Using SPSS in Education Part 2
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This document provides tips and guidance for using Excel to enter data that will be imported into SPSS for analysis. It discusses best practices for data entry in Excel like using consistent variable names and formats, avoiding blank rows, and entering missing data values. It also recommends double data entry to check for errors. The document then explains how to import the Excel file into SPSS and examine the data, including generating frequency tables to check for errors or unusual values. It provides examples of common statistical analyses in SPSS like two-sample t-tests to compare group means, one-way ANOVA to compare multiple group means, and correlation analysis to examine relationships between variables. Throughout, it emphasizes correctly interpreting SPSS output and reporting the results of statistical
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1.1
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Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)SERvice – Years of serviceGender: 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
Running head Organization behaviorOrganization behavior 2.docx
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoi ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
Ash bus 308 week 2 problem set new
This document provides instructions for a problem set analyzing employee salary data. It instructs the user to copy salary and employee data into their assignment file. It then provides guidance on using descriptive statistics and t-tests to analyze differences in compensation ratios between male and female employees. The goal is to determine if there is statistical evidence that male and female employees are paid equally for equal work.
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Chapter 18 – Pricing Setting in the Business WorldThere are few .docx
Chapter 18 – Pricing Setting in the Business World
There are few Methods for setting pricing – costs methods vs demand methods
Formulas considering costs and mark up will help you to do the Problem set assignment:
1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100)
Formula for setting price with the markup method
SP = Cost/(1- Markup %)
Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be….
SP = 15/(1-.40)=.60
= $25.00
2. Understand Role of different costs – fixed, variable, total costs and average costs
3. What is the breakeven point? Formula for calculating the Break Even point.
BE = Total Fixed Cost/Fixed cost contribution
Fixed Cost Contribution=Price – variable cost
4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price.
.ANOVA
Analysis of Variance is a method of testing the equality of three or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more population means when there is
one factor of interest.
Requirements
The populations have distributions that are approximately normal.
The populations have the same variance.
The samples are simple random samples of quantitative data.
The samples are independent of each other.
The different samples are from populations that are categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test.
Example
A professor at a local University believes there is a relationship between head size and
the major of the students in her biostatistics classes. She takes a random sample of 20
students from each of three classes and records their major and head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
F
The test statistic follows the F distribution which has two degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a software program is
generally used for the calculations. We will be using Microsoft Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box appears.
Input range is where the data is in the table. Be sure to put a check in the box for labels
in.
1Running head RESEARCH PROJECT PROPOSAL 13RESEA.docx
1
Running head: RESEARCH PROJECT PROPOSAL
1
3
RESEARCH PROJECT PROPOSAL
Research Project Proposal
Name Here
South University Online
Research Project Proposal
Provide a brief, general introduction to the topic and importance to your role (do not use first person anywhere in the paper). Conclude with a thesis statement.
Background and Significance of the Problem
Discuss here.
Statement of the Problem and Purpose of the Study
Discuss here.
Literature Review
Discuss here.
Research Questions, Hypothesis, and Variables
Discuss here.
Theoretical Framework
Overview and Guiding Propositions
Discuss here.
Application of Theory to Study Focus
Discuss here.
Methodology
Sample/Setting
Discuss here.
Sampling Strategy
Discuss here.
Research Design
Discuss here.
Extraneous Variables
Discuss here.
Instruments
Discuss here.
Description of the Intervention
Discuss here.
Data Collection Procedures
Discuss here.
Data Analysis Plans
Discuss here.
Ethical Issues
Discuss here.
Limitations of Proposed Study
Discuss here.
Implications for Practice
Discuss here.
References
Appendices
Lecture 6
(Additional information on t-tests and hypothesis testing)
Lecture 5 focused on perhaps the most common of the t-tests, the two sample assuming
equal variance. There are other versions as well; Excel lists two others, the two sample assuming
unequal variance and the paired t-test. We will end with some comments about rejecting the null
hypothesis.
Choosing between the t-test options
As the names imply each of the three forms of the t-test deal with different types of data
sets. The simplest distinction is between the equal and unequal variance tests. Both require that
the data be at least interval in nature, come from a normally distributed population, and be
independent of each other – that is, collected from different subjects.
The F-test for variance.
To determine if the population variances of two groups are statistically equal – in order to
correctly choose the equal variance version of the t-test – we use the F statistic, which is
calculated by dividing one variance by the other variance. If the outcome is less than 1.0, the
rejection region is in the left tail; if the value is greater than 1.0, the rejection region is in the
right tail. In either case, Excel provides the information we need.
To perform a hypothesis test for variance equality we use Excel’s F-Test Two-Sample for
Variances found in the Data Analysis section under the Data tab. The test set-up is very similar
to that of the t-test, entering data ranges, checking Labels box if they are included in the data
ranges, and identifying the start of the output range. The only unique element in this test is the
identification of our alpha level.
Since we are testing for equality of variances, we have a two sample test and the rejection
region is again in both tails. This means that our rejection region in each tail is 0.25. The F-test
id ...
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2. Choosing an appropriate question format is important so responses are understandable and analyzable. Questions types include single answers, multiple choices, scales, and grids.
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This document provides an introduction to SPSS (Statistical Package for Social Sciences) software. It discusses opening and closing SPSS, the structure and windows of SPSS including the Data View and Variable View windows for entering data. It defines key concepts in SPSS like variables, different types of variables (nominal, ordinal, interval, ratio), and the process of defining variables in the Variable View window by specifying name, type, width, labels, values etc. before entering data. Examples are given around designing an experiment with independent and dependent variables and dealing with extraneous variables.
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This document provides an overview of SPSS (Statistical Package for the Social Sciences) software and quantitative data analysis. It discusses what SPSS is used for, how to access SPSS, and some basic definitions related to quantitative research such as variables, levels of measurement, and descriptive statistics. It also gives examples of entering and working with quantitative data in SPSS, including creating a codebook, defining variables, inputting data, and saving files. The overall summary is that SPSS is a tool for analyzing quantitative data through descriptive statistics and significance testing to answer research questions.
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Chapter 1
The document outlines several key scientific concepts:
1) The scientific method is a standardized approach used by scientists involving making observations, forming hypotheses, conducting experiments, analyzing results, and publishing conclusions.
2) Scientific notation is a way to write very large or small numbers using powers of ten. It involves moving the decimal and multiplying by a power of ten.
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1.1
Percentages are ratios expressed as a fraction of 100. They are used commonly to represent things like discounts, interest rates, and exam scores. Examples are given to demonstrate how to calculate percentages of different amounts. Specifically, 100% means all, 50% means half, and 3.5% means 3.5/100. Charts can also help visualize percentages and compare amounts spent on different items from a total amount. Understanding percentages is important for real-life applications like discounts, loans, investments, and exam results.
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Similar to Using SPSS in Education Part 2
Excel Files AssingmentsCopy of Student_Assignment_File.11.01..docx
Excel Files Assingments/Copy of Student_Assignment_File.11.01.2016.xlsx
DataIDSalaryCompa-ratioMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1GradeCopy Employee Data set to this page.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)SERvice – Years of serviceGender: 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa-ratio - salary divided by midpoint
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descriptive statistics,then the cells should have an "=XX" formula in them, where XX is the column and row number showing the value in the descriptive statistics table. If you choose to generate each value using fxfunctions, then each function should be located in the cell and the location of the data values should be shown.So, Cell D31 - as an example - shoud contain something like "=T6" or "=average(T2:T26)". Having only a numerical value will not earn full credit.The reason for this is to allow instructors to provide feedback on Excel tools if the answers are not correct - we need to see how the results were obtained.In starting the analysis on a research question, we focus on overall descriptive statistics and seeing if differences exist. Probing into reasons and mitigating factors is a follow-up activity.1The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems willfocus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall.Sorting the compa-ratios into male and females will require you copy and paste the Compa-ratio and Gender1 columns, and then sort on Gender1.The values for age, performance rating, and service are prov ...
Running head Organization behaviorOrganization behavior 2.docx
Running head: Organization behavior
Organization behavior 2
Organization behavior
Name:
Institution:
Course:
Date:
Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc.
One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015).
Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012).
References
Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley.
O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoi ...
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
Ash bus 308 week 2 problem set new
This document provides instructions for a problem set analyzing employee salary data. It instructs the user to copy salary and employee data into their assignment file. It then provides guidance on using descriptive statistics and t-tests to analyze differences in compensation ratios between male and female employees. The goal is to determine if there is statistical evidence that male and female employees are paid equally for equal work.
Ash bus 308 week 2 problem set new
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Ash bus 308 week 2 problem set new
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Chapter 18 – Pricing Setting in the Business WorldThere are few .docx
Chapter 18 – Pricing Setting in the Business World
There are few Methods for setting pricing – costs methods vs demand methods
Formulas considering costs and mark up will help you to do the Problem set assignment:
1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100)
Formula for setting price with the markup method
SP = Cost/(1- Markup %)
Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be….
SP = 15/(1-.40)=.60
= $25.00
2. Understand Role of different costs – fixed, variable, total costs and average costs
3. What is the breakeven point? Formula for calculating the Break Even point.
BE = Total Fixed Cost/Fixed cost contribution
Fixed Cost Contribution=Price – variable cost
4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price.
.ANOVA
Analysis of Variance is a method of testing the equality of three or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more population means when there is
one factor of interest.
Requirements
The populations have distributions that are approximately normal.
The populations have the same variance.
The samples are simple random samples of quantitative data.
The samples are independent of each other.
The different samples are from populations that are categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test.
Example
A professor at a local University believes there is a relationship between head size and
the major of the students in her biostatistics classes. She takes a random sample of 20
students from each of three classes and records their major and head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
F
The test statistic follows the F distribution which has two degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a software program is
generally used for the calculations. We will be using Microsoft Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box appears.
Input range is where the data is in the table. Be sure to put a check in the box for labels
in.
1Running head RESEARCH PROJECT PROPOSAL 13RESEA.docx
1
Running head: RESEARCH PROJECT PROPOSAL
1
3
RESEARCH PROJECT PROPOSAL
Research Project Proposal
Name Here
South University Online
Research Project Proposal
Provide a brief, general introduction to the topic and importance to your role (do not use first person anywhere in the paper). Conclude with a thesis statement.
Background and Significance of the Problem
Discuss here.
Statement of the Problem and Purpose of the Study
Discuss here.
Literature Review
Discuss here.
Research Questions, Hypothesis, and Variables
Discuss here.
Theoretical Framework
Overview and Guiding Propositions
Discuss here.
Application of Theory to Study Focus
Discuss here.
Methodology
Sample/Setting
Discuss here.
Sampling Strategy
Discuss here.
Research Design
Discuss here.
Extraneous Variables
Discuss here.
Instruments
Discuss here.
Description of the Intervention
Discuss here.
Data Collection Procedures
Discuss here.
Data Analysis Plans
Discuss here.
Ethical Issues
Discuss here.
Limitations of Proposed Study
Discuss here.
Implications for Practice
Discuss here.
References
Appendices
Lecture 6
(Additional information on t-tests and hypothesis testing)
Lecture 5 focused on perhaps the most common of the t-tests, the two sample assuming
equal variance. There are other versions as well; Excel lists two others, the two sample assuming
unequal variance and the paired t-test. We will end with some comments about rejecting the null
hypothesis.
Choosing between the t-test options
As the names imply each of the three forms of the t-test deal with different types of data
sets. The simplest distinction is between the equal and unequal variance tests. Both require that
the data be at least interval in nature, come from a normally distributed population, and be
independent of each other – that is, collected from different subjects.
The F-test for variance.
To determine if the population variances of two groups are statistically equal – in order to
correctly choose the equal variance version of the t-test – we use the F statistic, which is
calculated by dividing one variance by the other variance. If the outcome is less than 1.0, the
rejection region is in the left tail; if the value is greater than 1.0, the rejection region is in the
right tail. In either case, Excel provides the information we need.
To perform a hypothesis test for variance equality we use Excel’s F-Test Two-Sample for
Variances found in the Data Analysis section under the Data tab. The test set-up is very similar
to that of the t-test, entering data ranges, checking Labels box if they are included in the data
ranges, and identifying the start of the output range. The only unique element in this test is the
identification of our alpha level.
Since we are testing for equality of variances, we have a two sample test and the rejection
region is again in both tails. This means that our rejection region in each tail is 0.25. The F-test
id ...
BUS 308 Week 2 Lecture 2 Statistical Testing for Differenc.docx
BUS 308 Week 2 Lecture 2
Statistical Testing for Differences – Part 1
After reading this lecture, the student should know:
1. How statistical distributions are used in hypothesis testing.
2. How to interpret the F test (both options) produced by Excel
3. How to interpret the T-test produced by Excel
Overview
Lecture 1 introduced the logic of statistical testing using the hypothesis testing procedure.
It also mentioned that we will be looking at two different tests this week. The t-test is used to
determine if means differ, from either a standard or claim or from another group. The F-test is
used to examine variance differences between groups.
This lecture starts by looking at statistical distributions – they underline the entire
statistical testing approach. They are kind of like the detective’s base belief that crimes are
committed for only a couple of reasons – money, vengeance, or love. The statistical distribution
that underlies each test assumes that statistical measures (such as the F value when comparing
variances and the t value when looking at means) follow a particular pattern, and this can be used
to make decisions.
While the underlying distributions differ for the different tests we will be looking at
throughout the course, they all have some basic similarities that allow us to examine the t
distribution and extrapolate from it to interpreting results based on other distributions.
Distributions. The basic logic for all statistical tests: If the null hypothesis claim is
correct, then the distribution of the statistical outcome will be distributed around a central value,
and larger and smaller values will be increasingly rare. At some point (and we define this as our
alpha value), we can say that the likelihood of getting a difference this large is extremely
unlikely and we will say that our results do not seem to come from a population that matches the
claims of the null hypothesis.
Note that this logic has several key elements:
1. The test is based on an assumption that the null hypothesis is correct. This gives us a
starting point, even if later proven wrong.
2. All sample results are turned into a statistic that matches the test selected (for
example, the F statistic when using the F-test, or the t-statistic when using the T-test.)
3. The calculated statistic is compared to a related statistical distribution to see how
likely an outcome we have.
4. The larger the test statistic, the more unlikely it is that the result matches or comes
from the population described by the null hypothesis claim.
We will demonstrate these ideas by looking at the questions being asked in this week’s
homework. We will show results of the related Excel tests, and discuss how to interpret the
output.
We need to remember that seeing different value (mean, variance, etc.) from different
samples does not tell us that the population parameters we are estimating are, in fact, different.
The ...
4DDBA 8307 Week 7 Assignment TemplateJohn DoeD.docx
4
DDBA 8307 Week 7 Assignment Template
John Doe
DDBA 8307-6
Dr. Jane Doe
1
Two-Way Contingency Table Analysis
Type text here. You will describe and defend using the two-way contingency table analysis. Use at least two outside resources—that is, resources not provided in the course resources, readings, etc. These citations will be presented in the References section. This exercise will give you practice for addressing Rubric Item 2.13b, which states, “Describes and defends, in detail, the statistical analyses that the student will conduct….” This section should be no more than two paragraphs.
Research Question
Type appropriate research question here?
Hypotheses
H0: Type appropriate null hypothesis here.
H1: Type appropriate alternative hypothesis here.
Results
Type introduction here.
Descriptive Statistics
Present the descriptive statistics here—use appropriate table and figures.
Inferential Results
Type the inferential results here.
2
References
Type references here in proper APA format.
Appendix – Two-Way Contingency Table Analysis
SPSS Output
BUS 308 Week 2 Lecture 2
Statistical Testing for Differences – Part 1
After reading this lecture, the student should know:
1. How statistical distributions are used in hypothesis testing.
2. How to interpret the F test (both options) produced by Excel
3. How to interpret the T-test produced by Excel
Overview
Lecture 1 introduced the logic of statistical testing using the hypothesis testing procedure.
It also mentioned that we will be looking at two different tests this week. The t-test is used to
determine if means differ, from either a standard or claim or from another group. The F-test is
used to examine variance differences between groups.
This lecture starts by looking at statistical distributions – they underline the entire
statistical testing approach. They are kind of like the detective’s base belief that crimes are
committed for only a couple of reasons – money, vengeance, or love. The statistical distribution
that underlies each test assumes that statistical measures (such as the F value when comparing
variances and the t value when looking at means) follow a particular pattern, and this can be used
to make decisions.
While the underlying distributions differ for the different tests we will be looking at
throughout the course, they all have some basic similarities that allow us to examine the t
distribution and extrapolate from it to interpreting results based on other distributions.
Distributions. The basic logic for all statistical tests: If the null hypothesis claim is
correct, then the distribution of the statistical outcome will be distributed around a central value,
and larger and smaller values will be increasingly rare. At some point (and we define this as our
alpha value), we can say that the likelihood of getting a difference this large is extremely
unlikely and we will say that our results do.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .
Business Email Rubric Subject Line Subject line clea.docx
Business Email Rubric
Subject Line
Subject line
clearly states the
main point of the
email
5points
Subject line is a
bit long (5+
words) or a bit
short (1 word) but
states the point of
the email
3points
Subject line does
not correspond to
the main point of
the email
2points
Subject line is
missing
0points
Greeting
Email includes a
professional
greeting that is
appropriate for the
audience; uses
the person's first
name
5points
Email includes a
professional
greeting that is
adequate for the
audience but uses
the person's first
and last name or
just the last name
3points
Email includes a
greeting but it is
not personalized
2points
Email lacks a
greeting
0points
Introductory
Comment
Email includes an
introductory
positive, relevant
comment
5points
it Email includes
an introductory,
positive comment
but may be very
general
3points
Email includes an
introduction only
2points
Email lacks an
introductory
comment
0points
Content
Purpose of the
email is clear, as
is the outcome;
content is succinct
and well organized
and does not
include
unnecessary
information
5points
Purpose of the
email is clear, as
is the outcome;
content could be
better organized
3points
Purpose of the
email is not clear
and/or content is
poorly organized;
it took more than
one reading to
understand what
the email is about
2points
Email seems to be
a collection of
unrelated
statements; it is
difficult to figure
out what the
purpose is
0points
Closing and
Signature
Email includes a
complementary
closing and
signature with all
required items
(name,
title/position,
company name,
phone number)
5points
Email includes a
complementary
closing and
signature but the
signature is
incomplete
3points
Email may be
missing either a
complementary
closing or
signature
2points
Email lacks a
closing and
signature
0points
Writing
Conventions
Email looks
professional and
does not have any
formatting or
writing errors
5points
Email is well
presented with
minimal (<3)
formatting or
writing errors
3points
Email includes
several (3+)
formatting or
writing errors
2points
Email is poorly
presented and has
an accumulation
of writing errors
that interfere with
readability
0points
BUS308 Week 3 Lecture 2
Examining Differences – ANOVA and Chi Square
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Conducting hypothesis tests with the ANVOA and Chi Square tests
2. How to interpret the Analysis of Variance test output
3. How to interpret Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
This week we introduced the ANOVA test for multiple mean equality and the Chi Square
tests for distrib ...
Data Analysis for Graduate Studies Summary
This document provides guidance on analysing qualitative and quantitative data. For qualitative data, it discusses preparing the data, identifying concepts and themes, and ensuring quality analysis. Key strategies for qualitative analysis include open coding, classification, and conceptual frameworks. For quantitative data, the document outlines recording, describing, and managing the data using techniques such as frequency counts, cross-tabulation, t-tests, chi-squared tests, and measures of central tendency and correlation. Examples are provided for coding, entering, and presenting both types of data.
Week 2 – Lecture 3 Making judgements about differences bet.docx
Week 2 – Lecture 3
Making judgements about differences between group statistics is one of the most
powerful things that statistics can do for us. It is also one of the most counter-intuitive things
that we need to master in the class.
Lecture 1 introduced the hypothesis testing procedure used in statistical testing. Lecture
2 examined how to set up, perform, and interpret the F test for variance equality. This lecture
will focus on t-tests for testing mean equality. Again, these examples will use the compa-ratio
variable, while the homework should use the Salary variable.
The T-Test
While we test for variance equality with an F test, we use the T-Test to test for mean
equality testing. The t-test also uses the degree of freedom (df) value in providing us with our
probability result; but again, Excel does the work for us.
There are three versions of the T-Test done for us by Excel. The first two are similar
except one version is done if the variances are equal and the other if the variances are not equal.
(Now we see the second reason for performing the F-test first.)
The third version of the T-test is for paired data, and is called T-test Paired Two Sample
for Means. Paired data are two measures taken on the same subject. Examples include a math
and English test score for students, preference for different drinks, and, in our data set the salary
and midpoint values. Note that paired data must be measured in the same units, and be from the
same subjects. Students in the past have incorrectly used the paired t-test on male and female
salaries. These are not paired, as the measures are taken on different people and cannot be paired
together for analysis.
In many ways, setting up Excel’s T-tests, and virtually all the functions we will study,
follow the same steps as we just went through:
1. Set up the data into distinct groups.
2. Select the test function from either the Fx or Analysis list
3. Input the data ranges and output ranges into the appropriate entry boxes, checking
Labels if appropriate.
4. Clicking on OK to produce the output.
As with the F-test, the T-test has a couple of options depending upon what you want your
output to look like. The Fx (or Formulas) option returns simply the p-value for the selected
version of the test. The Data | Analysis selection provides descriptive statistics that are useful for
additional analysis (some of which we will discuss later in the course).
The t-test requires that we select between three versions, one assuming equal variances
between the populations, one assuming unequal variances in the populations, and one requiring
paired data (two measures on each element in the sample, such as salary and midpoint for each
person in our data set.) All have the same data set-up approach, so only one will be shown.
Question 2
The second question for this week asks about salary mean equality between males and
females. The data and test se.
Technology-based assessments-special educationNew technologies r.docx
Technology-based assessments-special education
New technologies remain competitive in driving efforts to make learning more efficient. Technology-based assessment in special education has made quite some advancement (Goldsmith & LeBlanc, 2004). First applications of computer technology assessment were for the scoring student's test forms. Currently, features incorporate self-administration, software control in presentation, response evaluation based on algorithms, prescription based on expert knowledge and direct links in assessment and change in instructions. The technology-based assessment uses electronic and software systems to evaluate individual children in an educational setting. Traditional assessments employ approaches of the computer.
Video-based computer assisted test enabled learning of language for the student automatically increasing the validity of measurements. Video segments incorporated movie elements of moral dilemma in problem-solving tests. Students viewing the video segments respond by simply touching the screen. Innovative approaches have created relevance in testing procedures. Misplaced students result into poor results and get prompted to drop out. Teachers not well trained contribute to the misplacement due to poor management of certain behaviors and learning differences. For effect, teachers must be able to analyze data produced by the assessment and develop a due course of action.
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Holistic assessments are the best in technology-based assessments. They incorporate software control in presentation, conceptual models or algorithms, decision-making based rules and expert knowledge (Redecker, & Johannessen, 2013). Proliferation technology helps students in the inclusion of speech recognition, electronic communication, personal computers, robotics and artificial intelligence. Trends in technology-based assessments have impacted lives of students with a disability. They achieve school improvement goals as well as tracking student growth and progress. Current assessment norms have embedded current stan ...
Spss2 comparing means_two_groups
This document discusses different types of t-tests used to compare means: one sample t-tests, independent samples t-tests, and paired samples t-tests. It provides examples and steps for conducting each type of t-test in SPSS. Key points include that one sample t-tests compare a sample mean to a known value, independent samples t-tests compare means between two unrelated groups, and paired samples t-tests compare means within the same group across two time points or conditions. The document also outlines assumptions, how to interpret output and p-values, and how to report results for each t-test. Three cases are presented to demonstrate application of each t-test type.
Chi-square tests are great to show if distributions differ or i.docx
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
Ashford 4 - Week 3 - Discussion 1Your initial discussio.docx
Ashford 4: - Week 3 - Discussion 1
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
ANOVA
In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example?
Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario.
Ashford 4: - Week 3 - Discussion 2
Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated.
Effect Size
Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data?
Ashford 4: - Week 3 - Assignment
Problem Set Week Three
Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations.
All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet.
Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment.
See comments at the right of the data set.
ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Grade
8
23
1.000
23
32
90
9
1
5.8
0
F
A
The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)?
10
22
0.956
23
30
80
7
1
4.7
0
F
A
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11
23
1.000
23
41
100
19
1
4.8
0
F
A
14
24
1.043
23
32
90
12
1
6
0
F
A
The column labels in the table mean:
15
24
1.043
23
32
80
8
1
4.9
0
F
A
ID – Employee sample number
Salary – Salary in thousands
23
23
1.000
23
36
65
6
1
3.3
1
F
A
Age – Age in years
Performance Rating – Appraisal rating (Employee evaluation score)
26
24
1.043
23
22
95
2
1
6.2
1
F
A
Service – Years of service (rounded)
Gender: ...
Week 6 DQ1. What is your research questionIs there a differen.docx
Week 6 DQ
1. What is your research question?
Is there a difference between the math utility of a male and a female?
2. What is the null hypothesis for your question?
Hn There is no difference in the math utility between male and female.
Alternative hypotheses can also be created in the case the null hypothesis is proven incorrect. Two alternative hypotheses are:
Ha1 Feales have a higher math utility.
Ha2 Males have a higher math utility.
3. What research design would align with this question?
According to Frankfort-Nachmias and Leon-Guerrero (2015) a descriptive research design would be best for this type of study.
4. What comparison of means test was used to answer the question (be sure to defend the use of the test using the article you found in your search)?
The independent-samples T test was used to analyze the means for this data.
5. What dependent variable was used and how is it measured?
The dependent variable is the student’s math utility. It is measured from -3.51 to 1.31(University high school longitudinal study dataset. (2009).
6. What independent variable is used and how is it measured?
Either male (1) of female (2) (University high school longitudinal study dataset. (2009).
7. If you found significance, what is the strength of the effect?
The significance was 0.0000. This is much better than the standard of .05 significance as outlined by Frankfort-Nachmias and Leon-Guerrero (2015).
8. Identify your research question and explain your results for a lay audience, what is the answer to your research question?
My research question was “Is there a difference between the math utility of a male and a female?” Based on the analysis of the means (or average) through testing using the independent-samples T test there was no measurable difference between the math utility of male or females. This leads us to accept the null hypothesis of “There is no difference in the math utility between male and female” as true.
Group Statistics
T1 Student's sex
N
Mean
Std. Deviation
Std. Error Mean
T1 Scale of student's mathematics utility
Male
9453
.0140
1.01962
.01049
Female
9349
-.0481
.97291
.01006
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
T1 Scale of student's mathematics utility
Equal variances assumed
17.400
.000
4.276
18800
.000
.06216
.01454
.03367
.09066
Equal variances not assumed
4.277
18775.932
.000
.06216
.01453
.03367
.09065
University high school longitudinal study dataset. (2009).
References
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Thousand Oaks, CA: Sage Publications.
University high school longitudinal study dataset. (2009). Retrieved from class.waldenu.edu
The t Test for Related Samples
The t Test for Related Samples
Program Transcript
MAT.
MARKETING MANAGEMENT PHILOSOPHIESCHAPTER 1 - ASSIGNMENTQuest.docx
MARKETING MANAGEMENT PHILOSOPHIES
CHAPTER 1 - ASSIGNMENT
Question 1.
Considering the differences of the philosophies, in some cases slight differences, select a company (product or service) and describe the current philosophy they pose for the customer. Include in your comments the level of customer value delivered by the company’s actions.
In other words, measure the company’s interaction with their customers against the Market Concept Philosophy. Does the company operate under the Market Concept Philosophy or do they lean more toward one of the other Philosophies.
Be specific with your examples.
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variabl ...
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- 2. Tips for Data Entry in Excel 1. Row 1 of your Excel spreadsheet should contain only variable names. Do not extend names to row 2. 2. Each subsequent row (line) in the Excel spreadsheet should contain data for a single subject or observed entity. 3. Avoid blank rows — it will complicate your import and analysis. 4. If you have missing data in your data set, define a missing value code and place that code in any cell that contains missing data. 5. Always use date variables with four-digit year formats in Excel. 6. Use your data dictionary, making sure to include all of the variables you will need. 7. If you have the time or resources, enter your data twice (preferably using two different data entry people) and compare the two files.
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- 5. Excel File is now SPSS Ready
- 8. Normality – See it to Believe it
- 9. What if you don’t get a neat one? Don’t Panic. In fact true normality is a myth. Data showing moderate departure from normality can usually be used in parametric procedures In comparing means, for large sample size (>=40) invoke CLT as justification Central Limit Theorem states that sample means are approximately normal for large sample sizes even when the original populations are non-normal Besides, there are always non-parametric tests!
- 10. Two-Sample t-Test The Two-Sample (independent groups) t-Test to determine whether (the unknown means of) two populations are different from each other (based on independent samples from each population) Simple, we compare two separate populations – male and female, rural and urban, educational administrators and teachers Or we compare two groups (randomly divided) subjected to one of two treatments – experimental group and control group, ALM and non-ALM students Paired-Sample are before and after measurements on a single group – pre-test and post-test
- 11. Comparing Males and Females in terms of Job Satisfaction
- 12. Interpreting the interpretation The first part tells us which row to use If Sig. < 0.05 we use the bottom row Otherwise we use the top one
- 13. Interpreting the interpretation The two values we use are t and df If Sig. < 0.05 the difference is significant In this case it is not
- 14. What if you don’t get a significant difference? Don’t Panic. You have just discovered an interesting fact. You had not formulated the null hypothesis because you knew it will be rejected Just report the ‘reality’ as you have discovered, without fear of it being against ‘established’ truths/facts Narrative reporting – ‘A two sample Student’s t-test assuming equal variances was performed to test the hypothesis that mean scores in Job Satisfaction of men and women are equal. The scores of the two groups were not significantly different, t (148) = 1.647, p = 0.426 Alternately – ‘The mean score in Job Satisfaction of Males (M = 17.4, SD = 4.711, N = 82) was not significantly different from those of Females (M = 16.09, SD = 5.042, N = 68), t (148) = 1.647, p = 0.426’ Although, some of us will still prefer the table, interpretation, conclusion method of reporting
- 15. One-Way ANOVA
- 16. Interpreting the Output The first part of the output tells us whether the groups differ The p value (Sig.) is less than 0.05, hence they do Thus there is at least one mean that is different from the rest Which one(s), the post-hoc tests will tell
- 17. Interpreting the Output The pairs where p value (Sig.) is less than 0.05 are (1)Assistant Teachers (M = 4.9) and Adhyapak (9.52), and (2) Assistant Teachers (M = 4.9) and Samvida Shikshak Grade 2 (M = 10.68) It is worthwhile to mention that the Assistant Teachers on an average get a Salary of Rs. 40,000 plus perks. Average Salary of Adhyapak and Samvida Shikshak is significantly less. Yet their post is higher(!) and so is job expectancy So if Job Anxiety in the latter two types is more, it is not unanticipated No other groups differ in terms of Job Anxiety That leads to the question whether Job Anxiety is related to Salary? Perhaps some research will find out.
- 19. Interpreting Correlation This time SPSS has ‘flagged significant relationship’ (Oh! Thank You! Why don’t you do that every time?) But as usual they have given two tables, we can look at any one. The result is the same We can thus report – ‘An analysis using Pearson’s correlation coefficient indicated that there is a significant inverse relationship between job satisfaction and job anxiety of teachers, r (148) = – 0.47, p <0.01. The mean (SD) for Job Satisfaction was 16.41 (4.892) and for Job Anxiety was 7.37 (5.057)
- 20. Thank You FOR YOUR PATIENCE