administered to employees who frequented both locations, rating 10 parameters on a scale of 1 to 5. SPSS analysis found no statistically significant differences between the restaurants on any of the 10 parameters, including taste, menu variety, cost, quality, hygiene, service, ambience, nutrition, operating hours, and time to receive food. The null hypothesis of no difference between restaurants was accepted for all comparisons at the 5% significance level.
A one-sample z-test is used to compare a sample proportion to a population proportion. The document provides an example where a survey claims 90% of doctors recommend aspirin, and a sample of 100 doctors found 82% recommend aspirin. The z-test is calculated to determine if this difference is statistically significant. The null hypothesis is the sample and population proportions are the same. If the calculated z-statistic falls outside the critical values of -1.96 and 1.96, the null will be rejected, meaning the proportions are significantly different.
This document provides information about statistical tests and data analysis presented by Dr. Muhammedirfan H. Momin. It discusses the different types of statistical data, such as qualitative vs quantitative and continuous vs discrete data. It also covers topics like sample data sets, frequency distributions, risk factors for diseases, hypothesis testing, and tests for comparing proportions and means. Specific statistical tests discussed include the z-test and how to calculate test statistics and compare them to critical values to determine statistical significance. Examples are provided to illustrate how to perform these tests to analyze differences between data sets.
This document summarizes an SPSS workshop held on September 6-7, 2014 at the Faculty of Science, UM. It discusses various SPSS procedures like entering and cleaning data, checking for missing values, frequencies, descriptive statistics, reliability analysis, factor analysis, t-tests, ANOVA, and linear regression. Frequency tables are presented to analyze gender distribution and responses to motivation questions. Reliability analysis and factor analysis are conducted to assess scales. T-tests are used to compare depression, satisfaction, productivity, supervisor support, and coworker support between groups. ANOVA tests for differences in these variables between multiple ethnic groups.
The document discusses a one-sample t-test used to compare sample data to a standard value. It provides an example comparing intelligence scores of university students to the average score of 100. The sample of 6 students had a mean of 120. Running a one-tailed t-test in SPSS, the results showed the mean score was significantly higher than 100 with t(5)=3.15, p=.02. This allows the inference that the population mean intelligence at the university is greater than the standard score of 100.
This document provides an introduction to medical statistics. It discusses why statistics are used in medical research, which is to collect, summarize, analyze and draw conclusions from sample data. It then describes different types of data (categorical, numerical) and statistical methods used to describe data, including percentages, mean, median, mode, standard deviation. Methods to test confidence such as confidence intervals and p-values are also introduced. Key concepts like null hypotheses and what confidence intervals say about significance are explained through examples.
This chapter discusses the importance of psychological science and the scientific method. It explains key concepts like the need to think critically and avoid biases. Researchers use various methods like case studies, surveys, experiments and statistical analysis to study behavior and mental processes in a rigorous, objective manner. The scientific approach allows psychologists to develop and test theories to better understand human thought and action.
The document discusses two-sample hypothesis tests, including tests for differences between two population means and two population proportions. It provides examples of hypothesis tests comparing means and proportions from two independent samples, including the steps to set up null and alternative hypotheses, determine the appropriate test statistic, identify the rejection region, and make a conclusion. It also discusses tests for paired or dependent samples.
The document describes a study that used a paired t-test to compare various parameters of food quality and customer experience between Dunkin Donuts and McDonald's in Cyber City, Gurgaon. A survey was administered to employees who frequented both locations, rating 10 parameters on a scale of 1 to 5. SPSS analysis found no statistically significant differences between the restaurants on any of the 10 parameters, including taste, menu variety, cost, quality, hygiene, service, ambience, nutrition, operating hours, and time to receive food. The null hypothesis of no difference between restaurants was accepted for all comparisons at the 5% significance level.
A one-sample z-test is used to compare a sample proportion to a population proportion. The document provides an example where a survey claims 90% of doctors recommend aspirin, and a sample of 100 doctors found 82% recommend aspirin. The z-test is calculated to determine if this difference is statistically significant. The null hypothesis is the sample and population proportions are the same. If the calculated z-statistic falls outside the critical values of -1.96 and 1.96, the null will be rejected, meaning the proportions are significantly different.
This document describes how to perform a one-sample z-test to compare a sample proportion to a population proportion. It provides an example where a survey claims 90% of doctors recommend aspirin, and a sample of 100 doctors found 82% recommend aspirin. It outlines calculating the z-statistic to determine if this difference is statistically significant using a 95% confidence level. The z-statistic is calculated to be -1.08, which falls within the acceptable range so the null hypothesis that the population and sample proportions are the same is retained.
The document discusses small sample tests of hypotheses. It explains that for small sample sizes (n<30), a t-distribution is used instead of the normal distribution to account for the small sample size. There are three cases discussed for small sample tests: testing a population mean, comparing the means of two independent samples, and comparing the means of two paired samples. For each case, the assumptions, test statistic (involving a t-distribution), and an example are provided.
Calculating a two sample z test by handKen Plummer
The document describes how to calculate a two-sample z-test by hand to determine if there is a statistically significant difference between the reported anxiety symptoms of patients taking a new anti-anxiety medication versus a placebo. It provides the formula for the z-statistic and walks through calculating it step-by-step for a sample problem where 64 out of 200 patients taking the medication reported anxiety symptoms compared to 92 out of 200 patients taking the placebo. The calculated z-statistic is then compared to critical values to determine whether to reject or fail to reject the null hypothesis that there is no difference between the groups.
Calculating a single sample z test by handKen Plummer
The document explains how to calculate a single-sample z-test. It provides an example of testing a claim that 9 out of 10 doctors recommend aspirin by taking a random sample of 100 doctors, of which 82 recommend aspirin. It defines the null and alternative hypotheses, identifies the critical z-value of -1.96 and +1.96, and shows the step-by-step calculations to find the z-statistic of -2.67, which falls outside the critical values. This indicates the sample result is statistically significant and differs from the claimed population value.
The document discusses standard deviation and the normal distribution. Some key points:
- Standard deviation is a measure of how spread out values are from the mean. It is the square root of the variance.
- For a normal distribution, approximately 68% of values fall within 1 standard deviation of the mean, 95% within 2 standard deviations, and 99% within 3 standard deviations.
- The normal distribution is the most common continuous probability distribution and is important because many variables tend toward a normal distribution as the number of trials increases. It is used in statistical quality control.
Researchers tested a new anti-anxiety medication on 200 people and a placebo on another 200 people. 64 of those on the medication and 92 of those on the placebo reported anxiety symptoms. The researchers want to determine if there is a statistically significant difference in reported anxiety between the two groups using a two-sample z-test with an alpha of 0.05. A two-sample z-test is used to compare differences between two sample proportions and determines if any observed difference is likely due to chance or not.
This document provides information about the t-test and chi-square test. It defines the t-test as a test used to compare the means of two samples when the population standard deviation is unknown. It lists the assumptions of the t-test and provides the formula. An example t-test problem and solution is given. Chi-square is introduced as a test used with categorical and numerical data to test for independence and goodness of fit. The chi-square test statistic, degrees of freedom, and hypothesis testing process are outlined. An example chi-square goodness of fit problem and solution is also provided.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.3: Testing a Claim About a Mean
A study of psychiatric patients hospitalized for depression found a moderate positive correlation between black and white thinking ("simplicity") and depression. Statistical analysis showed this relationship was significant. While the study shows a relationship, causation cannot be determined. Further research is needed to understand the connection between these variables in clinical and general populations.
The document explains the hypothesis testing process used to determine if a weight loss program's claim can be supported. It involves:
1) Defining the null and alternative hypotheses based on the claim and sample data, with the null being that average weight loss is not less than 10 pounds.
2) Calculating a z-score for the sample mean and finding the critical value from the z-table based on a 95% confidence level.
3) Comparing the z-score to the critical value and rejecting the null hypothesis if the z-score is lower, meaning the sample provides evidence against the claim.
hypothesis testing-tests of proportions and variances in six sigmavdheerajk
The document provides information about various statistical hypothesis tests that can be used to analyze data and test if process improvements have resulted in significant changes. It discusses one proportion tests, two proportions tests, one-variance tests, two-variances tests, and how to determine which test to use based on the type of data and questions being asked. Examples are also provided of applying these tests using Minitab software to analyze sample data and test hypotheses about changes between before and after process improvement situations. The document aims to help determine the appropriate statistical tests for validating improvements in processes.
This document discusses significance tests for population means and proportions using Student's t-distribution and the normal distribution. It provides examples of hypothesis testing for a population mean using a paired t-test and for a population proportion using a single-sample z-test. It also discusses the assumptions, test statistics, and interpretations for these tests. Confidence intervals are presented as complementary to significance tests for estimating population parameters.
This document provides examples and explanations of common hypothesis testing techniques including:
- Z tests for large samples with known population variance to test claims about population means
- T tests for small samples with unknown population variance
- Tests comparing two population means using Z tests for large samples and T tests for small samples
- One-tailed and two-tailed tests at various significance levels (e.g. 5%, 10%)
Step-by-step solutions and calculations are shown for multiple examples testing claims about means, differences in means, and whether sample data is consistent with hypothesized population parameters.
This document discusses inferential statistics and epidemiological research. It introduces concepts like the central limit theorem, standard error, confidence intervals, hypothesis testing, and different statistical tests. Specifically, it covers:
- The central limit theorem states that sample means will follow a normal distribution, even if the population is not normally distributed.
- Standard error is used to measure sampling variation and determine confidence intervals around sample statistics to estimate population parameters.
- Hypothesis testing involves a null hypothesis of no difference and an alternative hypothesis of a significant difference.
- Common tests discussed include chi-square tests to compare proportions between groups and determine if differences are significant.
A researcher wants to conduct a survey to determine the prevalence of abusive behavior in children aged 6-12 years old in Manila. Using a sample size formula for one group proportions, the required sample size is calculated as 139 children based on: a past reported prevalence of 10%, a desired confidence level of 95%, and a tolerable error of 5%. Adding a 10% expected non-response rate, the total required sample size is rounded up to 153 children.
A second study wants to compare exam pass rates between two statistics class sections. Using the sample size formula for two groups proportions, the required sample size per group is calculated as 44 students based on: detecting a 15% pass rate difference, a confidence level of 95%,
Introduction to hypothesis testing ppt @ bec domsBabasab Patil
This document introduces hypothesis testing, including:
- Formulating null and alternative hypotheses for tests involving population means and proportions
- Using test statistics, critical values, and p-values to test hypotheses
- Defining Type I and Type II errors and their probabilities
- Examples of hypothesis tests for means (using z-tests and t-tests) and proportions (using z-tests) are provided to illustrate the concepts.
The document describes how to calculate a single-sample z-test. It explains that the z-critical value is determined by the significance level, often 0.05 corresponding to z-values of -1.96 and 1.96. An example calculates the z-statistic to test if sample data matches a population claim. It finds the z-statistic is -2.67, which is outside the critical values and considered rare, so the null hypothesis that the sample matches the claim is rejected.
This document discusses descriptive data mining techniques including hierarchical clustering, k-means clustering, and association rules. It provides examples of how to calculate distances between observations using Euclidean distance and standardized z-scores. It also discusses measures of similarity between observations for categorical variables, including matching coefficients and Jaccard's coefficient. Methods for calculating similarity between clusters in hierarchical clustering are presented, including single linkage, complete linkage, and average linkage.
This document provides an overview of different types of variables and methods for summarizing clinical data, including descriptive statistics. It discusses categorical variables like gender and ordinal variables like disease staging. For continuous variables it explains measures of central tendency like mean, median and mode, and measures of variation like range, standard deviation, and interquartile range. Graphs for summarizing univariate data are also covered, such as bar charts for categorical variables and histograms and box plots for continuous variables.
This document describes how to perform a one-sample z-test to compare a sample proportion to a population proportion. It provides an example where a survey claims 90% of doctors recommend aspirin, and a sample of 100 doctors found 82% recommend aspirin. It outlines calculating the z-statistic to determine if this difference is statistically significant using a 95% confidence level. The z-statistic is calculated to be -1.08, which falls within the acceptable range so the null hypothesis that the population and sample proportions are the same is retained.
The document discusses small sample tests of hypotheses. It explains that for small sample sizes (n<30), a t-distribution is used instead of the normal distribution to account for the small sample size. There are three cases discussed for small sample tests: testing a population mean, comparing the means of two independent samples, and comparing the means of two paired samples. For each case, the assumptions, test statistic (involving a t-distribution), and an example are provided.
Calculating a two sample z test by handKen Plummer
The document describes how to calculate a two-sample z-test by hand to determine if there is a statistically significant difference between the reported anxiety symptoms of patients taking a new anti-anxiety medication versus a placebo. It provides the formula for the z-statistic and walks through calculating it step-by-step for a sample problem where 64 out of 200 patients taking the medication reported anxiety symptoms compared to 92 out of 200 patients taking the placebo. The calculated z-statistic is then compared to critical values to determine whether to reject or fail to reject the null hypothesis that there is no difference between the groups.
Calculating a single sample z test by handKen Plummer
The document explains how to calculate a single-sample z-test. It provides an example of testing a claim that 9 out of 10 doctors recommend aspirin by taking a random sample of 100 doctors, of which 82 recommend aspirin. It defines the null and alternative hypotheses, identifies the critical z-value of -1.96 and +1.96, and shows the step-by-step calculations to find the z-statistic of -2.67, which falls outside the critical values. This indicates the sample result is statistically significant and differs from the claimed population value.
The document discusses standard deviation and the normal distribution. Some key points:
- Standard deviation is a measure of how spread out values are from the mean. It is the square root of the variance.
- For a normal distribution, approximately 68% of values fall within 1 standard deviation of the mean, 95% within 2 standard deviations, and 99% within 3 standard deviations.
- The normal distribution is the most common continuous probability distribution and is important because many variables tend toward a normal distribution as the number of trials increases. It is used in statistical quality control.
Researchers tested a new anti-anxiety medication on 200 people and a placebo on another 200 people. 64 of those on the medication and 92 of those on the placebo reported anxiety symptoms. The researchers want to determine if there is a statistically significant difference in reported anxiety between the two groups using a two-sample z-test with an alpha of 0.05. A two-sample z-test is used to compare differences between two sample proportions and determines if any observed difference is likely due to chance or not.
This document provides information about the t-test and chi-square test. It defines the t-test as a test used to compare the means of two samples when the population standard deviation is unknown. It lists the assumptions of the t-test and provides the formula. An example t-test problem and solution is given. Chi-square is introduced as a test used with categorical and numerical data to test for independence and goodness of fit. The chi-square test statistic, degrees of freedom, and hypothesis testing process are outlined. An example chi-square goodness of fit problem and solution is also provided.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.3: Testing a Claim About a Mean
A study of psychiatric patients hospitalized for depression found a moderate positive correlation between black and white thinking ("simplicity") and depression. Statistical analysis showed this relationship was significant. While the study shows a relationship, causation cannot be determined. Further research is needed to understand the connection between these variables in clinical and general populations.
The document explains the hypothesis testing process used to determine if a weight loss program's claim can be supported. It involves:
1) Defining the null and alternative hypotheses based on the claim and sample data, with the null being that average weight loss is not less than 10 pounds.
2) Calculating a z-score for the sample mean and finding the critical value from the z-table based on a 95% confidence level.
3) Comparing the z-score to the critical value and rejecting the null hypothesis if the z-score is lower, meaning the sample provides evidence against the claim.
hypothesis testing-tests of proportions and variances in six sigmavdheerajk
The document provides information about various statistical hypothesis tests that can be used to analyze data and test if process improvements have resulted in significant changes. It discusses one proportion tests, two proportions tests, one-variance tests, two-variances tests, and how to determine which test to use based on the type of data and questions being asked. Examples are also provided of applying these tests using Minitab software to analyze sample data and test hypotheses about changes between before and after process improvement situations. The document aims to help determine the appropriate statistical tests for validating improvements in processes.
This document discusses significance tests for population means and proportions using Student's t-distribution and the normal distribution. It provides examples of hypothesis testing for a population mean using a paired t-test and for a population proportion using a single-sample z-test. It also discusses the assumptions, test statistics, and interpretations for these tests. Confidence intervals are presented as complementary to significance tests for estimating population parameters.
This document provides examples and explanations of common hypothesis testing techniques including:
- Z tests for large samples with known population variance to test claims about population means
- T tests for small samples with unknown population variance
- Tests comparing two population means using Z tests for large samples and T tests for small samples
- One-tailed and two-tailed tests at various significance levels (e.g. 5%, 10%)
Step-by-step solutions and calculations are shown for multiple examples testing claims about means, differences in means, and whether sample data is consistent with hypothesized population parameters.
This document discusses inferential statistics and epidemiological research. It introduces concepts like the central limit theorem, standard error, confidence intervals, hypothesis testing, and different statistical tests. Specifically, it covers:
- The central limit theorem states that sample means will follow a normal distribution, even if the population is not normally distributed.
- Standard error is used to measure sampling variation and determine confidence intervals around sample statistics to estimate population parameters.
- Hypothesis testing involves a null hypothesis of no difference and an alternative hypothesis of a significant difference.
- Common tests discussed include chi-square tests to compare proportions between groups and determine if differences are significant.
A researcher wants to conduct a survey to determine the prevalence of abusive behavior in children aged 6-12 years old in Manila. Using a sample size formula for one group proportions, the required sample size is calculated as 139 children based on: a past reported prevalence of 10%, a desired confidence level of 95%, and a tolerable error of 5%. Adding a 10% expected non-response rate, the total required sample size is rounded up to 153 children.
A second study wants to compare exam pass rates between two statistics class sections. Using the sample size formula for two groups proportions, the required sample size per group is calculated as 44 students based on: detecting a 15% pass rate difference, a confidence level of 95%,
Introduction to hypothesis testing ppt @ bec domsBabasab Patil
This document introduces hypothesis testing, including:
- Formulating null and alternative hypotheses for tests involving population means and proportions
- Using test statistics, critical values, and p-values to test hypotheses
- Defining Type I and Type II errors and their probabilities
- Examples of hypothesis tests for means (using z-tests and t-tests) and proportions (using z-tests) are provided to illustrate the concepts.
The document describes how to calculate a single-sample z-test. It explains that the z-critical value is determined by the significance level, often 0.05 corresponding to z-values of -1.96 and 1.96. An example calculates the z-statistic to test if sample data matches a population claim. It finds the z-statistic is -2.67, which is outside the critical values and considered rare, so the null hypothesis that the sample matches the claim is rejected.
This document discusses descriptive data mining techniques including hierarchical clustering, k-means clustering, and association rules. It provides examples of how to calculate distances between observations using Euclidean distance and standardized z-scores. It also discusses measures of similarity between observations for categorical variables, including matching coefficients and Jaccard's coefficient. Methods for calculating similarity between clusters in hierarchical clustering are presented, including single linkage, complete linkage, and average linkage.
This document provides an overview of different types of variables and methods for summarizing clinical data, including descriptive statistics. It discusses categorical variables like gender and ordinal variables like disease staging. For continuous variables it explains measures of central tendency like mean, median and mode, and measures of variation like range, standard deviation, and interquartile range. Graphs for summarizing univariate data are also covered, such as bar charts for categorical variables and histograms and box plots for continuous variables.
This document discusses regression analysis techniques. It begins with defining regression and its objectives, such as using independent variables to predict dependent variable values. It then covers understanding regression through layman terms and statistical terms. The rest of the document assesses goodness of fit both graphically and statistically. It discusses assumptions of regression like normality, equal variance, and independent errors. It also covers analyzing residuals, outliers, influential cases, and addressing issues like multicollinearity.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document discusses using k-nearest neighbor (k-NN) algorithm for missing data imputation. It compares the performance of mean, median, and standard deviation imputation techniques when combined with k-NN. The techniques are applied to group data of different sizes, and median and standard deviation show better results than mean substitution. Accuracy improves with larger group sizes and higher percentages of missing data. Median and standard deviation imputation have slightly better performance than mean imputation for missing data imputation when combined with k-NN.
This document provides information about describing data using measures of center and spread such as the mean and standard deviation. It discusses Chebyshev's rule, which states that a certain percentage of data will fall within a given number of standard deviations from the mean. For a normal distribution, it presents the empirical rule - that approximately 68% of data lies within 1 standard deviation of the mean, 95% within 2 standard deviations, and 99.7% within 3 standard deviations. Several examples demonstrate calculating these percentages and interpreting data based on its mean and standard deviation. Practice problems at the end have the reader calculate ranges that certain percentages of data will fall within using Chebyshev's rule and the empirical rule.
This document describes various methods of descriptive data analysis, including:
1) Descriptive analysis uses measures of central tendency like mean, median, and mode to summarize large datasets. It is used to describe data through tables and diagrams.
2) Variables can be categorized as dependent or independent. Descriptive analysis examines relationships between these types of variables.
3) Common descriptive methods include frequency distributions, measures of central tendency and variability, as well as graphical presentations like histograms, bar charts, and scatter plots. These methods organize and illustrate key characteristics of quantitative data.
Factor Analysis for Exploratory StudiesManohar Pahan
This document presents a factor analysis that was conducted to identify factors related to fitness trainer popularity. It discusses the research problem, domain, and hypotheses. A 13-item questionnaire was administered to 50 fitness trainers. The data was cleaned and factor analysis was performed. Three factors were extracted based on eigenvalues above 1, explaining 72% of the variance. The factors were interpreted as adapting new fitness programs, introducing latest trends to clients, and client view of the trainer. Reliability analysis found the factors to be reliable.
This document analyzes the psychometric properties of the Swedish version of the Strengths and Difficulties Questionnaire (SDQ) among children and adolescents ages 12-16. It examines the internal consistency, factor structure, and validity of the SDQ using data from community and service contact samples. The results show good internal consistency for most scales. Factor analyses support a bifactor model over the original 5-factor model. Validity analyses find the Emotional Problems scale best distinguishes the samples, while other scales have lower accuracy. Further analyses are suggested to improve understanding of the SDQ's performance in Swedish populations.
This chapter discusses the scientific method in psychology and the need for psychological science to understand human behavior. It covers key concepts like theories, hypotheses, variables, the placebo effect, correlation, experimentation, and statistical analysis. Critical thinking is important to examine assumptions and evaluate evidence presented by psychologists in their research.
This document discusses the use of latent semantic analysis (LSA) for document clustering. It describes issues with traditional information retrieval systems, defines key concepts like synonymy and polysemy, and explains how LSA addresses these issues by reducing the semantic space. An experiment is described where documents are clustered with and without LSA preprocessing, showing that LSA leads to improved cluster quality metrics like purity, entropy, and average intra-cluster similarity. The study demonstrates LSA can perform comparably to dedicated clustering tools for organizing documents by topic.
Exploring Author Gender in Book Rating and Recommendation
M. D. Ekstrand, M. Tian, M. R. I. Kazi, H. Mehrpouyan, and D. Kluver
https://doi.org/10.1145/3240323.3240373
RecSys2018 論文読み会 (2018-11-17) https://atnd.org/events/101334
In this webinar Dr. Lani discusses key points in successfully completing your quantitative analysis. You will learn how to conduct common statistical analyses, how to examine assumptions, how to easily generate APA 6th edition tables and figures, how to use Intellectus Statistics(TM) Software, how to identify and interpret the appropriate statistics, and how to present and summarize your findings.
SSP is now Intellectus Statistics Software. Intellectus Statistics™ software primarily serves the academic and research communities as a powerful statistical package that can be purchased via four distinct cloud based subscriptions. Learn more here: http://www.statisticssolutions.com/buy-intellectus/
This document provides an overview of various multidimensional measurement and factor analysis techniques, including elementary linkage analysis, factor analysis, cluster analysis, multidimensional scaling, structural equation modeling, and multilevel modeling. It discusses the key stages and considerations for conducting factor analysis and interpreting the results, and provides examples of interpreting outputs from SPSS.
This document provides an overview of logistic regression. It begins by explaining that linear regression is not appropriate when the dependent variable is dichotomous. Logistic regression uses an S-shaped logistic function to model the probabilities of different outcomes. The logistic function transforms the non-linear probabilities into linear-looking data that can be modeled using linear regression. Examples are provided to demonstrate how logistic regression can be used to predict the probability of coronary heart disease based on age and to analyze the relationship between patient satisfaction and residence.
An Introduction to Factor analysis pptMukesh Bisht
This document discusses exploratory factor analysis (EFA). EFA is used to identify underlying factors that explain the pattern of correlations within a set of observed variables. The document outlines the steps of EFA, including testing assumptions, constructing a correlation matrix, determining the number of factors, rotating factors, and interpreting the factor loadings. It provides an example of running EFA on a dataset with 11 physical performance and anthropometric variables from 21 participants. The analysis extracts 3 factors that explain over 80% of the total variance.
The standard normal curve & its application in biomedical sciencesAbhi Manu
1) The document discusses the normal distribution and its applications in statistical inference. It is the most important probability distribution used to model many continuous variables in biomedical fields.
2) The normal distribution is characterized by its mean and standard deviation. It is perfectly symmetrical and bell-shaped. Properties of the normal curve include that about 68%, 95%, and 99.7% of the data lies within 1, 2, and 3 standard deviations of the mean, respectively.
3) The standard normal distribution is used to convert raw scores to z-scores in order to compare variables measured on different scales. Z-scores indicate how many standard deviations a score is above or below the mean and can be used to determine probabilities, percentiles
This document discusses exploratory factor analysis (EFA). EFA is used to identify underlying factors that explain the pattern of correlations within a set of observed variables. The document outlines the steps of EFA including testing assumptions, constructing a correlation matrix, determining the number of factors, rotating factors, and interpreting the factor loadings. It provides an example of running EFA on a dataset with 11 physical performance and anthropometric variables from 21 participants. The analysis extracts 3 factors that explain over 80% of the total variance.
This document provides guidance on performing and interpreting logistic regression analyses in SPSS. It discusses selecting appropriate statistical tests based on variable types and study objectives. It covers assumptions of logistic regression like linear relationships between predictors and the logit of the outcome. It also explains maximum likelihood estimation, interpreting coefficients, and evaluating model fit and accuracy. Guidelines are provided on reporting logistic regression results from SPSS outputs.
The document discusses key principles and guidelines for regulated bioanalysis including validation of quantitative bioanalytical methods. It covers validation of both non-chromatographic and chromatographic assays. Some of the main points covered include validation criteria for calibration curves, quality controls, selectivity, accuracy, precision, reproducibility, recovery, and stability. Examples of validation results are also provided to illustrate concepts like matrix effects, column ruggedness, and recovery.
Similar to Galambos N Analysis Of Survey Results (20)
2. » What hypotheses are being tested?
» What types of analyses are planned to test the
hypotheses?
» Look over the instrument and create a map or
outline of possible analysis methods
» What is the magnitude of the differences you
would like to detect?
3. » The most obvious reason for pilot testing is to
be able to estimate the sample size.
» Find potential sources of bias
» Assists in power calculations
» Discover possible distribution problems prior to
surveying the entire sample
4.
5. » A Type I error occurs when a true null
hypothesis is rejected. The probability of a
Type I error is denoted by α, and is the
significance level of the hypothesis test, with
0.05 being a common value for α.
» On the other hand, a Type II error occurs when
the null hypothesis is false and it is not
rejected. A Type II error is denoted by β and is
often set to 0.20.
6. True Results
Experimental Results Ho is true Ho is false
Reject Ho α (Type I error rate) Power = 1 - β
Accept Ho β (Type II error rate)
7. » Statistical Power Analysis for the Behavioral
Sciences—Jacob Cohen
» The power of a significance test is the probability of
rejecting a false null hypothesis, and is equal to 1 -
β. If β is 0.20, the power = 0.80.
» 0.80 is generally considered to be adequate level
for the power
» Since sample size and power are related, a small
sample size results in less power, or reduced
probability of rejecting a false null hypothesis.
8.
9. d = 0.2, 0.5, 0.8 (small, medium, and large effects)
n (for each group) 0.2 0.5 0.8
30 0.03 0.24 0.66
40 0.04 0.35 0.82
50 0.06 0.45 0.91
60 0.07 0.55 >0.995
80 0.12 0.82 >0.995
100 0.29 0.99 >0.995
200 0.29 >0.995 >0.995
500 0.72 >0.995 >0.995
10. » Missing Completely at Random (MCAR)
˃ Given two variables X and Y, the missingness is unrelated to either.
The missing values in X are independent of Y and vice versa.
˃ If the data are MCAR, then listwise deletion is appropriate
» Missing at Random (MAR)
˃ Given two variables X and Y, the missingness is related to or
dependent upon X, but not Y. Suppose X = age and Y = income and
income is more often missing in certain age groups, but within each
age group, no income group is missing more often that any
others, then the data are MAR.
» Nonignorable
˃ Given two variables X and Y, the missingness is related to X, but may
also be related to Y. In our age-income example, certain income
groups within an age group may be less likely to respond.
11. » Select items with a missing percentage greater
than 1% or 2%.
» Recode them into binary variables where with
1=missing and 0=non-missing.
» Analyze these variables by the demographic
variables using t-tests or chi-square, as
appropriate.
» Significant results indicate that missingness is
associated with one or more of the
demographic variables.
12. » Used to uncover relationship patterns among a
group of variables with the goal of reducing the
variables to a smaller group
» Two types of data reduction methods--
confirmatory and exploratory
» Exploratory factor analysis does not assume any
particular structure prior to the analysis and is used
to “explore” relationships between variables
» Confirmatory factor analysis is used to test
hypotheses regarding the underlying structure of a
group of variables
» Traditional factor analysis and principal
components analysis are exploratory data
reduction methods
13. » Principal components analysis a method often
used for reducing the number of variables
» Principal components analysis is part of the
factor analysis procedures in SAS and SPSS
» Although factor analysis (FA) and principal
components analysis (PCA) have mathematical
differences the results are often similar
» Many authors loosely use the term “factor
analysis” to refer to data reduction methods, in
general
14. » Finds groups that are correlated with each
other, possibly measuring the same
construct.
» Reduces the variables in the data to a
smaller number of items that account for
most of the variance of all of the variables in
the data
» The first component accounts for the
greatest amount of variance. Then second
one accounts for the greatest amount not
accounted for by the first component and is
uncorrelated with the first component.
15. » Suggested sample size: at least 100 subjects
and 10 observations per variable
» A correlation analysis of the variables should
result in most correlations greater than 0.3
» Bartlett’s test of sphericity is significant (p <
0.05)
» Kaiser-Meyer-Olkin (KMO) test of sampling
adequacy ≥ 0.6
» Determinant >0.00001 which indicates that
multicollinearity is not a problem
16. » In SPSS select principal components
under “extraction method”
» Select varimax rotation.
˃A rotation uses a transformation to aid in the
interpretation of the factor solution
˃A varimax rotation is orthogonal, so the components
are uncorrelated, which maximizes the column
variance
17. » Kaiser criterion—choose components with
eigenvalues greater than one.
» Scree plot—plot of eigenvalues
˃ Retain the eigenvalues before the leveling off point of the plot.
» Want the proportion of variance accounted
for by each factor (or component) to be 5%
to 10%
» Cumulative variance accounted for should
be 70% to 80%
20. » There should be at least three items with
significant loadings on each component
» Check the conceptualization of the component
items
» With an orthogonal rotation the factor loadings
= correlation between variable and component
» A communality is the proportion of variance in
a variable that is accounted for by the retained
components or factors. A communality is large
if it loads heavily on at least one component.
21. » Factor score
˃Save the regression scores as variables
˃Standardize the survey responses
˃For each subject’s response, multiply the
standardized survey response by the corresponding
regression weights—add the results
» Factor-based score
˃Average the responses of the items in the
component
˃Check for reverse codings and missing data.
22. » Cronbach’s Alpha is used to measure the
reliability or the internal consistency of the
factors or components.
» The variables in a scale are all entered into the
calculation to obtain the alpha score.
» A Cronbach’s alpha > 0.7 is considered to be
sufficient for demonstrating internal
consistency for most social science research,
while values > 0.6 are marginably acceptable