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Ash bus 308 week 5 quiz
This document contains 3 sets of 10 multiple choice questions about statistical analysis techniques including chi square tests, analysis of variance (ANOVA), confidence intervals, and contingency tables. The questions cover topics such as the differences between chi square and ANOVA, how to calculate confidence intervals and determine statistical significance, how to set up and interpret contingency tables, and how to perform and understand chi square tests for independence and goodness of fit.
Research methodology
The document summarizes the key differences between a t-test and a z-test. A t-test is used when the sample size is small and the population variance is unknown, while a z-test is used for large sample sizes where the population variance can be assumed to be known. The t-test follows a t-distribution and the z-test follows a normal distribution. Both tests are used to determine if the means of two datasets differ significantly.
Samle size
This document discusses determining appropriate sample sizes in survey research. It provides formulas and procedures for calculating sample sizes for continuous and categorical variables. For continuous variables, the Cochran formula is presented which considers the acceptable margin of error, alpha level, estimated variance, and population size. For categorical variables, similar considerations are made but typically result in larger sample sizes. The document emphasizes accurately estimating variables like variance and anticipated response rates to determine an appropriate oversampling size to achieve the target minimum sample.
Selection of appropriate data analysis technique
- The document discusses choosing the right statistical method for data analysis, which depends on factors like the number and measurement level of variables, the distribution of variables, the dependence/independence structure, the nature of the hypotheses, and sample size.
- It presents flowcharts for choosing a statistical method based on whether the hypothesis involves one variable (univariate), two variables (bivariate), or more than two variables (multivariate).
- For univariate data, descriptive statistics or a one-sample t-test can be used depending on whether description or inference is the goal; for bivariate data, the choice depends on the nature of the hypothesis (difference or association) and the level of measurement (parametric or nonparame
MEASUREMENT AND SAMPLING TECHNIQUES
This document discusses concepts related to measurement and sampling in field research. It defines key terms like population, element, sampling, sampling frame, census, survey, parameter, and statistic. The document explains that sampling involves selecting a subset of a population to study in order to make inferences about the larger population. It notes that samples must be representative of the population and discusses factors like sampling method, sample size, and sampling error that impact representativeness. The document also outlines the steps in the sampling process and lists some advantages of sampling like being faster and cheaper than studying the entire population.
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This document provides an overview of sampling and statistical inference concepts. It defines key terms like population, sample, parameter, and statistic. It discusses reasons for sampling and types of sampling and non-sampling errors. It also explains important sampling distributions like the sampling distribution of the mean, t-distribution, sampling distribution of a proportion, F distribution, and chi-square distribution. It defines concepts like degrees of freedom, standard error, and the central limit theorem.
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This document discusses key concepts related to sampling theory and measurement in research studies. It defines important sampling terms like population, sampling criteria, sampling methods, sampling error and bias. It also covers levels of measurement, reliability, validity and various measurement strategies like physiological measures, observations, interviews, questionnaires and scales. Finally, it provides an overview of statistical analysis techniques including descriptive statistics, inferential statistics, the normal curve and common tests like t-tests, ANOVA, and regression analysis.
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Ash bus 308 week 5 quiz
This document contains 3 sets of 10 multiple choice questions about statistical analysis techniques including chi square tests, analysis of variance (ANOVA), confidence intervals, and contingency tables. The questions cover topics such as the differences between chi square and ANOVA, how to calculate confidence intervals and determine statistical significance, how to set up and interpret contingency tables, and how to perform and understand chi square tests for independence and goodness of fit.
Research methodology
The document summarizes the key differences between a t-test and a z-test. A t-test is used when the sample size is small and the population variance is unknown, while a z-test is used for large sample sizes where the population variance can be assumed to be known. The t-test follows a t-distribution and the z-test follows a normal distribution. Both tests are used to determine if the means of two datasets differ significantly.
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This document discusses determining appropriate sample sizes in survey research. It provides formulas and procedures for calculating sample sizes for continuous and categorical variables. For continuous variables, the Cochran formula is presented which considers the acceptable margin of error, alpha level, estimated variance, and population size. For categorical variables, similar considerations are made but typically result in larger sample sizes. The document emphasizes accurately estimating variables like variance and anticipated response rates to determine an appropriate oversampling size to achieve the target minimum sample.
Selection of appropriate data analysis technique
- The document discusses choosing the right statistical method for data analysis, which depends on factors like the number and measurement level of variables, the distribution of variables, the dependence/independence structure, the nature of the hypotheses, and sample size.
- It presents flowcharts for choosing a statistical method based on whether the hypothesis involves one variable (univariate), two variables (bivariate), or more than two variables (multivariate).
- For univariate data, descriptive statistics or a one-sample t-test can be used depending on whether description or inference is the goal; for bivariate data, the choice depends on the nature of the hypothesis (difference or association) and the level of measurement (parametric or nonparame
MEASUREMENT AND SAMPLING TECHNIQUES
This document discusses concepts related to measurement and sampling in field research. It defines key terms like population, element, sampling, sampling frame, census, survey, parameter, and statistic. The document explains that sampling involves selecting a subset of a population to study in order to make inferences about the larger population. It notes that samples must be representative of the population and discusses factors like sampling method, sample size, and sampling error that impact representativeness. The document also outlines the steps in the sampling process and lists some advantages of sampling like being faster and cheaper than studying the entire population.
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This document provides an overview of sampling and statistical inference concepts. It defines key terms like population, sample, parameter, and statistic. It discusses reasons for sampling and types of sampling and non-sampling errors. It also explains important sampling distributions like the sampling distribution of the mean, t-distribution, sampling distribution of a proportion, F distribution, and chi-square distribution. It defines concepts like degrees of freedom, standard error, and the central limit theorem.
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This document discusses key concepts related to sampling theory and measurement in research studies. It defines important sampling terms like population, sampling criteria, sampling methods, sampling error and bias. It also covers levels of measurement, reliability, validity and various measurement strategies like physiological measures, observations, interviews, questionnaires and scales. Finally, it provides an overview of statistical analysis techniques including descriptive statistics, inferential statistics, the normal curve and common tests like t-tests, ANOVA, and regression analysis.
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This document outlines an assignment on multivariate analysis of variance (MANOVA). It discusses key concepts like how MANOVA differs from univariate analysis by considering correlations between multiple dependent variables. An example is provided of a study that used MANOVA to analyze phenotypic data from a kiwifruit breeding program to select superior parent plants. The study analyzed 20 variables related to flower, fruit, and vine characteristics. MANOVA was able to extract useful information by considering all variables simultaneously, in a way that would be difficult using only univariate analyses.
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The document discusses the history and definition of degrees of freedom. It states that the earliest concept of degrees of freedom was noted in the 1800s in the works of mathematician Carl Friedrich Gauss. The modern understanding was developed by statistician William Sealy Gosset in 1908, though he did not use the term. The term "degrees of freedom" became popular after English biologist Ronald Fisher began using it in 1922 when publishing reports on his work developing chi squares. Degrees of freedom represent the number of values in a study that can vary freely. They are important for understanding chi-square tests and the validity of the null hypothesis.
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This document provides an overview of various statistical analysis techniques used in inferential statistics, including t-tests, ANOVA, ANCOVA, chi-square, regression analysis, and interpreting null hypotheses. It defines key terms like alpha levels, effect sizes, and interpreting graphs. The overall purpose is to explain common statistical methods for analyzing data and determining the probability that results occurred by chance or were statistically significant.
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This document discusses key concepts related to sample and sample size in research. It defines a population as the entire group being studied, while a sample is a subset of the population. The size of the sample is represented by n and should be large enough to accurately represent the population within a desired level of confidence and margin of error. The document provides formulas for calculating sample size based on factors like population variance, desired confidence level, and acceptable margin of error. An optimal sample size allows for appropriate analysis while minimizing sampling error.
Sampling and Sample Size
The document discusses key concepts related to sampling and sample size, including:
- The difference between a population and a sample, with a sample being a subset of the population.
- Factors that influence sample representativeness, such as sampling procedure, sample size, and participation rate.
- The importance of defining the target population, sampling frame, sampling method, and determining an appropriate sample size.
- The two main types of sampling techniques - probability sampling and non-probability sampling. Probability sampling allows results to be generalized while non-probability sampling does not.
- Formulas for calculating sample sizes needed for estimating population means, comparing two independent samples, and estimating proportions.
- Examples
Sample size calculation
1. Sample size calculation is an important part of ethical scientific research to avoid underpowered studies.
2. There are different approaches to sample size calculation depending on the study design and endpoints, such as comparing proportions, estimating confidence intervals, or analyzing time to event outcomes.
3. Key steps include defining the research hypothesis, primary and secondary endpoints, how and in whom the endpoints will be measured, and determining what difference is clinically meaningful to detect between study groups.
Importance of Sampling design & Sample size
The document discusses the importance and benefits of sampling over a census for research purposes. It notes that sampling saves time and money compared to a census. Additionally, a sample may be more accurate than a census due to limitations in resources and risks of introducing unpopular actions to an entire market. The document also emphasizes that both sampling design and sample size are important considerations, as an inappropriate design will not allow findings to be generalized even with a large sample, and an inadequate sample size cannot meet study objectives. It provides some general guidelines for appropriate sample sizes.
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This document provides an overview of inferential statistics. It defines inferential statistics as using samples to draw conclusions about populations and make predictions. It discusses key concepts like hypothesis testing, null and alternative hypotheses, type I and type II errors, significance levels, power, and effect size. Common inferential tests like t-tests, ANOVA, and meta-analyses are also introduced. The document emphasizes that inferential statistics allow researchers to generalize from samples to populations and test hypotheses about relationships between variables.
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The document discusses different sampling techniques used in surveys. It explains the key difference between population and sample. Some main points:
- Random sampling techniques like simple random sampling, stratified random sampling, and cluster sampling aim to select a sample that accurately represents the overall population. They reduce sampling error.
- Non-random techniques like convenience sampling and judgment sampling do not give all units an equal chance of selection. They do not help reduce sampling error.
- Random sampling is preferred as it can provide sufficiently accurate results while reducing resources needed for surveying an entire population. Non-random sampling is used for convenience.
Non sampling error
This document discusses non-sampling error in surveys. It notes that non-sampling error occurs for reasons other than the sampling technique and can happen at every stage of a survey. There are two main types of non-sampling error: non-observation error, which includes non-coverage and non-response; and measurement error, which is when a respondent's answer differs from the true value. The document outlines various causes of non-sampling error and methods to reduce errors, such as improved training and monitoring of interviewers.
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The document discusses various sampling methods and how to determine sample size. It covers probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also discusses non-probability sampling methods like convenience sampling, judgment sampling, quota sampling, and snowball sampling. The key factors in determining sample size are the margin of error, confidence level, and estimating the variance in the population based on pilot data or previous studies. Cochran's formula is commonly used to calculate sample sizes needed for both continuous and categorical variables.
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This document discusses research methodology and survey methods. It outlines the typical steps in research including problem formulation, literature review, hypothesis formulation, data collection and analysis. It then discusses different types of surveys and sampling methods. It covers probability sampling techniques like simple random sampling, stratified random sampling and systematic random sampling. It also discusses non-probability sampling and different data collection sources like primary and secondary data. Finally, it discusses various methods for data processing, analysis and representation through graphs, charts and diagrams.
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Teknik sampling.pptx
This document discusses sampling techniques and sample size. It defines key terms like population, sample, sampling frame, and sampling schemes. It describes different sampling methods like probability and non-probability sampling. Probability sampling methods allow results to be generalized to the population and include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Sample size is determined based on desired confidence level, precision, and power to detect differences between groups. Sample size calculations are provided for estimating population parameters and comparing two groups.
RUNNING HEAD ONE WAY ANOVA1ONE WAY ANOVA .docx
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Stacy Hernandez
PSY7620
Dr. Lorie Fernandez
Capella University
Data Analysis and Application (DAA)
The one way ANOVA is used to determine whether there are any significant differences between the means of two or more independent groups. In this sample, the file grades.sav is used with section (independent variable) and quiz3 (dependent variable).
Data File Description
1. The one way ANOVA is used to determine whether there are any significant differences between the means of two or more independent groups.
2. In this sample, the file grades.sav is used with section (independent variable) and quiz3 (dependent variable).
3. The sample size (N) is 105.
Testing Assumptions
The dependent variable, quiz3, is measured at the interval or ratio level (meaning continuous). The dependent variable (quiz3) in this case, is therefore continuous since it ranges from one to 10. The independent variable (section) should consist of two or more categorical independent groups. In this case, the independent variable (section), has three groups, therefore it meets this assumption. There should be independence of observation, meaning that there is no relationship between the observations in each group or between the groups themselves. There should be no significant outliers, although there are single data points within the data that do not follow a normal pattern. Therefore, the outliers found will a negative effect on the one-way ANOVA, reducing the validity of the results.
Note the above boxplot indicates outliers in section two, with the id of 21.
The dependent variable (quiz3) should approximately have a normal distribution for each category of the independent variable (section). The null hypothesis is that the data is of a normal distribution, that the mean (average value of the dependent variable) is the same for all groups.
Ho โ the observed distribution fits the normal distribution.
The alternative hypothesis is that the data does not have a normal distribution; the average is not the same for all groups.
Ha โ the observed distribution does not fit the normal distribution.
It is observed that the data is not normally distributed. Most sections have quiz3 values between five and nine note this is a visual estimate. Note that the largest group also has the largest value of quiz3. The statistics from the histogram of quiz3 reveal that the Mean is 8.05; the Standard Deviation is 2.322, with a total number N of 105.
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
Skewness
Kurtosis
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
Statistic
Std. Error
quiz3
105
0
10
8.05
2.322
-1.177
.236
.805
.467
section
105
1
3
2.00
.797
.000
.236
-1.419
.467
Valid N
105
When looking at skewness, for a perfectly normal and symmetrical distribution, it has a value of zero (Warner, 20 ...
eMba i qt unit-5_sampling
This document discusses key concepts in quantitative techniques related to population, sample, sampling, and sample size calculation. It defines population as the total set of measurements of interest, and sample as a subset of the population. Probability and non-probability sampling methods are described. Probability sampling allows results to be generalized to the population, while non-probability sampling does not. Several probability sampling techniques are explained, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. The document also covers concepts like sampling error, confidence level, statistical power, and formulas for calculating minimum sample sizes. Sample size determination depends on factors like confidence level, power, expected difference, and standard deviation. Formulas presented can be used
Chi
This document provides information about the chi-square test, including:
- The chi-square test determines if there is a significant difference between expected and observed frequencies. It tests if differences are due to chance or are real differences.
- Examples of chi-square tests given include Pearson's chi-square test, Yates's correction, and tests for variance, independence, and homogeneity using contingency tables.
- Requirements for the chi-square test include quantitative data, categories, independent observations, adequate sample size, simple random sampling, and frequency data. All observations must be used.
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This document provides information on various survey methods and concepts. It discusses sampling methods like probability sampling (simple random sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling) and non-probability sampling (convenience sampling, purposive sampling, quota sampling). It also covers survey design types, importance of sampling, acceptable response rates, defining populations, steps in survey research, and increasing response rates. Classification of survey research methods includes temporal classification into cross-sectional and longitudinal surveys.
Sampling techniques new
This document provides an overview of sampling and key sampling concepts. It defines population and sample, and describes different types of sampling including: probability sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It also describes non-probability sampling methods like convenience sampling, quota sampling, and purposive sampling. The document discusses important sampling concepts like sampling frame, sampling error, and determining sample size. It provides examples and limitations of different sampling techniques.
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Degrees of freedom
The document discusses the history and definition of degrees of freedom. It states that the earliest concept of degrees of freedom was noted in the 1800s in the works of mathematician Carl Friedrich Gauss. The modern understanding was developed by statistician William Sealy Gosset in 1908, though he did not use the term. The term "degrees of freedom" became popular after English biologist Ronald Fisher began using it in 1922 when publishing reports on his work developing chi squares. Degrees of freedom represent the number of values in a study that can vary freely. They are important for understanding chi-square tests and the validity of the null hypothesis.
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This document discusses sample design and the t-test. It covers the sample design process which includes defining the population, sample frame, sample size, and sampling procedure. It also discusses probability and non-probability sampling techniques. The document then explains what a t-test is and how it can be used to test for differences between two group means. It covers the assumptions, procedures, hypotheses, and interpretation of t-test results.
Inferential Statistics
This document provides an overview of various statistical analysis techniques used in inferential statistics, including t-tests, ANOVA, ANCOVA, chi-square, regression analysis, and interpreting null hypotheses. It defines key terms like alpha levels, effect sizes, and interpreting graphs. The overall purpose is to explain common statistical methods for analyzing data and determining the probability that results occurred by chance or were statistically significant.
Sample and sample size
This document discusses key concepts related to sample and sample size in research. It defines a population as the entire group being studied, while a sample is a subset of the population. The size of the sample is represented by n and should be large enough to accurately represent the population within a desired level of confidence and margin of error. The document provides formulas for calculating sample size based on factors like population variance, desired confidence level, and acceptable margin of error. An optimal sample size allows for appropriate analysis while minimizing sampling error.
Sampling and Sample Size
The document discusses key concepts related to sampling and sample size, including:
- The difference between a population and a sample, with a sample being a subset of the population.
- Factors that influence sample representativeness, such as sampling procedure, sample size, and participation rate.
- The importance of defining the target population, sampling frame, sampling method, and determining an appropriate sample size.
- The two main types of sampling techniques - probability sampling and non-probability sampling. Probability sampling allows results to be generalized while non-probability sampling does not.
- Formulas for calculating sample sizes needed for estimating population means, comparing two independent samples, and estimating proportions.
- Examples
Sample size calculation
1. Sample size calculation is an important part of ethical scientific research to avoid underpowered studies.
2. There are different approaches to sample size calculation depending on the study design and endpoints, such as comparing proportions, estimating confidence intervals, or analyzing time to event outcomes.
3. Key steps include defining the research hypothesis, primary and secondary endpoints, how and in whom the endpoints will be measured, and determining what difference is clinically meaningful to detect between study groups.
Importance of Sampling design & Sample size
The document discusses the importance and benefits of sampling over a census for research purposes. It notes that sampling saves time and money compared to a census. Additionally, a sample may be more accurate than a census due to limitations in resources and risks of introducing unpopular actions to an entire market. The document also emphasizes that both sampling design and sample size are important considerations, as an inappropriate design will not allow findings to be generalized even with a large sample, and an inadequate sample size cannot meet study objectives. It provides some general guidelines for appropriate sample sizes.
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This document provides an overview of key concepts in sampling and statistics. It defines population as the entire set of items from which a sample can be drawn. It discusses different types of sampling methods including probability sampling (simple random, stratified, cluster, systematic) and non-probability sampling (convenience, judgmental, quota, snowball). It also defines key terms like bias, precision, randomization. The document discusses the sampling process and compares advantages and disadvantages of sampling. It provides examples of calculating standard error of mean and proportion. Finally, it distinguishes between standard deviation and standard error.
Inferential Statistics
This document provides an overview of inferential statistics. It defines inferential statistics as using samples to draw conclusions about populations and make predictions. It discusses key concepts like hypothesis testing, null and alternative hypotheses, type I and type II errors, significance levels, power, and effect size. Common inferential tests like t-tests, ANOVA, and meta-analyses are also introduced. The document emphasizes that inferential statistics allow researchers to generalize from samples to populations and test hypotheses about relationships between variables.
Sampling design ppt
Sampling
Population Vs. Sample
Characteristics of Good Samples
Terminology
Sampling Process
Sampling Design Process
2. sampling techniques
The document discusses different sampling techniques used in surveys. It explains the key difference between population and sample. Some main points:
- Random sampling techniques like simple random sampling, stratified random sampling, and cluster sampling aim to select a sample that accurately represents the overall population. They reduce sampling error.
- Non-random techniques like convenience sampling and judgment sampling do not give all units an equal chance of selection. They do not help reduce sampling error.
- Random sampling is preferred as it can provide sufficiently accurate results while reducing resources needed for surveying an entire population. Non-random sampling is used for convenience.
Non sampling error
This document discusses non-sampling error in surveys. It notes that non-sampling error occurs for reasons other than the sampling technique and can happen at every stage of a survey. There are two main types of non-sampling error: non-observation error, which includes non-coverage and non-response; and measurement error, which is when a respondent's answer differs from the true value. The document outlines various causes of non-sampling error and methods to reduce errors, such as improved training and monitoring of interviewers.
8 sampling & sample size (Dr. Mai,2014)
The document discusses various sampling methods and how to determine sample size. It covers probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also discusses non-probability sampling methods like convenience sampling, judgment sampling, quota sampling, and snowball sampling. The key factors in determining sample size are the margin of error, confidence level, and estimating the variance in the population based on pilot data or previous studies. Cochran's formula is commonly used to calculate sample sizes needed for both continuous and categorical variables.
Sampling
This document discusses research methodology and survey methods. It outlines the typical steps in research including problem formulation, literature review, hypothesis formulation, data collection and analysis. It then discusses different types of surveys and sampling methods. It covers probability sampling techniques like simple random sampling, stratified random sampling and systematic random sampling. It also discusses non-probability sampling and different data collection sources like primary and secondary data. Finally, it discusses various methods for data processing, analysis and representation through graphs, charts and diagrams.
Student's T Test
The document discusses Student's t-test, which is useful for three situations: when sample sizes are small, when the population standard deviation is unknown, and when comparing two samples. It describes how Student's t-test addresses the problems with small sample sizes that violate the Central Limit Theorem. It also explains how the t-test can be used to estimate an unknown population standard deviation from the sample standard deviation. Finally, it provides examples of using a t-test to compare the means of two samples and of using a paired t-test to compare salaries between two cities for the same jobs.
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This document discusses sampling techniques and sample size. It defines key terms like population, sample, sampling frame, and sampling schemes. It describes different sampling methods like probability and non-probability sampling. Probability sampling methods allow results to be generalized to the population and include simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Sample size is determined based on desired confidence level, precision, and power to detect differences between groups. Sample size calculations are provided for estimating population parameters and comparing two groups.
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Stacy Hernandez
PSY7620
Dr. Lorie Fernandez
Capella University
Data Analysis and Application (DAA)
The one way ANOVA is used to determine whether there are any significant differences between the means of two or more independent groups. In this sample, the file grades.sav is used with section (independent variable) and quiz3 (dependent variable).
Data File Description
1. The one way ANOVA is used to determine whether there are any significant differences between the means of two or more independent groups.
2. In this sample, the file grades.sav is used with section (independent variable) and quiz3 (dependent variable).
3. The sample size (N) is 105.
Testing Assumptions
The dependent variable, quiz3, is measured at the interval or ratio level (meaning continuous). The dependent variable (quiz3) in this case, is therefore continuous since it ranges from one to 10. The independent variable (section) should consist of two or more categorical independent groups. In this case, the independent variable (section), has three groups, therefore it meets this assumption. There should be independence of observation, meaning that there is no relationship between the observations in each group or between the groups themselves. There should be no significant outliers, although there are single data points within the data that do not follow a normal pattern. Therefore, the outliers found will a negative effect on the one-way ANOVA, reducing the validity of the results.
Note the above boxplot indicates outliers in section two, with the id of 21.
The dependent variable (quiz3) should approximately have a normal distribution for each category of the independent variable (section). The null hypothesis is that the data is of a normal distribution, that the mean (average value of the dependent variable) is the same for all groups.
Ho โ the observed distribution fits the normal distribution.
The alternative hypothesis is that the data does not have a normal distribution; the average is not the same for all groups.
Ha โ the observed distribution does not fit the normal distribution.
It is observed that the data is not normally distributed. Most sections have quiz3 values between five and nine note this is a visual estimate. Note that the largest group also has the largest value of quiz3. The statistics from the histogram of quiz3 reveal that the Mean is 8.05; the Standard Deviation is 2.322, with a total number N of 105.
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
Skewness
Kurtosis
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
Statistic
Std. Error
quiz3
105
0
10
8.05
2.322
-1.177
.236
.805
.467
section
105
1
3
2.00
.797
.000
.236
-1.419
.467
Valid N
105
When looking at skewness, for a perfectly normal and symmetrical distribution, it has a value of zero (Warner, 20 ...
eMba i qt unit-5_sampling
This document discusses key concepts in quantitative techniques related to population, sample, sampling, and sample size calculation. It defines population as the total set of measurements of interest, and sample as a subset of the population. Probability and non-probability sampling methods are described. Probability sampling allows results to be generalized to the population, while non-probability sampling does not. Several probability sampling techniques are explained, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. The document also covers concepts like sampling error, confidence level, statistical power, and formulas for calculating minimum sample sizes. Sample size determination depends on factors like confidence level, power, expected difference, and standard deviation. Formulas presented can be used
Chi
This document provides information about the chi-square test, including:
- The chi-square test determines if there is a significant difference between expected and observed frequencies. It tests if differences are due to chance or are real differences.
- Examples of chi-square tests given include Pearson's chi-square test, Yates's correction, and tests for variance, independence, and homogeneity using contingency tables.
- Requirements for the chi-square test include quantitative data, categories, independent observations, adequate sample size, simple random sampling, and frequency data. All observations must be used.
Kamrizzaman sir 4, 5 & 6 chapter, 5011
This document provides information on various survey methods and concepts. It discusses sampling methods like probability sampling (simple random sampling, systematic sampling, stratified sampling, cluster sampling, multistage sampling) and non-probability sampling (convenience sampling, purposive sampling, quota sampling). It also covers survey design types, importance of sampling, acceptable response rates, defining populations, steps in survey research, and increasing response rates. Classification of survey research methods includes temporal classification into cross-sectional and longitudinal surveys.
Sampling techniques new
This document provides an overview of sampling and key sampling concepts. It defines population and sample, and describes different types of sampling including: probability sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling. It also describes non-probability sampling methods like convenience sampling, quota sampling, and purposive sampling. The document discusses important sampling concepts like sampling frame, sampling error, and determining sample size. It provides examples and limitations of different sampling techniques.
Sampling techniques new
The document discusses sampling methods and concepts. It defines key terms like population, sample, sampling frame and sampling error. It describes different types of sampling including probability sampling methods like simple random sampling, systematic random sampling and cluster sampling. It also discusses non-probability sampling and factors to consider in determining sample size. The document provides guidance on calculating sampling error and outlines principles of good sampling.
Critique of an ArticleQUESTIONS TO ASK WHILE READING A RESEARCH .docx
Critique of an Article
QUESTIONS TO ASK WHILE READING A RESEARCH ARTICLE
INTRODUCTION โ Why did they start?
ยท Why is this research important? How well did the authors justify this in the review of literature? Did they summarize the existing literature relevant to the issue addressed?
ยท What is the research question/s of interest (purpose/objectives) or hypothesis? Is the phrasing comprehensive, clear, specific?
ยท Do the authors use well-defined, non-ambiguous terminology (avoiding jargon)?
METHODS โ What did they do?
ยท Did the authors provide sufficient detail for the replication of the study?
ยท Whatโs the study design? Are there any potential threats to the internal and external validity of the design?
ยท Have the data collection procedures and instruments described? What is the dependent variable (DV)? Independent variables (IV)? Is the dependent variable (outcome measure) well defined and clearly measurable? Are the IVs (exposure or condition to be manipulated) well defined/clearly measurable?
ยท Who is the sample population? Does this population place any limits on the generalizability of the results? Was the sampling procedure/subject selection described?
ยท Sample size โ How do the authors justify the number of subjects in the study? Are โdrop outsโ accounted for?
ยท How was their sample assessed? Was the measure validated?
ยท Does the study design have any control condition to rule out chance findings? Are there threats to the internal and/or external validity of the study?
ยท Is the time frame adequate to answer the research question?
RESULTS โ What did they find?
ยท Were the number of subjects in each group or subgroup used in the analysis specified? Were the subject characteristics summarized?
ยท Did the results reported relate to the specified objective/hypotheses?
ยท Do the tables and figures โspeak for themselves?โ Are they useful and clear?
ยท Are the tables adequately titled, labeled, and footnoted so they can be interpreted without reference to text?
DISCUSSION โ What do the results mean?
ยท Did authors discuss the results in relations to the objectives/hypotheses? Is there a clear distinction between the results of the hypothesis being tested and other, unexpected findings being discussed?
ยท Were the results discussed in relation to those from similar studies? Discussed in relation to the theoretical background for this field of study?
ยท Are the authors justified in the strength of the statements they make in the study? Did they offer alternative explanations for results?
ยท How will these results add to this field of research? Were potential directions for future studies mentioned?
REFERENCES โ Are they comprehensive and current?
MISCELLANEOUS
Did the article:
ยท Define ambiguous terms?
ยท Define acronyms the first time they appeared in text?
ยท Provide references, citations for statement of facts?
ยท Follow logical sequencing of facts?
ยท Present information clearly and concisely?
REMEMBER
A โcritiqueโ does .
Sampling
This document discusses key principles of statistical sampling:
1) The principle of statistical regularity states that a randomly selected moderate sample will possess the characteristics of the larger population.
2) The principle of inertia of large numbers states that as sample size increases, results become more reliable and accurate due to balancing of variations.
3) It describes different sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling.
Answer all questions individually and cite all work!!1. Provid.docx
Answer all questions individually and cite all work!!
1. Provide an example of an idea, creativity, and innovation and argue why it best fits that category.
2. Identify three catalysts to enable innovativeness. Explain how they would enable innovation in your organization.
3. Why is it significant that an organization allow for failure? What are some significant ways an organization can allow for failure and still find success?
4. Making a pivot has saved organizations from completely deteriorating. Research an organization of your choice that has made an impactful pivot. Write an 8-10 sentence summary of the organization and the monumental pivot.ย
iStockphoto/Thinkstock
chapter 11
Nominal Data and the
Chi-Square Tests: What Occurs
Versus What Is Expected
Learning Objectives
After reading this chapter, you will be able to. . .
1. describe nominal data.
2. complete and explain the chi-square goodness-of-fit test.
3. complete and explain the chi-square test of independence.
4. present and interpret the results of the two types of chi-square test in proper APA format.
CN
CO_LO
CO_TX
CO_NL
CT
CO_CRD
suk85842_11_c11.indd 407 10/23/13 1:45 PM
CHAPTER 11Section 11.1 Nominal Data
When there was an important development in statistical analysis in the early part of the 20th century, more often than not Karl Pearson was associated with it. Many of those
who made important contributions were members of the department that Pearson founded
at University College London. William Sealy Gosset, who developed the t-tests; Ronald A.
Fisher, who developed analysis of variance; and Charles Spearman, who developed factor
analysis, all gravitated to Pearsonโs department at some point. Although social relations
among these men were not always harmonious, they were enormously productive schol-
ars, and this was particularly true of Pearson. Besides the correlation coefficient named for
him, Pearson also developed an analytical approach related to Spearmanโs factor analysis
called principal components analysis, and he developed the procedures that are the sub-
jects of this chapter, the chi-square tests. The Greek letter chi (x) is pronounced โkieโ like
โpie.โ Chi is the equivalent of the letter c, rather than the letter x, which it resembles.
11.1 Nominal Data
With the exception of Spearmanโs rho in Chapter 9, the attention in Chapters 1 through 10 has been directed at procedures designed for interval or ratio data. Sometimes
the data is not interval scale, nor is it the ordinal-scale data that Spearmanโs rho accom-
modates. When the data is nominal scale, often one of the chi-square (x2) tests is used.
It will be helpful to review what makes data nominal scale. Nominal data either fits into a
category or it does not, which is why nominal data is sometimes called categorical or clas-
sification data. Because the analysis is based on counting the data, it is also called count or
frequency data. Compared to ratio, interva ...
Sampling methods
This document discusses various sampling methods used in research studies. It begins with defining key terms like population, sampling, target population and sampling frame. It then describes the main types of sampling methods - probability sampling methods like simple random sampling, stratified random sampling and cluster sampling as well as non-probability sampling methods like convenience sampling and snowball sampling. The advantages and limitations of different sampling methods are provided. The document emphasizes that probability sampling allows generalization of results to the target population while non-probability sampling does not. It concludes by noting some sources of error in sampling.
Chi square test
This is the information about biostatistics and there are various test which are performed in the laboratory to the field. these tests are f test chi square test etc. on the basis of these data we confirmed probability and calculation of variability. here is the whole information about the chi square test
Class24 chi squaretestofindependenceposthoc
This document provides an overview of the chi-square test of independence through 18 slides. It defines independence, demonstrates it, discusses expected frequencies, and outlines the 5 steps for conducting a chi-square test of independence: 1) checking assumptions, 2) stating hypotheses and significance level, 3) identifying the sampling distribution and test statistic, 4) computing the test statistic, and 5) making a decision and interpreting results. It also covers examining standardized residuals to identify which cells are contributing most to a significant result.
Class24 chi squaretestofindependenceposthoc(1)
This document provides an overview of the chi-square test of independence through 15 slides. It defines independence, demonstrates it using an example, and outlines the 5 steps for conducting a chi-square test of independence: 1) checking assumptions, 2) stating hypotheses and level of significance, 3) identifying the sampling distribution and test statistic, 4) computing the test statistic, and 5) making a decision and interpreting results. It also discusses how to identify which cells are contributing to a significant result using standardized residuals.
Sample Size Estimation and Statistical Test Selection
Sample Size estimation and a step-by-step approach for choosing an appropriate statistical test for data analysis.
Nonparametric tests assignment
This document provides information about non-parametric statistical tests. It discusses the Mann-Whitney U test, chi-square test, and how to perform chi-square tests in SPSS. Key points include:
- Non-parametric tests do not assume a specific data distribution and can be used for small sample sizes, ordinal data, or outliers. Examples include Mann-Whitney U, Kruskal-Wallis, and chi-square tests.
- Chi-square tests independence between two categorical variables. Assumptions include frequencies data and expected counts over 5 in 80% of cells.
- To perform a chi-square test in SPSS, select two categorical variables, choose crosstabs
probability and non-probability samplings
This document provides an overview of different sampling methods, including probability and non-probability sampling. It defines key terms like population, sample, and frame. It then describes various probability sampling techniques like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Examples are provided for each. Advantages and disadvantages of each method are also outlined. The document concludes by describing non-probability sampling techniques like convenience sampling, purposive sampling, quota sampling, and snowball sampling.
Sampling Variability And The Precision Of A Sample by Dr Sindhu Almas copy.pptx
What use is all this stuff about variability?
Sampling โ the big idea
Sampling In Practice
Sampling โ the big idea
Need For Sampling
Disadvantages Of Sampling
Types Of Sampling
Factors Affecting Sample Size
Sampling Distribution
Calculating A Confidence Interval Using Software
Data Analysis (Hypothesis testing, Regression).pptx
This document provides an overview of statistical concepts and tests related to hypothesis testing and comparing means, including t-tests, chi-squared tests, and confidence intervals. It uses examples from a study on the Titanic and a weight loss study to demonstrate how to conduct and interpret these tests. Key points covered include defining null and alternative hypotheses; determining whether to use paired or independent t-tests; checking assumptions; and interpreting p-values, test statistics, and confidence intervals. Worked examples guide the reader in applying these statistical concepts to analyze real data.
sampling and statiscal inference
This document provides an overview of key concepts in sampling and statistics. It defines key terms like population, parameter, sample, and sampling error. It discusses different sampling methods like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. It also covers non-probability sampling methods. The document explains how to calculate sampling error and discusses other sources of bias. Overall, it serves as an introduction to important statistical concepts for sample surveys and studies.
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A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the bodyโs response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
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- 1. ASH BUS 308 Week 5 Quiz (3 Set) NEW Check this A+ tutorial guideline at http://www.uopassignments.com/bus-308-ash/bus-308- week-5-quiz-recent For more classes visit http://www.uopassignments.com Question 1. Compared to the ANOVA test, Chi Square procedures are not powerful (able to detect small differences). Question 2. In confidence intervals, the width of the interval depends only on the variation within the data set. Question 3. The percent confidence interval is the range having the percent probability of containing the actual population parameter. Question 4. The Chi Square test can be performed on categorical (nominal) level data. Question 5. For a one sample confidence interval, the interval is calculated around the estimated population or standard. Question 6. The chi square test is very sensitive to small differences in frequency distributions. Question 7. The probability that the actual population mean will be outside of a 98% confidence interval is Question 8. A confidence interval is generally created when statistical tests fail to reject the null hypothesis โ that is, when results are not statistically significant. Question 9. A contingency table is a multiple row and multiple column table showing counts in each cell. Question 10. For a one sample confidence interval, if the interval contains the population mean, the corresponding t-test will have a statistically significant result โ rejecting the null hypothesis.
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