16 USING LINEAR REGRESSION PREDICTING THE FUTURE
16: MEDIA LIBRARY
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WHAT YOU WILL LEARN IN THIS CHAPTER
· Understanding how prediction works and how it can be used in the social and behavioral sciences
· Understanding how and why linear regression works when predicting one variable on the basis of another
· Judging the accuracy of predictions
· Understanding how multiple regression works and why it is useful
INTRODUCTION TO LINEAR REGRESSION
You’ve seen it all over the news—concern about obesity and how it affects work and daily life. A set of researchers in Sweden was interested in looking at how well mobility disability and/or obesity predicted job strain and whether social support at work can modify this association. The study included more than 35,000 participants, and differences in job strain mean scores were estimated using linear regression, the exact focus of what we are discussing in this chapter. The results found that level of mobile disability did predict job strain and that social support at work significantly modified the association among job strain, mobile disability, and obesity.
Want to know more? Go to the library or go online …
Norrback, M., De Munter, J., Tynelius, P., Ahlstrom, G., & Rasmussen, F. (2016). The association of mobility disability, weight status and job strain: A cross-sectional study. Scandinavian Journal of Public Health, 44, 311–319.
WHAT IS PREDICTION ALL ABOUT?
Here’s the scoop. Not only can you compute the degree to which two variables are related to one another (by computing a correlation coefficient as we did in Chapter 5), but you can also use these correlations to predict the value of one variable based on the value of another. This is a very special case of how correlations can be used, and it is a very powerful tool for social and behavioral sciences researchers.
The basic idea is to use a set of previously collected data (such as data on variables X and Y), calculate how correlated these variables are with one another, and then use that correlation and the knowledge of X to predict Y. Sound difficult? It’s not really, especially once you see it illustrated.
For example, a researcher collects data on total high school grade point average (GPA) and first-year college GPA for 400 students in their freshman year at the state university. He computes the correlation between the two variables. Then, he uses the techniques you’ll learn about later in this chapter to take a new set of high school GPAs and (knowing the relationship between high school GPA and first-year college GPA from the previous set of students) predict what first-year GPA should be for a new student who is just starting out. Pretty nifty, huh?
Here’s another example. A group of kindergarten teachers is interested in finding out how well ex.
BUS 308 – Week 4 Lecture 2 Interpreting Relationships .docxcurwenmichaela
BUS 308 – Week 4 Lecture 2
Interpreting Relationships
Expected Outcomes
After reading this lecture, the student should be able to:
1. Interpret the strength of a correlation
2. Interpret a Correlation Table
3. Interpret a Linear Regression Equation
4. Interpret a Multiple Regression Equation
Overview
As in many detective stories, we will often find that when one thing changes, we see that
something else has changed as well. Moving to correlation and regression opens up new insights
into our data sets, but still lets us use what we have learned about Excel tools in setting up and
generating our results.
The correlation between events is mirrored in data analysis examinations with correlation
analysis. This week’s focus changes from detecting and evaluating differences to looking at
relationships. As students often comment, finding significant differences in gender-based
measures does not explain why these differences exist. Correlation, while not always explaining
why things happen gives data detectives great clues on what to examine more closely and helps
move us towards understanding why outcomes exist and what impacts them. If we see
correlations in the real world, we often will spend time examining what might underlie them;
finding out if they are spurious or causal.
Regression lets us use relationships between and among our variables to predict or
explain outcomes based upon inputs, factors we think might be related. In our quest to
understand what impacts the compa-ratio and salary outcomes we see, we have often been
frustrated due to being basically limited to examining only two variables at a time, when we felt
that we needed to include many other factors. Regression, particularly multiple regression, is the
tool that allows us to do this.
Linear Correlation
When two things seem to move in a somewhat predictable way, we say they are
correlated. This correlation could be direct or positive, both move in the same direction, or it
could be inverse or negative, where when one increases the other decreases. The Law of Supply
in economics is a common example of an inverse (or negative) correlation, where the more
supply we have of something, the less we typically can charge for it; the Law of Demand is an
example of a direct (or positive) correlation as the more demand exists for something, the more
we can charge for it. Height and weight in young children is another common example of a
direct correlation, as one increases so does the other measure.
Probably the most commonly used correlation is the Pearson Correlation Coefficient,
symbolized by r. It measures the strength of the association – the extent to which measures
change together – between interval or ratio level measures as well as the direction of the
relationship (inverse or direct). Several measures in our company data set could use the Pearson
Correlation to show relationships; salary and midpoint, salary and yea.
BUS 308 – Week 4 Lecture 2 Interpreting Relationships .docxjasoninnes20
BUS 308 – Week 4 Lecture 2
Interpreting Relationships
Expected Outcomes
After reading this lecture, the student should be able to:
1. Interpret the strength of a correlation
2. Interpret a Correlation Table
3. Interpret a Linear Regression Equation
4. Interpret a Multiple Regression Equation
Overview
As in many detective stories, we will often find that when one thing changes, we see that
something else has changed as well. Moving to correlation and regression opens up new insights
into our data sets, but still lets us use what we have learned about Excel tools in setting up and
generating our results.
The correlation between events is mirrored in data analysis examinations with correlation
analysis. This week’s focus changes from detecting and evaluating differences to looking at
relationships. As students often comment, finding significant differences in gender-based
measures does not explain why these differences exist. Correlation, while not always explaining
why things happen gives data detectives great clues on what to examine more closely and helps
move us towards understanding why outcomes exist and what impacts them. If we see
correlations in the real world, we often will spend time examining what might underlie them;
finding out if they are spurious or causal.
Regression lets us use relationships between and among our variables to predict or
explain outcomes based upon inputs, factors we think might be related. In our quest to
understand what impacts the compa-ratio and salary outcomes we see, we have often been
frustrated due to being basically limited to examining only two variables at a time, when we felt
that we needed to include many other factors. Regression, particularly multiple regression, is the
tool that allows us to do this.
Linear Correlation
When two things seem to move in a somewhat predictable way, we say they are
correlated. This correlation could be direct or positive, both move in the same direction, or it
could be inverse or negative, where when one increases the other decreases. The Law of Supply
in economics is a common example of an inverse (or negative) correlation, where the more
supply we have of something, the less we typically can charge for it; the Law of Demand is an
example of a direct (or positive) correlation as the more demand exists for something, the more
we can charge for it. Height and weight in young children is another common example of a
direct correlation, as one increases so does the other measure.
Probably the most commonly used correlation is the Pearson Correlation Coefficient,
symbolized by r. It measures the strength of the association – the extent to which measures
change together – between interval or ratio level measures as well as the direction of the
relationship (inverse or direct). Several measures in our company data set could use the Pearson
Correlation to show relationships; salary and midpoint, salary and yea ...
For this assignment, use the aschooltest.sav dataset.The dMerrileeDelvalle969
For this assignment, use the aschooltest.sav dataset.
The dataset consists of Reading, Writing, Math, Science, and Social Studies test scores for 200 students. Demographic data include gender, race, SES, school type, and program type.
Instructions:
Work with the aschooltest.sav datafile and respond to the following questions in a few sentences. Please submit your SPSS output either in your assignment or separately.
1. Identify an Independent and Dependent Variable (of your choice) and develop a hypothesis about what you expect to find. (
note: the IV is a grouping variable, which means it needs to have more than 2 categories and the DV is continuous)
2. Run Assumption tests for Normality and initial Homogeneity of Variance. What are your results?
3. Run the one-way ANOVA with the Levene test & Tukey post hoc test.
a. What are the results of the Levene test? What does this mean?
b. What are the results of the one-way ANOVA (use notation)? What does it mean?
c. Are post hoc tests necessary? If so, what are the results of those analyses?
4. How do your analyses address your hypotheses?
Is concentration of single parent families associated with reading scores?
Using the AECF state data, the regression below measures the effect of the state's percentage of single parent families on the percentage of 4th graders with below basic reading scores.
%belowbasicread = β0 + β1x%SPF + u
Stata Output
1) Please write out the regression equation using the coefficients in the table
2) Please provide an interpretation of the coefficient for SPF
3) How does the model fit?
4) What is the NULL hypothesis for a T test about a regression coefficient?
5) What is the ALTERNATE hypothesis for a T test about a regression coefficient?
6) Look at the p value for the coefficient SPF.
a) Report the p value
b) How many stars would it get if we used our standard convention?
* p ≤ .1 ** p ≤ .05 *** p ≤ .01
image1.png
Two-Variable (Bivariate) Regression
In the last unit, we covered scatterplots and correlation. Social scientists use these as descriptive tools for getting an idea about how our variables of interest are related. But these tools only get us so far. Regression analysis is the next step. Regression is by far the most used tool in social science research.
Simple regression analysis can tell us several things:
1. Regression can estimate the relationship between x and y in their
original units of measurement. To see why this is so useful, consider the example of infant mortality and median family income. Let’s say that a policymaker is interested in knowing how much of a change in median family income is needed to significantly reduce the infant mortality rate. Correlation cannot answer this question, but regression can.
2. Regression can tell us how well the independent variable (x) explains the dependent variable (y). The measure is called the
R square.
Simple Tw ...
BUS 308 – Week 4 Lecture 2 Interpreting Relationships .docxcurwenmichaela
BUS 308 – Week 4 Lecture 2
Interpreting Relationships
Expected Outcomes
After reading this lecture, the student should be able to:
1. Interpret the strength of a correlation
2. Interpret a Correlation Table
3. Interpret a Linear Regression Equation
4. Interpret a Multiple Regression Equation
Overview
As in many detective stories, we will often find that when one thing changes, we see that
something else has changed as well. Moving to correlation and regression opens up new insights
into our data sets, but still lets us use what we have learned about Excel tools in setting up and
generating our results.
The correlation between events is mirrored in data analysis examinations with correlation
analysis. This week’s focus changes from detecting and evaluating differences to looking at
relationships. As students often comment, finding significant differences in gender-based
measures does not explain why these differences exist. Correlation, while not always explaining
why things happen gives data detectives great clues on what to examine more closely and helps
move us towards understanding why outcomes exist and what impacts them. If we see
correlations in the real world, we often will spend time examining what might underlie them;
finding out if they are spurious or causal.
Regression lets us use relationships between and among our variables to predict or
explain outcomes based upon inputs, factors we think might be related. In our quest to
understand what impacts the compa-ratio and salary outcomes we see, we have often been
frustrated due to being basically limited to examining only two variables at a time, when we felt
that we needed to include many other factors. Regression, particularly multiple regression, is the
tool that allows us to do this.
Linear Correlation
When two things seem to move in a somewhat predictable way, we say they are
correlated. This correlation could be direct or positive, both move in the same direction, or it
could be inverse or negative, where when one increases the other decreases. The Law of Supply
in economics is a common example of an inverse (or negative) correlation, where the more
supply we have of something, the less we typically can charge for it; the Law of Demand is an
example of a direct (or positive) correlation as the more demand exists for something, the more
we can charge for it. Height and weight in young children is another common example of a
direct correlation, as one increases so does the other measure.
Probably the most commonly used correlation is the Pearson Correlation Coefficient,
symbolized by r. It measures the strength of the association – the extent to which measures
change together – between interval or ratio level measures as well as the direction of the
relationship (inverse or direct). Several measures in our company data set could use the Pearson
Correlation to show relationships; salary and midpoint, salary and yea.
BUS 308 – Week 4 Lecture 2 Interpreting Relationships .docxjasoninnes20
BUS 308 – Week 4 Lecture 2
Interpreting Relationships
Expected Outcomes
After reading this lecture, the student should be able to:
1. Interpret the strength of a correlation
2. Interpret a Correlation Table
3. Interpret a Linear Regression Equation
4. Interpret a Multiple Regression Equation
Overview
As in many detective stories, we will often find that when one thing changes, we see that
something else has changed as well. Moving to correlation and regression opens up new insights
into our data sets, but still lets us use what we have learned about Excel tools in setting up and
generating our results.
The correlation between events is mirrored in data analysis examinations with correlation
analysis. This week’s focus changes from detecting and evaluating differences to looking at
relationships. As students often comment, finding significant differences in gender-based
measures does not explain why these differences exist. Correlation, while not always explaining
why things happen gives data detectives great clues on what to examine more closely and helps
move us towards understanding why outcomes exist and what impacts them. If we see
correlations in the real world, we often will spend time examining what might underlie them;
finding out if they are spurious or causal.
Regression lets us use relationships between and among our variables to predict or
explain outcomes based upon inputs, factors we think might be related. In our quest to
understand what impacts the compa-ratio and salary outcomes we see, we have often been
frustrated due to being basically limited to examining only two variables at a time, when we felt
that we needed to include many other factors. Regression, particularly multiple regression, is the
tool that allows us to do this.
Linear Correlation
When two things seem to move in a somewhat predictable way, we say they are
correlated. This correlation could be direct or positive, both move in the same direction, or it
could be inverse or negative, where when one increases the other decreases. The Law of Supply
in economics is a common example of an inverse (or negative) correlation, where the more
supply we have of something, the less we typically can charge for it; the Law of Demand is an
example of a direct (or positive) correlation as the more demand exists for something, the more
we can charge for it. Height and weight in young children is another common example of a
direct correlation, as one increases so does the other measure.
Probably the most commonly used correlation is the Pearson Correlation Coefficient,
symbolized by r. It measures the strength of the association – the extent to which measures
change together – between interval or ratio level measures as well as the direction of the
relationship (inverse or direct). Several measures in our company data set could use the Pearson
Correlation to show relationships; salary and midpoint, salary and yea ...
For this assignment, use the aschooltest.sav dataset.The dMerrileeDelvalle969
For this assignment, use the aschooltest.sav dataset.
The dataset consists of Reading, Writing, Math, Science, and Social Studies test scores for 200 students. Demographic data include gender, race, SES, school type, and program type.
Instructions:
Work with the aschooltest.sav datafile and respond to the following questions in a few sentences. Please submit your SPSS output either in your assignment or separately.
1. Identify an Independent and Dependent Variable (of your choice) and develop a hypothesis about what you expect to find. (
note: the IV is a grouping variable, which means it needs to have more than 2 categories and the DV is continuous)
2. Run Assumption tests for Normality and initial Homogeneity of Variance. What are your results?
3. Run the one-way ANOVA with the Levene test & Tukey post hoc test.
a. What are the results of the Levene test? What does this mean?
b. What are the results of the one-way ANOVA (use notation)? What does it mean?
c. Are post hoc tests necessary? If so, what are the results of those analyses?
4. How do your analyses address your hypotheses?
Is concentration of single parent families associated with reading scores?
Using the AECF state data, the regression below measures the effect of the state's percentage of single parent families on the percentage of 4th graders with below basic reading scores.
%belowbasicread = β0 + β1x%SPF + u
Stata Output
1) Please write out the regression equation using the coefficients in the table
2) Please provide an interpretation of the coefficient for SPF
3) How does the model fit?
4) What is the NULL hypothesis for a T test about a regression coefficient?
5) What is the ALTERNATE hypothesis for a T test about a regression coefficient?
6) Look at the p value for the coefficient SPF.
a) Report the p value
b) How many stars would it get if we used our standard convention?
* p ≤ .1 ** p ≤ .05 *** p ≤ .01
image1.png
Two-Variable (Bivariate) Regression
In the last unit, we covered scatterplots and correlation. Social scientists use these as descriptive tools for getting an idea about how our variables of interest are related. But these tools only get us so far. Regression analysis is the next step. Regression is by far the most used tool in social science research.
Simple regression analysis can tell us several things:
1. Regression can estimate the relationship between x and y in their
original units of measurement. To see why this is so useful, consider the example of infant mortality and median family income. Let’s say that a policymaker is interested in knowing how much of a change in median family income is needed to significantly reduce the infant mortality rate. Correlation cannot answer this question, but regression can.
2. Regression can tell us how well the independent variable (x) explains the dependent variable (y). The measure is called the
R square.
Simple Tw ...
Exercise 29Calculating Simple Linear RegressionSimple linear reg.docxAlleneMcclendon878
Exercise 29
Calculating Simple Linear Regression
Simple linear regression
is a procedure that provides an estimate of the value of a dependent variable (outcome) based on the value of an independent variable (predictor). Knowing that estimate with some degree of accuracy, we can use regression analysis to predict the value of one variable if we know the value of the other variable (
Cohen & Cohen, 1983
). The regression equation is a mathematical expression of the influence that a predictor has on a dependent variable, based on some theoretical framework. For example, in
Exercise 14
,
Figure 14-1
illustrates the linear relationship between gestational age and birth weight. As shown in the scatterplot, there is a strong positive relationship between the two variables. Advanced gestational ages predict higher birth weights.
A regression equation can be generated with a data set containing subjects'
x
and
y
values. Once this equation is generated, it can be used to predict future subjects'
y
values, given only their
x
values. In simple or bivariate regression, predictions are made in cases with two variables. The score on variable
y
(dependent variable, or outcome) is predicted from the same subject's known score on variable
x
(independent variable, or predictor).
Research Designs Appropriate for Simple Linear Regression
Research designs that may utilize simple linear regression include any associational design (
Gliner et al., 2009
). The variables involved in the design are attributional, meaning the variables are characteristics of the participant, such as health status, blood pressure, gender, diagnosis, or ethnicity. Regardless of the nature of variables, the dependent variable submitted to simple linear regression must be measured as continuous, at the interval or ratio level.
Statistical Formula and Assumptions
Use of simple linear regression involves the following assumptions (
Zar, 2010
):
1.
Normal distribution of the dependent (
y
) variable
2.
Linear relationship between
x
and
y
3.
Independent observations
4.
No (or little) multicollinearity
5.
Homoscedasticity
320
Data that are
homoscedastic
are evenly dispersed both above and below the regression line, which indicates a linear relationship on a scatterplot. Homoscedasticity reflects equal variance of both variables. In other words, for every value of
x
, the distribution of
y
values should have equal variability. If the data for the predictor and dependent variable are not homoscedastic, inferences made during significance testing could be invalid (
Cohen & Cohen, 1983
;
Zar, 2010
). Visual examples of homoscedasticity and heteroscedasticity are presented in
Exercise 30
.
In simple linear regression, the dependent variable is continuous, and the predictor can be any scale of measurement; however, if the predictor is nominal, it must be correctly coded. Once the data are ready, the parameters
a
and
b
are computed to obtain a regression equatio.
The future is uncertain. Some events do have a very small probabil.docxoreo10
The future is uncertain. Some events do have a very small probability of happening, like an asteroid destroying the earth. So we accept that tomorrow will come as a certain event. But future demand for a business’s goods and services is very uncertain. Yet, the management of a company wants to have some idea of the survival (or growth) of the company in the future. Should they expect to hire more people or let some go? Should they plan to increase capacity? How much investment is needed for future assets, or should they down size?
Forecasting provides some ideas about the future, but how this is accomplished can vary from company to company. And one key factor is how accurate the forecast is. Generally, the further into the future one looks, the more uncertain the information is. How do forecasters reduce their forecasting errors? How much error is tolerable?
Another key factor in forecasting is data availability. Data processing and storage capability have become extremely available and inexpensive. Software and computing power is also very cheap. Collecting real-time sales data via point-of-sales systems is now common at most retail establishments. But couple this with a situation in companies that have a large number of products, such as a retail store or a large manufacturing company with hundreds or thousands of product numbers and/or product lines, forecasting becomes complicated.
Forecasting Methods
There are two main types or genres of forecasting methods, qualitative and quantitative. The former consists of judgment and analysis of qualitative factors, such as scenario building and scenario analysis. The latter is obviously based on numerical analysis. This genre of forecasting includes such methods as linear regression, time series analysis, and data mining algorithms like CHAID and CART, which are useful especially in the growing world of artificial intelligence and machine learning in business. This module will look at the linear regression and time series analysis using exponential smoothing.
Linear Growth
When using any mathematical model, we have to consider which inputs are reasonable to use. Whenever we extrapolate, or make predictions into the future, we are assuming the model will continue to be valid. There are different types of mathematical model, one of which is linear growth model or algebraic growth model and another is exponential growth model, or geometric growth model. The constant change is the defining characteristic of linear growth. Plotting the values, we can see the values form a straight line, the shape of linear growth.
If a quantity starts at size P0 and grows by d every time period, then the quantity after n time periods can be determined using either of these relations:
Recursive form:
Pn = Pn-1 + d
Explicit form:
Pn = P0 + d n
In this equation, d represents the common difference – the amount that the population changes each time n increases by 1. Calculating values using the explicit form and plot ...
This project was a part of our coursework - Applied Regression Analysis.
In this project, our aim was to find the relationship between One Independent and Four dependent variable.
To understand how the followers are increases on twitter, so we took No of followers as our Independent variable and Years Since they joined, Number of years passed since that person has joined, Number of Photos and Videos posted and Number of People that person is following back as our dependent variable and performed Multiple linear regression analysis.
Estimating Models Using Dummy VariablesYou have had plenty of op.docxSANSKAR20
Estimating Models Using Dummy Variables
You have had plenty of opportunity to interpret coefficients for metric variables in regression models. Using and interpreting categorical variables takes just a little bit of extra practice. In this Discussion, you will have the opportunity to practice how to recode categorical variables so they can be used in a regression model and how to properly interpret the coefficients. Additionally, you will gain some practice in running diagnostics and identifying any potential problems with the model.
To prepare for this Discussion:
Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.
Create a research question using the General Social Survey dataset that can be answered by multiple regression. Using the SPSS software, choose a categorical variable to dummy code as one of your predictor variables.
Estimate a multiple regression model that answers your research question. Post your response to the following:
What is your research question?
Interpret the coefficients for the model, specifically commenting on the dummy variable.
Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment on what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
Regression Diagnostics and Model Evaluation
Regression Diagnostics and Model Evaluation
Program Transcript
[MUSIC PLAYING]
MATT JONES: We've gone over estimating bivariate and multiple regression
models, but one thing we haven't talked about up to this point are some of the
assumptions of multiple regression models. It's very important to adhere to these
assumptions to have proper interpretation of our models. These assumptions
include linearity, independence of error, homoscedasticity, multicollinearity,
undue influence, and normal distribution of errors. Let's go back to SPSS to see
how we can test these assumptions and evaluate our models.
Let's go ahead and estimate a multiple regression model using respondent's
socioeconomic status index is the dependent variable, respondent's highest
education as an independent variable, and occupational prestige score as an
independent variable. But this time, let's request some additional information to
perform some diagnostics around our model.
Go to analyze, regression, and linear, since we are still using an ordinary least
squares method. We'll scroll down and enter my dependent variable first,
respondent socioeconomic index. My independent variables of occupational
prestige and highest year of school completed. I want to go over to statistics and
request some additional information. I will request collinearity ...
Requirements.docxRequirementsFont Times New RomanI NEED .docxheunice
Requirements.docx
Requirements:
Font: Times New Roman
I NEED 7 APA Style reference and In-text citation
Spacing: SINGLE
All the number of words are included next to the questions.
__________________________________________________________________________________
BSBLDR511 - Develop and use emotional intelligence
Questions:
1. Explain emotional intelligence principles and strategies (100 words)
2. Describe the relationship between emotionally effective people and the attainment of business objectives (100 words)
3. Explain how to communicate with a diverse workforce which has varying cultural expressions of emotion (100 words)
4. List at least five (5) examples of emotional strengths and weaknesses. Explain all. (100 words)
5. Identify at least three (3) examples of emotional states you might identify in co-workers in the workplace, and outline the common cues for each. (100 words)
6. Why is it essential to consider varying cultural expressions of emotions when working and responding to emotional cues in a diverse workforce? (100 words)
7. There are a variety of opportunities you may provide in your workplace for others to express their thoughts and feelings. List two (2). ( 100 words)
8. Why is it important to assist others to understand the effect of their behavior and emotions on others in the workplace? ( 100 words)
9. What information will you need to consider to ensure you use the strengths of workgroup members to achieve workplace outcomes? (100 words)
Quiz 8 Notes
Scatterplots, Correlation and Regression
We are turning to our last quiz topic; regression. To get to regression, we need to understand several concepts first.
To start with, we will be working with two quantitative variables. The goal is to see if there is a relationship/association between the two variables. As one variable increases, what does the second variable do? If the second variable makes a consistent change then a relationship may exist. MAJOR POINT: saying a relationship exists does NOT mean there is Causation. The greatest abuse of statistical work is here, when a person runs a regression then says Variable A causes Variable B to change. You must have experimental results to establish causation.
Looking at the two variables that will be in a regression you need to know that each variable plays a specific role. One of the variables, X, will be the independent/explanatory variable and the other, Y, will be the dependent/ response variable. In a regression we are looking to see if changes in, Y; occur as X changes. It is very important that you establish at the beginning which of your variables will be X and which will be Y. Swapping the places for the two variables may not work. Let’s do an example.
In economics, we discuss the relationship of the quantity demand and the price of a good. Which one would be the X in a regression, and which would be, Y? The Law of Demand says, “as the price of a good increases, the quantity demanded decreases”. Which is allow.
3 pagesAfter reading the Cybersecurity Act of 2015, address .docxnovabroom
3 pages
After reading the
Cybersecurity Act of 2015
, address the private/public partnership with the DHS National Cybersecurity and Communications Integration Center (NCCIC), arguably the most important aspect of the act. The Cybersecurity Act of 2015 allows for private and public sharing of cybersecurity threat information.
What should the DHS NCCIC (public) share with private sector organizations? What type of threat information would enable private organizations to better secure their networks?
On the flip side, what should private organizations share with the NCCIC? As it is written, private organization sharing is completely voluntary. Should this be mandatory? If so, what are the implications to the customers' private data?
The government is not allowed to collect data on citizens. How should the act be updated to make it better and more value-added for the public-private partnership in regards to cybersecurity?
.
3 pages, 4 sourcesPaper detailsNeed a full retirement plan p.docxnovabroom
3 pages, 4 sources
Paper details
Need a full retirement plan proposal in excel with cited sources.
My career objective would be to start out of school as an associate accountant, then advance to a Director of Finance until I get promoted as CFO working in the healthcare industry in Las Vegas
.
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Exercise 29Calculating Simple Linear RegressionSimple linear reg.docxAlleneMcclendon878
Exercise 29
Calculating Simple Linear Regression
Simple linear regression
is a procedure that provides an estimate of the value of a dependent variable (outcome) based on the value of an independent variable (predictor). Knowing that estimate with some degree of accuracy, we can use regression analysis to predict the value of one variable if we know the value of the other variable (
Cohen & Cohen, 1983
). The regression equation is a mathematical expression of the influence that a predictor has on a dependent variable, based on some theoretical framework. For example, in
Exercise 14
,
Figure 14-1
illustrates the linear relationship between gestational age and birth weight. As shown in the scatterplot, there is a strong positive relationship between the two variables. Advanced gestational ages predict higher birth weights.
A regression equation can be generated with a data set containing subjects'
x
and
y
values. Once this equation is generated, it can be used to predict future subjects'
y
values, given only their
x
values. In simple or bivariate regression, predictions are made in cases with two variables. The score on variable
y
(dependent variable, or outcome) is predicted from the same subject's known score on variable
x
(independent variable, or predictor).
Research Designs Appropriate for Simple Linear Regression
Research designs that may utilize simple linear regression include any associational design (
Gliner et al., 2009
). The variables involved in the design are attributional, meaning the variables are characteristics of the participant, such as health status, blood pressure, gender, diagnosis, or ethnicity. Regardless of the nature of variables, the dependent variable submitted to simple linear regression must be measured as continuous, at the interval or ratio level.
Statistical Formula and Assumptions
Use of simple linear regression involves the following assumptions (
Zar, 2010
):
1.
Normal distribution of the dependent (
y
) variable
2.
Linear relationship between
x
and
y
3.
Independent observations
4.
No (or little) multicollinearity
5.
Homoscedasticity
320
Data that are
homoscedastic
are evenly dispersed both above and below the regression line, which indicates a linear relationship on a scatterplot. Homoscedasticity reflects equal variance of both variables. In other words, for every value of
x
, the distribution of
y
values should have equal variability. If the data for the predictor and dependent variable are not homoscedastic, inferences made during significance testing could be invalid (
Cohen & Cohen, 1983
;
Zar, 2010
). Visual examples of homoscedasticity and heteroscedasticity are presented in
Exercise 30
.
In simple linear regression, the dependent variable is continuous, and the predictor can be any scale of measurement; however, if the predictor is nominal, it must be correctly coded. Once the data are ready, the parameters
a
and
b
are computed to obtain a regression equatio.
The future is uncertain. Some events do have a very small probabil.docxoreo10
The future is uncertain. Some events do have a very small probability of happening, like an asteroid destroying the earth. So we accept that tomorrow will come as a certain event. But future demand for a business’s goods and services is very uncertain. Yet, the management of a company wants to have some idea of the survival (or growth) of the company in the future. Should they expect to hire more people or let some go? Should they plan to increase capacity? How much investment is needed for future assets, or should they down size?
Forecasting provides some ideas about the future, but how this is accomplished can vary from company to company. And one key factor is how accurate the forecast is. Generally, the further into the future one looks, the more uncertain the information is. How do forecasters reduce their forecasting errors? How much error is tolerable?
Another key factor in forecasting is data availability. Data processing and storage capability have become extremely available and inexpensive. Software and computing power is also very cheap. Collecting real-time sales data via point-of-sales systems is now common at most retail establishments. But couple this with a situation in companies that have a large number of products, such as a retail store or a large manufacturing company with hundreds or thousands of product numbers and/or product lines, forecasting becomes complicated.
Forecasting Methods
There are two main types or genres of forecasting methods, qualitative and quantitative. The former consists of judgment and analysis of qualitative factors, such as scenario building and scenario analysis. The latter is obviously based on numerical analysis. This genre of forecasting includes such methods as linear regression, time series analysis, and data mining algorithms like CHAID and CART, which are useful especially in the growing world of artificial intelligence and machine learning in business. This module will look at the linear regression and time series analysis using exponential smoothing.
Linear Growth
When using any mathematical model, we have to consider which inputs are reasonable to use. Whenever we extrapolate, or make predictions into the future, we are assuming the model will continue to be valid. There are different types of mathematical model, one of which is linear growth model or algebraic growth model and another is exponential growth model, or geometric growth model. The constant change is the defining characteristic of linear growth. Plotting the values, we can see the values form a straight line, the shape of linear growth.
If a quantity starts at size P0 and grows by d every time period, then the quantity after n time periods can be determined using either of these relations:
Recursive form:
Pn = Pn-1 + d
Explicit form:
Pn = P0 + d n
In this equation, d represents the common difference – the amount that the population changes each time n increases by 1. Calculating values using the explicit form and plot ...
This project was a part of our coursework - Applied Regression Analysis.
In this project, our aim was to find the relationship between One Independent and Four dependent variable.
To understand how the followers are increases on twitter, so we took No of followers as our Independent variable and Years Since they joined, Number of years passed since that person has joined, Number of Photos and Videos posted and Number of People that person is following back as our dependent variable and performed Multiple linear regression analysis.
Estimating Models Using Dummy VariablesYou have had plenty of op.docxSANSKAR20
Estimating Models Using Dummy Variables
You have had plenty of opportunity to interpret coefficients for metric variables in regression models. Using and interpreting categorical variables takes just a little bit of extra practice. In this Discussion, you will have the opportunity to practice how to recode categorical variables so they can be used in a regression model and how to properly interpret the coefficients. Additionally, you will gain some practice in running diagnostics and identifying any potential problems with the model.
To prepare for this Discussion:
Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables.
Create a research question using the General Social Survey dataset that can be answered by multiple regression. Using the SPSS software, choose a categorical variable to dummy code as one of your predictor variables.
Estimate a multiple regression model that answers your research question. Post your response to the following:
What is your research question?
Interpret the coefficients for the model, specifically commenting on the dummy variable.
Run diagnostics for the regression model. Does the model meet all of the assumptions? Be sure and comment on what assumptions were not met and the possible implications. Is there any possible remedy for one the assumption violations?
Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.
Regression Diagnostics and Model Evaluation
Regression Diagnostics and Model Evaluation
Program Transcript
[MUSIC PLAYING]
MATT JONES: We've gone over estimating bivariate and multiple regression
models, but one thing we haven't talked about up to this point are some of the
assumptions of multiple regression models. It's very important to adhere to these
assumptions to have proper interpretation of our models. These assumptions
include linearity, independence of error, homoscedasticity, multicollinearity,
undue influence, and normal distribution of errors. Let's go back to SPSS to see
how we can test these assumptions and evaluate our models.
Let's go ahead and estimate a multiple regression model using respondent's
socioeconomic status index is the dependent variable, respondent's highest
education as an independent variable, and occupational prestige score as an
independent variable. But this time, let's request some additional information to
perform some diagnostics around our model.
Go to analyze, regression, and linear, since we are still using an ordinary least
squares method. We'll scroll down and enter my dependent variable first,
respondent socioeconomic index. My independent variables of occupational
prestige and highest year of school completed. I want to go over to statistics and
request some additional information. I will request collinearity ...
Requirements.docxRequirementsFont Times New RomanI NEED .docxheunice
Requirements.docx
Requirements:
Font: Times New Roman
I NEED 7 APA Style reference and In-text citation
Spacing: SINGLE
All the number of words are included next to the questions.
__________________________________________________________________________________
BSBLDR511 - Develop and use emotional intelligence
Questions:
1. Explain emotional intelligence principles and strategies (100 words)
2. Describe the relationship between emotionally effective people and the attainment of business objectives (100 words)
3. Explain how to communicate with a diverse workforce which has varying cultural expressions of emotion (100 words)
4. List at least five (5) examples of emotional strengths and weaknesses. Explain all. (100 words)
5. Identify at least three (3) examples of emotional states you might identify in co-workers in the workplace, and outline the common cues for each. (100 words)
6. Why is it essential to consider varying cultural expressions of emotions when working and responding to emotional cues in a diverse workforce? (100 words)
7. There are a variety of opportunities you may provide in your workplace for others to express their thoughts and feelings. List two (2). ( 100 words)
8. Why is it important to assist others to understand the effect of their behavior and emotions on others in the workplace? ( 100 words)
9. What information will you need to consider to ensure you use the strengths of workgroup members to achieve workplace outcomes? (100 words)
Quiz 8 Notes
Scatterplots, Correlation and Regression
We are turning to our last quiz topic; regression. To get to regression, we need to understand several concepts first.
To start with, we will be working with two quantitative variables. The goal is to see if there is a relationship/association between the two variables. As one variable increases, what does the second variable do? If the second variable makes a consistent change then a relationship may exist. MAJOR POINT: saying a relationship exists does NOT mean there is Causation. The greatest abuse of statistical work is here, when a person runs a regression then says Variable A causes Variable B to change. You must have experimental results to establish causation.
Looking at the two variables that will be in a regression you need to know that each variable plays a specific role. One of the variables, X, will be the independent/explanatory variable and the other, Y, will be the dependent/ response variable. In a regression we are looking to see if changes in, Y; occur as X changes. It is very important that you establish at the beginning which of your variables will be X and which will be Y. Swapping the places for the two variables may not work. Let’s do an example.
In economics, we discuss the relationship of the quantity demand and the price of a good. Which one would be the X in a regression, and which would be, Y? The Law of Demand says, “as the price of a good increases, the quantity demanded decreases”. Which is allow.
3 pagesAfter reading the Cybersecurity Act of 2015, address .docxnovabroom
3 pages
After reading the
Cybersecurity Act of 2015
, address the private/public partnership with the DHS National Cybersecurity and Communications Integration Center (NCCIC), arguably the most important aspect of the act. The Cybersecurity Act of 2015 allows for private and public sharing of cybersecurity threat information.
What should the DHS NCCIC (public) share with private sector organizations? What type of threat information would enable private organizations to better secure their networks?
On the flip side, what should private organizations share with the NCCIC? As it is written, private organization sharing is completely voluntary. Should this be mandatory? If so, what are the implications to the customers' private data?
The government is not allowed to collect data on citizens. How should the act be updated to make it better and more value-added for the public-private partnership in regards to cybersecurity?
.
3 pages, 4 sourcesPaper detailsNeed a full retirement plan p.docxnovabroom
3 pages, 4 sources
Paper details
Need a full retirement plan proposal in excel with cited sources.
My career objective would be to start out of school as an associate accountant, then advance to a Director of Finance until I get promoted as CFO working in the healthcare industry in Las Vegas
.
3 pagesThis paper should describe, as well as compare and contra.docxnovabroom
3 pages
This paper should describe, as well as compare and contrast, Diffie Hellman and Kerberos. You should include data flow diagrams that outline the transaction of both kerberos and Diffie Hellman - one diagram each please using Microsoft Visio or Dia (free open source tool). These diagrams are NOT part of the page total required for this assignment.
single spacing
, normal margins, use 12 pt font - reference what isn't yours please
.
3 assignments listed below1. In a 350 word essay, compare a.docxnovabroom
3 assignments listed below
1.
In a 350 word essay, compare and contrast the healthcare system of the United States with the WHO’s Millennium Development Goals. Be sure that you are providing the significant components of the US system as well as the WHO'S Millennium Development Goals.
The essay must be submitted using 12 point times new roman font double spaced in APA format. You must have at least one reference on a separate reference page. The assignment must be submitted in APA format; you do not need an abstract.
2.
Children have always contributed to the total number of migrants crossing the southern border of the United States illegally, but in 2014, a steady overall increase in unaccompanied minors from Central America reached crisis proportions when tens of thousands of children from El Salvador, Guatemala, and Honduras crossed the Rio Grande and overwhelmed border patrols and local infrastructure (Dart 2014).
Since legislators passed the William Wilberforce Trafficking Victims Protection Reauthorization Act of 2008 in the last days of the Bush administration, unaccompanied minors from countries that do not share a border with the United States are guaranteed a hearing with an immigration judge where they may request asylum based on a “credible” fear of persecution or torture (U.S. Congress 2008). In some cases, these children are looking for relatives and can be placed with family while awaiting a hearing on their immigration status; in other cases, they are held in processing centers until the Department of Health and Human Services makes other arrangements (Popescu 2014).
The 2014 surge placed such a strain on state resources that Texas began transferring the children to Immigration and Naturalization facilities in California and elsewhere, without incident for the most part. On July 1, 2014, however, buses carrying the migrant children were blocked by protesters in Murrietta, California, who chanted, "Go home" and "We don’t want you.” (Fox News and Associated Press 2014; Reyes 2014).
A functional perspective theorist might focus on the dysfunctions caused by the sudden influx of underage asylum seekers, while a conflict perspective theorist might look at the way social stratification influences how the members of a developed country are treating the lower-status migrants from less-developed countries in Latin America. An interactionist theorist might see the significance in the attitude of the Murrietta protesters toward the migrant children.
Respond to the following questions in a 350-word essay using 12 point times new roman font double spaced: Given the fact that these children are fleeing various kinds of violence and extreme poverty, how should the U.S. government respond? Should the government pass laws granting a general amnesty? Or should it follow a zero-tolerance policy, automatically returning any and all unaccompanied minor migrants to their countries of origin so as to discourage additional immigration tha.
/
3 Communication Challenges in a Diverse, Global Marketplace
LEARNING OBJECTIVES
After studying this chapter, you will be able to
1 (http://content.thuzelearning.com/books/Bovee.7626.18.1/sections/p7001012451000000000000000001b6f#P7001012451000000000000000001B75)
Discuss the opportunities and challenges of intercultural communication.
2 (http://content.thuzelearning.com/books/Bovee.7626.18.1/sections/p7001012451000000000000000001bb4#P7001012451000000000000000001BBA)
De�ine culture, explain how culture is learned, and de�ine ethnocentrism and stereotyping.
3 (http://content.thuzelearning.com/books/Bovee.7626.18.1/sections/p7001012451000000000000000001b�b#P7001012451000000000000000001BFF)
Explain the importance of recognizing cultural variations, and list eight categories of cultural differences.
4 (http://content.thuzelearning.com/books/Bovee.7626.18.1/sections/p7001012451000000000000000001c9b#P7001012451000000000000000001CA0) List
four general guidelines for adapting to any business culture.
5 (http://content.thuzelearning.com/books/Bovee.7626.18.1/sections/p7001012451000000000000000001cc6#P7001012451000000000000000001CCA)
Identify seven steps you can take to improve your intercultural communication skills.
MyBCommLab®
Improve Your Grade!
More than 10 million students improved their results using Pearson MyLabs. Visit mybcommlab.com (http://mybcommlab.com) for simulations, tutorials, and
end-ofchapter problems.
COMMUNICATION CLOSE-UP AT
Kaiser Permanente
kp.org (http://kp.org)
Delivering quality health care is dif�icult enough, given the complexities of technology, government regulations, evolving scienti�ic and medical understanding, and
the variability of human performance. It gets even more daunting when you add the challenges of communication among medical staff and between patients and
their caregivers, which often takes place under stressful circumstances. Those communication efforts are challenging enough in an environment where everyone
speaks the same language and feels at home in a single cultural context—but they’re in�initely more complex in the United States, whose residents identify with
dozens of different cultures and speak several hundred languages.
The Oakland-based health-care system Kaiser Permanente has been embracing the challenges and opportunities of diversity since its founding in 1945. It made a
strong statement with its very �irst hospital when it refused to follow the then-common practice of segregating patients by race. Now, as the largest not-for-pro�it
health system in the United States, Kaiser’s client base includes more than 10 million members from over 100 distinct cultures.
At the core of Kaiser’s approach is culturally competent care, which it de�ines as “health care that acknowledges cultural diversity in the clinical setting, respects
members’ beliefs and practices, and ensures that cultural needs are considered and respected at every point of contact.” These priorities.
2Women with a Parasol-Madame Monet and Her SonClau.docxnovabroom
2
Women with a Parasol-Madame Monet and Her Son
Claud Monet (1840-1926)
1875
Oil on Canvas
100 x 81 cm
119.4 x 99.7 cm
Image from National Gallery of Art.
Working thesis statement
- “Woman with a Parasol” is also called “The Stroll”. Painted 1875 (art, n.d.) in France Argenteuil; The character in the paint are Monet’s wife Camille Monet and his 7-year-old son.
- This paint was finished within a day; he was using the fast-visible brushstrokes to create this work. This work witnessed that Monet got away from the Academy style. (Gallery, n.d.) The theme of the paint is one of kind. (Proving the impressionism)
- “Woman with a Parasol” was exhibited in second impressionist exhibition, 1876. (Art)
- The theme and environment in the paint earned many claps and praises. The whole image provides people with a feeling of freedom and kind. (Art, nga.gov, n.d.)
The controversy parts.
· How much contribution that this paint did to the modern art world.
· The affections about the theme in this paint.
· The viewer nowadays is judging the art value of this paint.
Those controversy parts about the paint were making a progress in modern art and improve the development of art.
Bibliography:
1. “Woman with a Parasol - Madame Monet and Her Son.” Modern Painters 29, no. 1 (March 2017): 45. https://search.ebscohost.com/login.aspx?direct=true&db=edb&AN=121204182&site=eds-live.
2. Goldwater, Robert. "The Glory that was France." Art News 65 (March 1966):42, repro. cover. 1966
3. Hand, John Oliver. National Gallery of Art: Master Paintings from the Collection. Washington and New York, 2004: 382-383, no. 317, color repro. 2004
4. C. Monet Gallery “Woman with a Parasol”. https://www.cmonetgallery.com/woman-with-a-parasol.aspx
5. Woman with a Parasol, 1875 by Claude Monet, Claude Monet Paintings, biography, and Quotes. https://www.claude-monet.com/woman-with-a-parasol.jsp#prettyPhoto
6. Eelco Kappe. “Woman with a Parasol - Madame Monet and Her Son by Claude onet.” TripImprover, (2019/10/16) https://www.tripimprover.com/blog/woman-with-a-parasol-madame-monet-and-her-son-by-claude-monet#comments
7. Google Art and Culture, National Gallery of Art, Washington DC. https://artsandculture.google.com/asset/woman-with-a-parasol-madame-monet-and-her-son/EwHxeymQQnprMg
8. Charles Saatchi. “Charles Saatchi's Great Masterpieces: when a family scene was an act of rebellion.”19 March 2018. 7:00AMhttps://www.telegraph.co.uk/art/artists/charles-saatchis-great-masterpieces-family-scene-act-rebellion/
9. TotallyHistory. “Woman with a Parasol”. http://totallyhistory.com/woman-with-a-parasol/
10.Peter C. Baker. “THE REAl WORLD OF MONET”, The New York. January 10,2013. https://www.newyorker.com/books/page-turner/the-real-world-of-monet
Improving financial literacy in
college of business students:
modernizing delivery tools
Ronald Kuntze
College of Business, University of New Haven, West Haven, Connecticut, USA
Chen (Ken) Wu and Barbara Ross Wooldridge
Soules Colleg.
2The following is a list of some of the resources availabl.docxnovabroom
2
The following is a list of some of the resources available in the Trident Online Library related to the HR field.
Academic Research
Journal of Applied Psychology
This journal focuses on the applications of psychology research. This research journal is a good source for learning about the latest developments in cognitive, motivational and behavioral psychology and implications for the workplace. It is available through Business Source Complete in the Trident Online Library.
Personnel Psychology: A Journal of Applied Research
This scholarly journal has practical utility in that it centers on personnel psychology. The articles focus on the latest research on selection and recruitment, training, leadership, rewards, and diversity. It is available through Business Source Complete in the Trident Online Library.
Academy of Management Journal
This journal focuses on the management side of psychology. The articles are mainly theoretical. This journal would be a good resource for those researchers looking for new managerial theories and methods. It is available through Business Source Complete in the Trident Online Library.
The Academy of Management Review
This journal also focuses on management psychology. It is regarded as a top journal in its field and publishes theoretical and conceptual articles on management and organization theory. It is available through Business Source Complete in the Trident Online Library.
Professional Journals
Harvard Business Review
Harvard Business Review is a cornerstone business journal that has practical applications for HR professionals. This is a great resource to find case studies and expert insights on business practices. It is available through Business Source Complete in the Trident Online Library.
Human Resource Management Journal
This journal has best practices articles for HR professionals in the workplace. It is available (up to 1 year ago) through Business Source Complete in the Trident Online Library.
HRMagazine
This magazine is published by the Society for Human Resource Management. The articles are a great resource for HR professionals dealing with the most recent issues in the workplace. It is available through Business Source Complete in the Trident Online Library.
TD: Talent Development
The Association for Talent Development publishes this magazine. It is targeted to professionals in the human resource development field. It is available through Business Source Complete in the Trident Online Library.
Workforce
Solution
s Review
This magazine that focuses on many topics within human resource management. The articles included are written by industry experts and academics. They are targeted to HR professionals in the workplace. It is available through Business Source Complete in the Trident Online Library.
Adapted from: PennState University Libraries (2017). Retrieved from http://guides.libraries.psu.edu/human-resources/journals.
Assignment
Select three articles (published within the past five years),.
3 If you like to develop a computer-based DAQ measurement syst.docxnovabroom
3:
If you like to develop a computer-based DAQ measurement system or that can provide several functions in a Smart Home System, such as climate control or gas leakage detection functions, answer the following for the climate control systemfunction:
3.1 Draw the hardware connections of the system focusing on the pin connections of the system components, so that the system can provide the 'Climate Control'
function. The available devices are: (5 marks)
Microprocessor-based system (Laptop/PC).
Interface board: NI USB DAQ.
LM35 Temperature sensor Humidity sensor
Micro-switches Variable resistor LEDs Relays
Multi-output power supply
Include any required passive electronic components
3.2 Draw a flowchart for a program that can achieve both the climate control and gas leakage detection functions. (4 marks)
3.3 What are the factors that should be considered when selecting a DAQ card?
(4 marks)
3.4 Discuss the signal aliasing problem and how you can overcome this effect; supportyour answer with figures and drawings(2 marks)
3.5 What are the steps of conversion of continuous signals to digital values (ADC)?
(2 marks)
3.6 Name four types of ADC’s and choose any two to compare between them; what is the ADC type that is used in NI DAQ’s? support your answer with figures anddrawings(7 marks)
3.7 Compare between RTD (Resistance Type Device) and Thermocouples temperature sensors; support your answer with examples and drawings. The LM35 sensor can be classified as which type of temperature sensors? (5 marks)
3.8 Give examples of DAQ cards that can be used to measure the following properties and discuss the reasons for your selection.?
1- Displacement
2- Vibration
3- Strain (6 marks)
Total 35 marks4:
You are to develop a home security system that can be used to monitor a house of two doors and four windows. The output of the system should present the status of each location independently and should provide an audible warning in case of any problem - including the detection of smoke. The available devices are:
− PIC16F877 Microcontroller (given in Figure 4.1)
− two door push button switches
− four window push button switches
− one Motion Detector
− one smoke detector sensor
− eight LEDs
− one buzzer
− Include any passive electronic components required.
According to your study answer the following questions:
4.1 Draw a block diagram for the complete system. (4 marks)
4.2 Using the PIC16F877A microcontroller shown in Figure 4.1, draw the wiring diagram of the proposed system. Include any necessary electronic components required for the microcontroller to function correctly; state the function of each
element. (8 marks)
4.3 Draw a flowchart for a program that can achieve the above function. (4 marks)
4.4 Given the pin confi.
2BackgroundThe research focuses on investigating leaders fro.docxnovabroom
2
Background
The research focuses on investigating leaders from highly rated managed care organizations based on their leadership practices in comparison to leaders from low rated managed care organizations. High rated organizations are managed care organizations who have attained either 4.5 or 5 Medicare Stars ratings whiles low ratings organizations are organizations who have attained 3 Stars or less.
The research design: Survey was sent to leaders from both high Medicare rated and low rated organizations. I believe I have enough sample size so the result will be significant. I have received 35 response from leaders from high rated organizations and 35 from low rated organizations (35 participants each responded, making 70 participants in total). The goal is to find out if there is a significant difference in leadership practice between leaders from highly rated organizations and low rated organizations.
The survey tool used is Leadership Practice Inventory (LPI), which has a total of 30 behavioral statements that reflect on the practices leaders regularly use in managing their organizations. The leaders were invited to complete the survey online. The 30 survey questions are grouped in 5 Models:
1. Model the Way
1. Inspire a Shared Vision
1. Challenge the Process
1. Enable Others to Act
1. Encourage the Heart
The participants completed the LPI self-test, where they must rate themselves depending on the frequency, which they believe in engaging in each of the five models. They rate themselves on a 10 point likert scale, below.
1-Almost Never
3-Seldom
5-Occasionally
7-Fairly Often
9-Very Frequently
2-Rarely
4-Once in a While
6-Sometimes
8-Usually
10-Almost always
1. Dependent Variable: Attaining high Overall Medicare Star Rating
1. Independent Variables:
1. Leadership practice Practices (Model the Way, Inspire a Shared Vision, Challenge the Process, Enable Others to Act, and Encourage the Heart)
1. Years of Experience
1. Leadership Style
Abbreviations meaning:
LP- Leadership Practice
MSR – Medicare Stars Ratings
MSROs – Medicare Stars Ratings Organizations
YoE – Years of Experience
The following hypotheses has been tested, analyzed (page 4-23). SPSS software was used for data analysis.
Hypothesis 1 - There is a significant difference in LP between leaders from high (4.5 or 5) MSROs and low (3 Stars or less) MSROs.
Hypothesis 2 – There is a strong relationship between MSRs and the LP of both high and low MSROs
Hypothesis 3 - In comparison to other 4 models (thus Model the Way, Challenge the Process, Enable Others to Act, Encourage the Hearts), practicing the “Inspire A Shared Vision” model is very significant in helping leaders influence the attainment of high MSR in MCOs.
Hypothesis 4 – The leaders’ leadership style contributes to a leader’s ability to influence the achievement of high Medicare ratings for MCO.
Hypothesis 5 – The Leaders’ of Years of Experience (YoE) is effective in enabling leaders influence the attainment o.
2TITLE OF PAPERDavid B. JonesColumbia Southe.docxnovabroom
2
TITLE OF PAPER
David B. Jones
Columbia Southern University
BBA: 3201 Principles of Marketing
Nancy Ely Mount
Month/Date/ 2020
Marketing is
Four Elements of Marketing:
Creating
Communicating
Delivering
Exchanging
Holistic Marketing Concept is a people oriented approach utilizing the four principles of :
Relationship
Integrated
Internal
Performance marketing
.
2To ADD names From ADD name Date ADD date Subject ADD ti.docxnovabroom
2
To: ADD names From: ADD name Date: ADD date Subject: ADD title
Introduction
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum et nisl ante. Etiam pulvinar fringilla ipsum facilisis efficitur. Maecenas volutpat risus dignissim dui euismod auctor. Nulla facilisi. Mauris euismod tellus malesuada dolor egestas, ac vulputate odio suscipit.
Sed pellentesque sagittis diam, sit amet faucibus diam lobortis quis. Sed mattis turpis ligula, in accumsan ante pellentesque eu. Quisque ut nisl leo. Nullam ipsum odio, eleifend non orcinon, volutpat sollicitudin lacus (Cuddy, 2002). Identify Changes
Donec tincidunt ligula eget sollicitudin vehicula. Proin pharetra tellus id lectus mollis sollicitudin. Etiam auctor ligula a nulla posuere, consequat feugiat ex lobortis. Duis eu cursus arcu, congue luctus turpis. Sed dapibus turpis ac diam viverra consectetur. Aliquam placerat molestie eros vel posuere.
This Photo by Unknown Author is licensed under CC BY-SA
Figure 1. Title (Source: www.source-of-graphic.edu )Product Offerings
Sed facilisis, lacus vel accumsan convallis, massa est ullamcorper mauris, quis feugiat eros ligula eget est. Vivamus nunc turpis, lobortis et magna a, convallis aliquam diam. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Figure 2. Title (Source of data citation)
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum et nisl ante. Etiam pulvinar fringilla ipsum facilisis efficitur. Maecenas volutpat risus dignissim dui euismod auctor. Nulla facilisi. Mauris euismod tellus malesuada dolor egestas, ac vulputate odio suscipit. Capabilities
Donec tincidunt ligula eget sollicitudin vehicula. Proin pharetra tellus id lectus mollis sollicitudin. Etiam auctor ligula a nulla posuere, consequat feugiat ex lobortis. Duis eu cursus arcu, congue luctus turpis. Sed dapibus turpis ac diam viverra consectetur.
References
Basu, K. K. (2015). The Leader's Role in Managing Change: Five Cases of Technology-Enabled Business Transformation. Global Business & Organizational Excellence, 34(3), 28-42. doi:10.1002/joe.21602.
Connelly, B., Dalton, T., Murphy, D., Rosales, D., Sudlow, D., & Havelka, D. (2016). Too Much of a Good Thing: User Leadership at TPAC. Information Systems Education Journal, 14(2), 34-42.
Rouse, M. (2018). Changed Block Tracking. Retrieved from Techtarget Network: https://searchvmware.techtarget.com/definition/Changed-Block-Tracking-CBT
Change the Chart Title to Fit Your Needs
Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5
Assessing Similarities and Differences in Self-Control
between Police Officers and Offenders
Ryan C. Meldrum1 & Christopher M. Donner2 & Shawna Cleary3 &
Andy Hochstetler4 & Matt DeLisi4
Received: 2 August 2019 /Accepted: 21 October 2019 /
Published online: 2 December 2019
# Southern Criminal.
2Megan Bowen02042020 Professor Cozen Comm 146Int.docxnovabroom
2
Megan Bowen
02/04/2020
Professor Cozen
Comm 146
Interest Paper- Mental Health in Student Athletes
I am a communication major so must take this class to fulfill my requirements for the course, however, this class will set me up to understand the in-depth reasoning behind communication. The only rhetoric class I have taken in the past is rhetoric in English, not communication; I learnt about Plato, Socrates and all the pervious rhetors that formed the basis on how we communicate today. You could argue that learning it in English and now in communication it could be very similar or the same, but we aren’t focusing on what they wrote or spoke of but why and how. In this paper I chose to analyze a TedX talk from a student athlete Victoria Garrick called ‘Athletes and mental Health: The hidden opponent’, it discusses the challenges that she faced with mental health, and the struggles maintaining a top sport on a colligate team. The reasons behind this are based on the broad ideas and opinions people have on student athletes and mental health separately and together.
College athletics is a huge industry, an incredible achievement to get into a division 1 college on an athletic scholarship, but behind all this there are some dark truths. The TedX talk from Victoria Garrick explains these truths from an athlete’s perspective, this is conflicting to the ideas that an average student or outsider has, it explains what is happening behind closed doors. This artifact was gripping to me, it is something that I completely relate too; the artifact itself is a more personal approach to understand what is happening in regard to mental health in student athletes than just reading an article online. To me personally it is easier to find an artifact that I can easily relate too, something that is grossly underappreciated and classed as embarrassing, such a topic as mental health. There were no obstacles in retrieving artifacts for this interest, it is such a broad area that I am interested in finding more information about. There are artifacts everywhere about topics such as this, articles, speeches, documentaries, all gripping a relatable.
In this class I am aware that I have much to learn, understand the way in which we communicate and why, the best ways to communicate, and the best evidence and artifacts to find for a specific topic. Finding an artifact for a topic that you are deeply invested in is different than having to find one that your heart isn’t in. With regards to this paper I am already thinking about ideas of where I can focus my information on next, where can I understand different political views behind this topic? What are the families of these student athletes going through? Mental health and student athletes separately. With regards to this class I would like to be able to find these sources and write about them in a way that grips a reader and helps me understand the reasoning behind such communication methods.
1
2
Megan Bowen
P.
2From On the Advantage and Disadvantage of History for L.docxnovabroom
2
From On the Advantage and Disadvantage of History for Life, by Friedrich Nietzsche (1874)
Section 1:
CONSIDER the herds that are feeding yonder: they know not the meaning of yesterday or to-day; they graze and ruminate, move or rest, from morning to night, from day to day, taken up with their little loves and hates, at the mercy of the moment, feeling neither melancholy nor satiety. Man cannot see them without regret, for even in the pride of his humanity he looks enviously on the beast's happiness. He wishes simply to live without satiety or pain, like the beast; yet it is all in vain, for he will not change places with it. He may ask the beast—"Why do you look at me and not speak to me of your happiness?" The beast wants to answer—"Because I always forget what I wished to say": but he forgets this answer too, and is silent; and the man is left to wonder.
He wonders also about himself, that he cannot learn to forget, but hangs on the past: however far or fast he run, that chain runs with him. It is matter for wonder: the moment, that is here and gone, that was nothing before and nothing after, returns like a spectre to trouble the quiet of a later moment. A leaf is continually dropping out of the volume of time and fluttering away and suddenly it flutters back into the man's lap. Then he says, "I remember . . . ," and envies the beast, that forgets at once, and sees every moment really die, sink into night and mist, extinguished for ever. The beast lives unhistorically; for it "goes into" the present, like a number, without leaving any curious remainder. It cannot dissimulate, it conceals nothing; at every moment it seems what it actually is, and thus can be nothing that is not honest. But man is always resisting the great and continually increasing weight of the past; it presses him down, and bows his shoulders; he travels with a dark invisible burden that he can plausibly disown, and is only too glad to disown in converse with his fellows—in order to excite their envy. And so it hurts him, like the thought of a lost Paradise, to see a herd grazing, or, nearer still, a child, that has nothing yet of the past to disown, and plays in a happy blindness between the walls of the past and the future. And yet its play must be disturbed, and only too soon will it be summoned from its little kingdom of oblivion. Then it learns to understand the words "once upon a time," the "open sesame" that lets in battle, suffering and weariness on mankind, and reminds them what their existence really is, an imperfect tense that never becomes a present. And when death brings at last the desired forgetfulness, it abolishes life and being together, and sets the seal on the knowledge that "being" is merely a continual "has been," a thing that lives by denying and destroying and contradicting itself.
If happiness and the chase for new happiness keep alive in any sense the will to live, no philosophy has perhaps more truth than the cynic's: for the beast's happine.
257Speaking of researchGuidelines for evaluating resea.docxnovabroom
257
Speaking of research
Guidelines for evaluating research articles
Phillip Rumrill∗, Shawn Fitzgerald and
Megen Ware
Kent State University, Department of Educational
Foundations and Special Services Center for
Disability Studies, 405 White Hall, P.O. Box 5190,
Kent, OH 44242-0001, USA
The article describes the components and composition of
journal articles that report empirical research findings in the
field of rehabilitation. The authors delineate technical writing
strategies and discuss the contents of research manuscripts,
including the Title, Abstract, Introduction, Method, Results,
Discussion, and References. The article concludes with a
scale that practitioners, manuscript reviewers, educators, and
students can use in critically analyzing the content and scien-
tific merits of published rehabilitation research.
Keywords: Evaluation, research articles, guidelines for cri-
tique
1. Introduction
The purpose of this article is to examine the com-
ponents of a research article and provide guidelines
for conducting critical analyses of published works.
Distilled from the American Psychological Associa-
tion’s [1] Publication Manual and related descriptions
in several research design texts [4,8,9,12,15], descrip-
tions of how authors in rehabilitation and disability
studies address each section of a research article are
featured. The article concludes with a framework that
rehabilitation educators, graduate students, practition-
ers, and other Work readers can use in critiquing re-
search articles on the basis of their scientific merits and
practical utility.
∗Corresponding author: Tel.: +1 330 672 2294; Fax: +1 330 672
2512; E-mail: [email protected]
2. Anatomy of a research article
For nearly 50 years, the American Psychological As-
sociation has presented guidelines for authors to follow
in composing manuscripts for publication in profes-
sional journals [1]. Most journals in disability studies
and rehabilitation adhere to those style and formatting
guidelines. In the paragraphs to follow, descriptions
of each section of a standard research article are pre-
sented: Title, Abstract, Introduction, Method, Results,
Discussion, and References.
2.1. Title
As with other kinds of literature, the title of a scien-
tific or scholarly journal article is a very important fea-
ture. At the risk of contravening the age-old adage “You
can’t judge a book by its cover,” Bellini and Rumrill [4]
speculated that most articles in rehabilitation journals
are either read or not read based upon the prospective
reader’s perusal of the title. Therefore, developing a
clear, concise title that conveys the article’s key con-
cepts, hypotheses, methods, and variables under study
is critical for researchers wishing to share their findings
with a large, professional audience. A standard-length
title for a journal article in the social sciences is 12–15
words, including a sub-title if appropriate. Because so-
cial science and medical indexing systems rely hea.
2800 word count.APA formatplagiarism free paperThe paper.docxnovabroom
2800 word count.
APA format
plagiarism free paper
The paper should have:
Title with all the authors.
Introduction
Methods/Materials
Results (graphics and tables encouraged)
Discussion and conclusion
Citations.
.
28 CHAPTER 4 THE CARBON FOOTPRINT CONTROVERSY Wha.docxnovabroom
28
CHAPTER 4: THE CARBON FOOTPRINT CONTROVERSY
What is the carbon footprint controversy?
Nearly all humans consume meat, dairy, and egg products in some form. In recent years the
e i me al m eme ha ed he ece i f ed ci g e ca b f i . Ca e
reduce our footprint without changing our diet? Much controversy surrounds that question. One
very extreme view on the political-left is below.
But when it comes to bad for the environment, nothing literally compares with eating meat. The business of raising
animals for food causes about 40 percent more global warming than all cars, trucks, and planes combined. If you care
about the planet, it's actually better to eat a salad in a Hummer than a cheeseburger in a Prius.
Bill Maher, host of HBO talk show Real Time with Bill Maher, writing in the Huffington Post in 2009. Accessed April 25,
2013 at http://www.huffingtonpost.com/bill-maher/new-rule-a-hole-in-one-sh_b_259281.html.
The last decade has seen a movement advocating a vegan diet in order to reduce carbon emissions,
and in some respects the argument is logical. After all, it takes about 3.388 lbs of corn (and many
other inputs) to produce a single pound of retail beef, making meat seem relatively inefficient to
grains, thus leading to a larger carbon footprint.134 So common is this notion that some schools
e c age Mea le M da for the sake of the environment. The Meatless Monday movement
has even been adopted by the Norwegian military.135 Moreover, there is some scientific research
showing that vegan (and vegetarian) diets do result in a smaller carbon footprint.136
When dealing with issues as big as global warming i ea feel hel le , like he e li le e ca d make a
diffe e ce B he mall cha ge e make e e da ca ha e a eme d im ac . Tha h his Meatless Monday
resolution is important. Together we can better our health, the animals and the environment, one plate at a time.
Los Angeles Councilmember Ed Reyes, co-author of a Meatless Monday resolution in 2012.137
However, equally prestigious research shows that vegan diets can result in a higher carbon
footprint.138 How can this be? One reason is that some carbon footprint estimates are wrong, or
rather, interpreted incorrectly. The idea of livestock production being a large carbon emitter began
with a report by the United Nations (UN) suggesting that livestock contributes 18% f he ld
carbon footprint, more than the transportation sector,139 thus giving Bill Maher reason to point the
blame at burgers instead of Hummers.
It turns out that this 18% is fraught with errors, a lea , d e e e e c di i i he U.S.
For instance, the UN did not account for the carbon emissions involved in making the inputs used
in the transportation sector, but they did for livestock. This would be like saying the production of
tires has zero carbon emissions but the production of corn does. Also, that 18% makes a number of
contestable assumptions, especially regardi.
261
Megaregion Planning
and High-Speed Rail
Petra Todorovich
c h a p t e r 2 4
?
On April 16, 2009, President Obama stood before an audience at the Eisenhower
Executive Office Building and made an announcement that signaled a new era of
passenger rail in the United States. Months before, the American Recovery and
Reinvestment Act (ARRA) had provided $8 billion for a new program at the
Federal Railroad Administration (FRA) to issue competitive grants to states to
make capital investments in high-speed and conventional passenger rail. Little did
the president know that providing the single largest boost for intercity rail plan-
ning in this country in a generation had also motivated a sudden and giant leap for-
ward in planning and governing megaregions. Luckily, regional planners had been
studying emerging megaregions for the previous five years, in affiliation with the
New York–based Regional Plan Association’s (RPA) America 2050 program. Again
and again, the planners had identified high-speed rail as the key transportation
investment to serve megaregion economies. But high-speed rail was a distant
dream. That all changed with the passage of ARRA at the nadir of the Great
Recession. Now a federal program exists to support high-speed rail planning
and implementation. Making that program a success will largely depend on the
ability of multiple actors at the local, regional, state, and binational levels to come
together as megaregions to coordinate and leverage federal rail investments.
Revisiting Megalopolis: RPA Resurrects
the Megaregion Idea
As if planning for the Tri-State New York metropolitan region was not sufficiently
complicated, in 2005 the Regional Plan Association launched a national program
called America 2050 that focused on the emergence of a new urban scale: the
megaregion. This was not actually a new concept for RPA. In 1967 a volume of the
Second Regional Plan documented the emergence of “The Atlantic Urban Region,”
an urban chain stretching 460 miles from Maine to Virginia (Regional Plan
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Account: s7380033.main.cmmc
Association 1967). Earlier that decade, French geographer Jean Gottmann had
coined the term “Megalopolis” to describe the same region in his 1961 book,
Megalopolis: The Urbanized Northeastern Seaboard of the United States (Gottmann
1961). The .
250 WORDS Moyer Instruments is a rapidly growing manufacturer .docxnovabroom
250 WORDS
Moyer Instruments is a rapidly growing manufacturer of medical devices. As a result of its growth, the company's management recently modified several of its procedures and practices to improve internal control. Some employees are upset with the changes. They have complained that all these changes just show that the company no longer trusts them. Required: "Internal controls exist because most people can't be trusted." Is this true? Explain.
.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Thesis Statement for students diagnonsed withADHD.ppt
16 USING LINEAR REGRESSION PREDICTING THE FUTURE16 MEDIA LIBRAR.docx
1. 16 USING LINEAR REGRESSION PREDICTING THE
FUTURE
16: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Linear Regression
Lightboard Lecture Video
· Multiple Regression
Time to Practice Video
· Chapter 16: Problem 2
Difficulty Scale
(as hard as they get!)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Understanding how prediction works and how it can be used in
the social and behavioral sciences
· Understanding how and why linear regression works when
predicting one variable on the basis of another
· Judging the accuracy of predictions
· Understanding how multiple regression works and why it is
useful
INTRODUCTION TO LINEAR REGRESSION
You’ve seen it all over the news—concern about obesity and
how it affects work and daily life. A set of researchers in
Sweden was interested in looking at how well mobility
disability and/or obesity predicted job strain and whether social
support at work can modify this association. The study included
more than 35,000 participants, and differences in job strain
mean scores were estimated using linear regression, the exact
focus of what we are discussing in this chapter. The results
found that level of mobile disability did predict job strain and
that social support at work significantly modified the
association among job strain, mobile disability, and obesity.
Want to know more? Go to the library or go online …
Norrback, M., De Munter, J., Tynelius, P., Ahlstrom, G., &
2. Rasmussen, F. (2016). The association of mobility disability,
weight status and job strain: A cross-sectional
study. Scandinavian Journal of Public Health, 44, 311–319.
WHAT IS PREDICTION ALL ABOUT?
Here’s the scoop. Not only can you compute the degree to which
two variables are related to one another (by computing a
correlation coefficient as we did in Chapter 5), but you can also
use these correlations to predict the value of one variable based
on the value of another. This is a very special case of how
correlations can be used, and it is a very powerful tool for
social and behavioral sciences researchers.
The basic idea is to use a set of previously collected data (such
as data on variables X and Y), calculate how correlated these
variables are with one another, and then use that correlation and
the knowledge of X to predict Y. Sound difficult? It’s not
really, especially once you see it illustrated.
For example, a researcher collects data on total high school
grade point average (GPA) and first-year college GPA for 400
students in their freshman year at the state university. He
computes the correlation between the two variables. Then, he
uses the techniques you’ll learn about later in this chapter to
take a new set of high school GPAs and (knowing the
relationship between high school GPA and first-year college
GPA from the previous set of students) predict what first-year
GPA should be for a new student who is just starting out. Pretty
nifty, huh?
Here’s another example. A group of kindergarten teachers is
interested in finding out how well extra help after for their
students aids them in first grade. That is, does the amount of
extra help in kindergarten predict success in first grade? Once
again, these teachers know the correlation between the amount
of extra help and first-grade performance from prior years; they
can apply it to a new set of students and predict first-grade
performance based on the amount of kindergarten help.
How does regression work? Data are collected on past events
(such as the existing relationship between two variables) and
3. then applied to a future event given knowledge of only one
variable. It’s easier than you think.
The higher the absolute value of the correlation coefficient,
regardless of whether it is direct or indirect (positive or
negative), the more accurate the prediction is of one variable
from the other based on that correlation. That’s because the
more two variables share in common, the more you know about
the second variable based on your knowledge of the first
variable. And you may already surmise that when the
correlation is perfect (+1.0 or −1.0), then the prediction is
perfect as well. If rxy = −1.0 or +1.0 and if you know the value
of X, then you also know the exact value of Y. Likewise,
if rxy = −1.0 or +1.0 and you know the value of Y, then you
also know the exact value of X. Either way works just fine.
What we’ll do in this chapter is go through the process of using
linear regression to predict a Y score from an X score. We’ll
begin by discussing the general logic that underlies prediction,
then review some simple line-drawing skills and, finally,
discuss the prediction process using specific examples.
Why the prediction of Y from X and not the other way around?
Convention. Seems like a good idea to have a consistent way to
identify variables, so the Y variable becomes the dependent
variable or the one being predicted and the X variable becomes
the independent variable and is the variable used to predict the
value of Y. And when predicted, the Y value is represented
as Y′ (read as Y prime)—the predicted value of Y. (To sound
like an expert, you might call the independent variable
a predictor and the dependent variable the criterion. Purists save
the terms independent and dependent to describe cause-and-
effect relationships, which we cannot assume when talking
about correlations.)THE LOGIC OF PREDICTION
Before we begin with the actual calculations and show you how
correlations are used for prediction, let’s understand the
argument for why and how prediction works. We will continue
with the example of predicting college GPA from high school
GPA.
4. Prediction is the computation of future outcomes based on a
knowledge of present ones. When we want to predict one
variable from another, we need to first compute the correlation
between the two variables. Table 16.1 shows the data we will be
using in this example. Figure 16.1 shows the scatterplot
(see Chapter 5) of the two variables that are being computed.
Table 16.1 ⬢ Total High School GPA and First-Year College
GPA
High School GPA
First-Year College GPA
3.50
3.30
2.50
2.20
4.00
3.50
3.80
2.70
2.80
3.50
1.90
2.00
3.20
3.10
3.70
3.40
2.70
1.90
3.30
3.70
Figure 16.1 ⬢ Scatterplot of high school GPA and college GPA
To predict college GPA from high school GPA, we have to
create a regression equation and use that to plot what is called
a regression line. A regression line reflects our best guess as to
what score on the Y variable (college GPA) would be predicted
5. by a score on the X variable (high school GPA). For all the data
you see in Table 16.1, the regression line is drawn so that it
minimizes the distance between itself and each of the points on
the predicted (Y′) variable. You’ll learn shortly how to draw
that line, shown in Figure 16.2.
What does the regression line you see in Figure 16.2 represent?
First, it’s the regression of the Y variable on the X variable. In
other words, Y (college GPA) is being predicted from X (high
school GPA). This regression line is also called the line of best
fit. The line fits these data because it minimizes the distance
between each individual point and the regression line. Those
distances are errors because it means the prediction was wrong;
it was some distance from the right answer. The line is drawn to
minimize those errors. For example, if you take all these points
and try to find the line that best fits them all at once, the line
you see in Figure 16.2 is the one you would use.
Second, it’s the line that allows us our best guess (at estimating
what college GPA would be, given each high school GPA). For
example, if high school GPA is 3.0, then college GPA should be
around (remember, this is only an eyeball prediction) 2.8. Take
a look at Figure 16.3 to see how we did this. We located the
predictor value (3.0) on the x-axis, drew a perpendicular line
from the x-axis to the regression line, then drew a horizontal
line to the y-axis, and finally estimated what the predicted value
of Y would be.
Figure 16.3 ⬢ Estimating college GPA given high school GPA
Third, the distance between each individual data point and the
regression line is the error in prediction—a direct reflection of
the correlation between the two variables. For example, if you
look at data point (3.3, 3.7), marked in Figure 16.4, you can see
that this (X, Y) data point is above the regression line. The
distance between that point and the line is the error in
prediction, as marked in Figure 16.4, because if the prediction
were perfect, then all the predicted points would fall where?
Right on the regression or prediction line.
6. Figure 16.4 ⬢ Prediction is rarely perfect: estimating the error
in prediction
Fourth, if the correlation were perfect (and the x-axis meets
the y-axis at Y ’s mean), all the data points would align
themselves along a 45° angle, and the regression line would
pass through each point (just as we said earlier in the third
point).
Given the regression line, we can use it to precisely predict any
future score. That’s what we’ll do right now—create the line
and then do some prediction work.CORE CONCEPTS IN
STATS VIDEOLinear RegressionDRAWING THE WORLD’S
BEST LINE (FOR YOUR DATA)
The simplest way to think of prediction is that you are
determining the score on one variable (which we’ll call Y—
the criterion or dependent variable) based on the value of
another score (which we’ll call X—the predictor or independent
variable).
The way that we find out how well X can predict Y is through
the creation of the regression line we mentioned earlier in this
chapter. This line is created from data that have already been
collected. The equations are then used to predict scores using a
new value for X, the predictor variable.
Formula 16.1 shows the general formula for the regression line,
which may look familiar because you may have used something
very similar in a high school or college math course. In
geometry, it’s the formula for any straight line:
(16.1)
Y'=bX+a,Y′=bX+a,
where
· Y ′ is the predicted score of Y based on a known value of X;
· b is the slope, or direction, of the line;
· X is the score being used as the predictor; and
· a is the point at which the line crosses the y-axis.
Let’s use the same data shown earlier in Table 16.1, along with
a few more calculations that we will need thrown in.
9. From this table, we see that
· ∑ X, or the sum of all the X values, is 31.4.
· ∑Y, or the sum of all the Y values, is 29.3.
· ∑ X 2, or the sum of each X value squared, is 102.5.
· ∑ Y 2, or the sum of each Y value squared, is 89.99.
· ∑ XY, or the sum of the products of X and Y, is 94.75.
Formula 16.2 is used to compute the slope of the regression line
(b in the equation for a straight line):
(16.2)
b=ΣXY−(ΣXΣY/n)ΣX2−[(ΣX)2/n].b=ΣXY−(ΣXΣY/n)ΣX2−[(ΣX)
2/n].
In Formula 16.3, you can see the computed value for b, the
slope of the line:
(16.3)
b=94.75−[(31.4×29.3)/10]102.5−[(31.4)2/10],b=2.7493.904=0.7
04.b=94.75−[(31.4×29.3)/10]102.5−[(31.4)2/10],b=2.7493.904=
0.704.
Formula 16.4 is used to compute the point at which the line
crosses the y-axis (a in the equation for a straight line):
(16.4)
a=ΣY−bΣXn.a=ΣY−bΣXn.
In Formula 16.5, you can see the computed value for a, the
intercept of the line:
(16.5)
a=29.3−(0.704×31.4)10,a=7.1910=0.719.a=29.3−(0.704×31.4)10
,a=7.1910=0.719.
Now, if we go back and substitute b and a into the equation for
a straight line (Y = bX + a), we come up with the final
regression line:
Y'=0.704X+0.719.Y′=0.704X+0.719.
Why the Y ′ and not just a plain Y ? Remember, we are
using X to predict Y, so we use Y ′ to mean the predicted and
not the actual value of Y.
So, now that we have this equation, what can we do with it?
Predict Y, of course.
For example, let’s say that high school GPA equals 2.8 (or X =
10. 2.8). If we substitute the value of 2.8 into the equation, we get
the following formula:
Y'=0.704(2.8)+0.719=2.69.Y′=0.704(2.8)+0.719=2.69.
So, 2.69 is the predicted value of Y (or Y ′) given X is equal to
2.8. Now, for any X score, we can easily and quickly compute a
predicted Y score.
You can use this formula and the known values to compute
predicted values. That’s most of what we just talked about. But
you can also plot a regression line to show how well the scores
(what you are trying to predict) actually fit the data from which
you are predicting. Take another look at Figure 16.2, the plot of
the high school–college GPA data. It includes a regression line,
which is also called a trend line. How did we get this line?
Easy. We used the same charting skills you learned in Chapter
5 to create a scatterplot; then we selected Add Fit Line in the
SPSS Chart Editor. Poof! Done!
You can see that the trend is positive (in that the line has a
positive slope) and that the correlation is .6835—very positive.
And you can see that the data points do not align directly on the
line, but they are pretty close, which indicates that there is a
relatively small amount of error.
Not all lines that fit best between a bunch of data points are
straight. Rather, they could be curvilinear, just as you can have
a curvilinear relationship between your variables, as we
discussed in Chapter 5. For example, the relationship between
anxiety and performance is such that when people are not at all
anxious or very anxious, they don’t perform very well. But if
they’re moderately anxious, then performance can be enhanced.
The relationship between these two variables is curvilinear, and
the prediction of Y from X takes that into account. Dealing with
curvilinear relationships is beyond the scope of this book, but
fortunately, most relationships you’ll see in the social sciences
are essentially linear.
HOW GOOD IS YOUR PREDICTION?
How can we measure how good a job we have done predicting
one outcome from another? We know that the higher the
11. absolute magnitude of the correlation between two variables,
the better the prediction. In theory, that’s great. But being
practical, we can also look at the difference between the
predicted value (Y ′) and the actual value (Y) when we first
compute the formula of the regression line.
For example, if the formula for the regression line is Y ′ =
0.704X + 0.719, the predicted Y (or Y ′) for an X value of 2.8 is
0.704(2.8) + 0.719, or 2.69. We know that the actual Y value
that corresponds to an X value is 3.5 (from the data set shown
in Table 16.1). The difference between 3.5 and 2.69 is 0.81, and
that’s the size of the error in prediction.
Another measure of error that you could use is the coefficient of
determination (see Chapter 5), which is the percentage of error
that is reduced in the relationship between variables. For
example, if the correlation between two variables is .4 and the
coefficient of determination is 16% or .42, the reduction in
error is 16% since initially we suspect the relationship between
the two variables starts at 0 or 100% error (no predictive value
at all).
If we take all of these differences, we can compute the average
amount that each data point differs from the predicted data
point, or the standard error of estimate. This is a kind of
standard deviation that reflects average error along the line of
regression. The value tells us how much imprecision there is in
our estimate. As you might expect, the higher the correlation
between the two values (and the better the prediction), the lower
this standard error of estimate will be. In fact, if the correlation
between the two variables is perfect (either +1 or −1), then the
standard error of estimate is zero. Why? Because if prediction is
perfect, all of the actual data points fall on the regression line,
and there’s no error in estimating Y from X.
The predicted Y ′, or dependent variable, need not always be a
continuous one, such as height, test score, or problem-solving
skills. It can be a categorical variable, such as admit/don’t
admit, Level A/Level B, or Social Class 1/Social Class 2. The
score that’s used in the prediction is “dummy coded” to be a 0
12. or a 1 (or any two values) and then used in the same equation.
Yes, you are right that the level of measurement for this sort of
correlational stuff is supposed to be at the interval level, but a
variable with just two values works mathematically as if it has
equal-sized intervals because there is only one interval.
USING SPSS TO COMPUTE THE REGRESSION LINE
Let’s use SPSS to compute the regression line that predicts Y′
from X. The data set we are using is Chapter 16 Data Set 1. We
will be using the number of hours of training to predict how
severe injuries will be if someone is injured playing football.
There are two variables in this data set:
Variable
Definition
Training (X)
Number of hours per week of strength training
Injuries (Y)
Severity of injuries on a scale from 1 to 10
Here are the steps to compute the regression line that we
discussed in this chapter. Follow along and do it yourself.
1. Open the file named Chapter 16 Data Set 1.
2. Click Analyze → Regression → Linear. You’ll see the Linear
Regression dialog box shown in Figure 16.5.
3. Click on the variable named Injuries and then move it to the
Dependent: variable box. It’s the dependent variable because its
value depends on the value of number of hours of training. In
other words, it’s the variable being predicted.
4. Click on the variable named Training and then move it to the
Independent(s): variable box.
5. Click OK, and you will see the partial results of the analysis,
as shown in Figure 16.6.
We’ll get to the interpretation of this output in a moment. First,
let’s have SPSS overlay a regression line on the scatterplot for
these data like the one you saw earlier in Figure 16.2.
6. Click Graphs → Legacy Dialogs → Scatter/Dot.
7. Click Simple Scatter and then click Define. You’ll see the
13. simple Scatterplot dialog box.
8. Click Injuries and move it to the variable label to the Y Axis:
box. Remember, the predicted variable is represented by the y-
axis.
9. Click Training and move it to the variable label to the X
Axis: box.
10. Click OK, and you will see the scatterplot as shown
in Figure 16.7.
Now let’s draw the regression line.
11. If you are not in the chart editor, double-click on the chart
to select it for editing.
12. Click on the Add Fit Line at Total button (on the second
row of buttons, about fifth from the left) that looks a little like
this: .
13. Close the Properties box that opened when you selected the
Add Fit Line at Total button and then close the chart editor
window. The completed scatterplot, with the regression line, is
shown in Figure 16.8 along with the multiple regression
value R2, which equals 0.21. As you will read more about
shortly, the multiple regression correlation coefficient is the
regression of all the X values on the predicated value.
When you have the Properties dialog box open for drawing the
regression line, notice that there is a set of Confidence Intervals
options. When clicked, these show you a boundary within which
there is a specific probability as to how good the prediction is.
For example, if you click Mean and specify 95%, the graph will
show you the boundaries surrounding the regression line, within
which there is a 95% chance of the predicted scores occurring.
This idea of wanting to be within a certain range of error 95%
of the time is the same as wanting a .05 significance level for
statistical analyses.
Figure 16.5 ⬢ Linear Regression dialog box
Understanding the SPSS Output
The SPSS output tells us several things:
1. The formula for the regression line is taken from the first set
of output shown in Figure 16.6 as Y ′ = –0.125X + 6.847. This
14. equation can be used to predict level of injury given any number
of hours spent in strength training.
2. As you can see in Figure 16.8, the regression line has a
negative slope, reflecting a negative correlation (of –.458,
which is what Beta is in Figure 16.6) between hours of training
and severity of injuries. So it appears, given the data, that the
more one trains, the fewer severe injuries occur.
3. You can also see that the prediction is significant—in other
words, predicting Y from X is based on a significant
relationship between the two variables such that the test of
significance for both the constant (Training) and the predicted
variable (Injuries) is significantly different from zero (which it
would be if there was no predictive value for X predicting Y).
So just how good is the prediction? Well, the SPSS output
(which we did not show you) also indicates that the standard
error of estimate for Injuries (the predicted variable) is 2.182;
double that (4.36) and you’ll see that there is a 95% chance
(remember 1.96 or about 2 standard deviations away from the
mean creates a 95% confidence interval) the prediction will fall
between the mean of all injuries (which is 4.33) and ±4.46. So,
based on the correlation coefficient, the prediction is okay but
not great.
THE MORE PREDICTORS THE BETTER? MAYBE
All of the examples that we have used so far in the chapter have
been for one criterion or outcome measure and one predictor
variable. There is also the case of regression where more than
one predictor or independent variable is used to predict a
particular outcome. If one variable can predict an outcome with
some degree of accuracy, then why couldn’t two do a better job?
Maybe so, but there’s a big caveat—read on.
For example, if high school GPA is a pretty good indicator of
college GPA, then how about high school GPA plus number of
hours of extracurricular activities? So, instead of
Y'=bX+a,Y′=bX+a,
the model for the regression equation becomes
Y'=bX1+bX2+a,Y′=bX1+bX2+a,
15. where
· X1 is the value of the first independent variable,
· X2 is the value of the second independent variable,
· b is the regression weight for that particular variable, and
· a is the intercept of the regression line, or where the
regression line crosses the y-axis.
As you may have guessed, this model is called multiple
regression (multiple predictors, right?). So, in theory anyway,
you are predicting an outcome from two independent variables
rather than one. But you want to add additional predictor
variables only under certain conditions. Read on.
LIGHTBOARD LECTURE VIDEO
Multiple Regression
Any variable you add has to make a unique contribution to
understanding the dependent variable. Otherwise, why use it?
What do we mean by unique? The additional variable needs to
explain differences in the predicted variable that the first
predictor does not. That is, the two variables in combination
should predict Y better than any one of the variables would do
alone.
In our example, level of participation in extracurricular
activities could make a unique contribution. But should we add
a variable such as the number of hours each student studied in
high school as a third independent variable or predictor?
Because number of hours of study is probably highly related to
high school GPA (another of our predictor variables,
remember?), study time probably would not add very much to
the overall prediction of college GPA. We might be better off
looking for another variable (such as ratings on letters of
recommendation) rather than collecting the data on study time.
Take a look at Figure 16.9, which is the result of a multiple
regression analysis that adds the number of extracurricular
activity hours to the data you saw in Table 16.1. You can see
how both high school GPA and number of hours of
extracurricular activity are significant contributors to first-year
college GPA. This is a powerful way of examining what and
16. how more than one independent variable contribute to
prediction of another variable.
Figure 16.9 ⬢ A multiple regression analysis
The Big Rule(s) When It Comes to Using Multiple Predictor
Variables
If you are using more than one predictor variable, try to keep
the following two important guidelines in mind:
1. When selecting a variable to predict an outcome, select a
predictor variable (X) that is related to the criterion variable
(Y). That way, the two share something in common (remember,
they should be correlated).
2. When selecting more than one predictor variable (such
as X1 and X2), try to select variables that are independent or
uncorrelated with one another but are both related to the
outcome or predicted (Y) variable.
In effect, you want only independent or predictor variables that
are related to the dependent variable and are unrelated to each
other. That way, each one makes as distinct a contribution as
possible to predicting the dependent or predicted variable.
There are whole books on multiple regression, and much of
what one needs to learn about this powerful procedure is beyond
the scope of this book. Chapter 18 talks more about multiple
regression.
How many predictor variables are too many? Well, if one
variable predicts some outcome, and two are even more
accurate, then why not three, four, or
five predictor variables? In practical terms, every time you add
a variable, an expense is incurred. Someone has to go collect
the data, it takes time (which is $$$ when it comes to research
budgets), and so on. From a theoretical sense, there is a fixed
limit on how many variables can contribute to an understanding
of what we are trying to predict. Remember that it is best when
the predictor or independent variables are independent or
unrelated to each other. The problem is that once you get to
three or four variables, fewer things can remain unrelated.
17. Better to be accurate and conservative than to include too many
variables and waste money and the power of prediction.
Real-World Stats
How children feel about what they do is often very closely
related to how well they do what they do. The aim of this study
was to analyze the consequences of emotion during a writing
exercise. In the model this research follows, motivation and
affect (the experience of emotion) play an important role during
the writing process. Fourth and fifth graders were instructed to
write autobiographical narratives with no emotional content,
positive emotional content, and negative emotional content. The
results showed no effect regarding these instructions on the
proportion of spelling errors, but the results did reveal an effect
on the length of narrative the children wrote. A simple
regression analysis (just like the ones we did and discussed in
this chapter) showed a correlation and some predictive value
between working memory capacity and the number of spelling
errors in the neutral condition only. Since the model on which
the researchers based much of their preliminary thought about
this topic states that emotions can increase the cognitive load or
the amount of “work” necessary in writing, that becomes the
focus of the discussion in this research article.
Want to know more? Go online or to the library and find …
Fartoukh, M., Chanquoy, L., & Piolat, A. (2012). Effects of
emotion on writing processes in …
Chapter 9 Global Inequality and Poverty
ONE PHOTO CAPTURES A SHARP CONTRAST BETWEEN
RICH AND POOR IN THE DEVELOPING WORLD. The high-
rise buildings in the background are apartments for the wealthy.
Learning Objectives
1. 9.1Examine how widening gap between rich and poor
strengthens inequality-perpetuating institutions
2. 9.2Contrast between the viewpoints of globalists and
antiglobalists on the effects of globalization
18. 3. 9.3Examine the causes and the impact of domestic or global
inequality between nations
4. 9.4Examine the economic, social, and educational inequality
that exists within rich countries
5. 9.5Examine the inequalities that exist in different aspects of
life in poor countries
6. 9.6Review the six dimensions of poverty that can be used to
gauge poverty
7. 9.7Evaluate some of the measures for diminishing poverty
and reducing inequality
The richest eighty people in the world control as much wealth
as the poorest half of the world’s population. Thirty-five of
those eighty are Americans. The top 1 percent of the world’s
richest people control 48 percent of the world’s total wealth.
More than one billion people in the world live on less than
$1.25 a day.1 Inequality exists within the United States. The
richest four hundred Americans own more assets than the
poorest 150 million, or almost half the population. The bottom
15 percent, about forty-six million people, live in households
earning less than $22,000 per year. The top 5 percent of
households in Washington, D.C., make an average of more than
$500,000, while the bottom 20 percent make less than $9,500.
Conflict between rich and poor is now the greatest source of
tension in American society. Economic inequality has emerged
as a dominant global issue that has fueled massive protests and
popular uprisings. The global financial crisis and economic
recession have rekindled debates about inequality and its
consequences. Discussions about wealth and poverty and how to
achieve greater equality are as old as human society. They
demonstrate a perennial concern about the implications of
inequality for the security and well-being of communities.
Given the persistence of inequality among individuals, groups,
and nations over centuries, this debate is interminable.
Struggles to achieve equality are also endless. Issues pertaining
to global inequality and poverty permeate almost every
significant global problem, from trade to the environment, from
19. terrorism and criminal activities to democratization and human
rights, and from ethnic conflicts to the proliferation of weapons
of mass destruction. As we have seen, popular uprisings in the
Middle East and North Africa were strongly influenced by
widespread inequality and poverty. Consequently, as our
discussion shows, inequality and poverty are closely connected
to politics, economics, and culture.
A central question addressed in this chapter is whether
inequality matters. Human societies are inherently unequal due
to variations of abilities, opportunities, geographic location,
luck, personal characteristics, and so on. But why is it
important to address issues of inequality, something that
societies have struggled with historically? Globalization is
widely perceived as the major cause of global inequality. Yet,
as we have noted, unequal distributions of wealth existed
independent of the current wave of globalization and are present
in societies little affected by it. This chapter analyzes the
globalization and inequality debate as well as the current state
of global inequality. In addition to focusing on inequality
between rich and poor countries and inequality within both
developed and developing societies, we will examine the issue
of gender inequality. This chapter discusses the enduring issues
of global poverty, hunger and malnutrition, economic
development and poverty, and efforts to close the gap between
rich and poor and reduce the negative effects of inequality and
poverty. The chapter concludes with a case study of food
security and rising food prices.9.1: Does Inequality Matter?
1. 9.1 Examine how widening gap between rich and poor
strengthens inequality-perpetuating institutions
The existence of inequality is not automatically a major
problem, especially when the economy is growing and there are
many opportunities for upward mobility. As long as the standard
of living is improving for those on the bottom of the economic
ladder, concerns about inequality tend to diminish. The last two
decades of the twentieth century and the first decade of this
century were characterized by a widening gap between rich and
20. poor and the proliferation of millionaires and billionaires.
While economic disparities remained a serious problem in
developing countries, the forces of globalization created
conditions that helped widen the gap between rich and poor in
industrialized societies. When the economy deteriorates, the gap
between rich and poor tends to be narrower, but concerns about
inequality are heightened. During the global economic
recession, the wealthy lost money, but the poor lost their jobs,
houses, and health insurance. In the United States, the poverty
rate peaked at 15.1 percent in 2010, its highest level since 1993.
In 2013, the poverty rate was still high, at 15.0 percent.
Widespread demonstrations in the United States against
excessive executive compensation, especially those in
companies that received financial assistance from the
government, underscores the dangers of economic inequality.
The financial and economic crisis increased inequality and
heightened awareness of the concentration of wealth held by the
top 1 percent of Americans. That awareness led to “We are the
99 percent,” a battle cry of the Occupy Wall Street protests
against financial inequality that began in New York City and
spread around the world. The perception that economic
inequality is essentially transitory when opportunities for
economic advancement are widely available mitigates negative
effects of actual inequality.
However, persistent inequality and enduring poverty challenge
beliefs in the equality of opportunity and the possibility of
upward mobility. Eventually, the legitimacy of the economic
system and political and social institutions are challenged.
Extreme inequality is detrimental to sustainable economic
growth.
The legitimacy of the global economic system is likely to be
strengthened if a larger number of countries and individuals are
benefiting from it. Extreme inequality perpetuates poverty and
the concentration of economic and political power and reduces
economic efficiency. It strengthens inequality-perpetuating
institutions in three ways:
21. 1. Inequality discourages the political participation of poor
people, which, in turn, diminishes their access to education,
health care, and other services that contribute to economic
growth and development.
2. Inequality often prevents the building and proper functioning
of impartial institutions and observance of the rule of law.
3. Inequality enables the wealthy to refuse to compromise
politically or economically, which further weakens poor
societies in a global society that requires relatively fast
responses to economic developments.2
These consequences of inequality combine to ensure that poor
societies will remain poor and unequal, trapping most of their
inhabitants in a destructive cycle of poverty. Growing
inequality among as well as within nations has direct and
indirect implications for globalization. Inequality could
undermine globalization by influencing countries to adopt
protectionist policies and disengage, to the extent possible,
from the global economy. But the ramifications extend beyond
economic issues to problems such as terrorism, the environment,
and the spread of infectious diseases. Inequality influences
global perceptions of America and weakens its soft power, or its
cultural attraction.
As Chapter 4 shows, the democratization process and the
effective functioning of consolidated democracy depend largely
on a significant degree of economic and social equality. The
legitimacy of any democratic system is contingent upon the
voters’ belief that they have a vested interest in its preservation.
Their allegiance to the democracy is influenced partly by the
benefits they derive from the economic system. Inequality
undermines democracy by fostering despair and alienation
among workers and corruption and the abuse of power among
the wealthy. It corrodes trust and civility among citizens.
Inequality destroys the people’s will to engage in collective
solutions to political, social, and economic problems because it
weakens their sense of unity and common interests. Massive
protests globally against governments underscore this point.
22. The unequal distribution of wealth is often mitigated by
government redistributive policies. Extreme inequality
sometimes results in the voters pressuring governments to enact
trade protection legislation to safeguard their employment and
livelihoods. In this case, voters exercising their democratic
rights could inadvertently undermine the economic system that
supports democracy.
Global and domestic inequalities often directly affect many
areas. Terrorism is widely linked to poverty within developing
nations. Huge inequalities often fuel resentment, which finds
expression in global crime and a general disregard for the rules
and norms of global society. Those who are extremely poor are
often excluded from participation in decisions that negatively
impact their lives. They become vulnerable to being influenced
by radical minorities who are committed to violent change.
Poverty contributes to global and regional problems by fueling
ethnic and regional conflicts, creating large numbers of
refugees, and inhibiting access to resources, such as petroleum.
Finally, global and domestic inequality is perceived as
stimulating the global drug trade. For example, poor farmers in
Bolivia regard the cultivation of coca as essential to their
survival. More than three-quarters of the heroin sold in Europe
is refined from opium grown in Afghanistan by poor farmers.
The costs of fighting the war against drugs in poor countries,
such as Colombia and Afghanistan, are extremely high.9.2: The
Globalization and Inequality Debate
1. 9.2 Contrast between the viewpoints of globalists and
antiglobalists on the effects of globalization
The impact of globalization on income distribution and living
standards is a controversial topic. Preoccupation with
globalization to the exclusion of other factors often muddles the
debate about globalization and inequality. Would less
globalization produce more equality, and would more equality
among and within nations result in an improved quality of life
for the poor? There are two dominant, but sometimes
overlapping, viewpoints on this issue. The globalists argue that
23. globalization has increased economic growth and decreased
global inequality and poverty. The antiglobalists generally
perceive globalization as a negative and destructive force that is
responsible for the increasing global inequality and poverty and
the declining levels of human welfare.39.2.1: Globalists Make
Their Case
From the globalists’ perspective, the basic cause of inequality
and poverty is the relatively low level of globalization in some
countries. In other words, the poorest societies are the least
integrated into the global economy. Openness to foreign trade,
investments, and technology—combined with reforms such as
the privatization of the domestic economy—will ultimately
accelerate economic growth. The Organization for Economic
Cooperation and Development (OECD) calculated that countries
that are relatively open to trade grew about twice as fast as
those that are relatively closed to trade.4 China’s rapid
economic growth is an obvious example. On the other hand,
North Korea, Myanmar (formerly Burma), and Kenya are on the
margins of globalization and remain impoverished.
Globalists also argue that globalization has contributed to the
decline of inequality. Furthermore, poverty can be reduced even
as inequality increases. David Dollar and Aart Kraay found that
“a long-term global trend toward greater inequality prevailed
for at least 200 years; it peaked around 1975. But since then, it
has stabilized and possibly even reversed.”5 The accelerated
economic growth of China and India, the world’s two most
populous countries, which is seen as directly linked to
globalization, is given as the principal reason for the change.
Much of the inequality that persists within countries is due less
to globalization and more to policies dealing with education,
taxation, and social problems. Moreover, more economic growth
in China, for example, has been accompanied by a spectacular
reduction in poverty.6
Globalists emphasize that the number of people moving out of
poverty has increased. More than 800 million people have
abandoned the ranks of absolute poverty since 1990. The
24. number of people living in absolute poverty remains high—
around 1.2 billion. But given rapid population growth rates in
the poorest countries, the decline in global poverty is
impressive. The world’s poor are seen as getting to be less poor
in both absolute and relative terms.7 The more globalized poor
nations become, the better off their populations are in both
absolute and relative terms. Globalization has generally helped
the poor by contributing to reductions in the cost of numerous
consumer products. Less money has higher purchasing power in
a globalized economy. Finally, by facilitating migration,
establishing small businesses that rely on the Internet, and
improving access to jobs in telecommunications and computer
technologies in countries such as India and China, globalization
improves the quality of life for the poor.9.2.2: Antiglobalists
Make Their Case
Antiglobalists believe that globalization is widening the gap
between the haves and the have-nots. Concerned with making
global capitalism more equitable, they view globalization as
primarily benefiting the rich while making life more difficult
for the poor. Antiglobalists argue that globalization is a zero-
sum game, meaning that the rich are winning at the expense of
the poor. Antiglobalists also argue that globalization benefits
rich countries, such as the United States. China is one of the
few developing countries that is generally regarded as profiting
from free trade and open markets. The United States, the
locomotive of globalization, benefits the most from open
markets worldwide. George Soros—a leading financier,
philanthropist, and critic of globalization, though not an
antiglobalist—believes that globalization drains surplus capital
from periphery or developing countries to the United States,
thereby allowing Americans to spend more than they save and
import more than they export.8 Similarly, Jack Beatty contends
that the foundation of inequality resulting from globalization is
that rich countries do not play by the rules that they made to
govern the global economic system. Basically, the United States
and other Western countries require developing countries to
25. open their markets without reciprocating commensurably. To
support this argument, Beatty points out that although global
rules on trade discourage governments from subsidizing
industries, rich countries continue to provide subsidies to
agriculture.9
Critics also argue that globalization is like an “economic
temptress,” promising riches but not delivering. Global
communications have heightened awareness of the vast
disparities between rich and poor within the same society and
especially between rich and poor countries. Simultaneously,
global communications spawn aspirations of escaping poverty
and enjoying the good life. Unfortunately, globalization is
unable to make these dreams real. Countries integrated into the
global economic system are the most severely affected by
downturns in the economy. For example, Southeast Asia, which
depends on exports of steel, textiles, and electronic components,
suffers significantly in global economic crises and is unable to
generate enough jobs and sufficient wages for a population with
aspirations nurtured by television programs that depict
prosperity. Although conceding that globalization is not entirely
responsible for global poverty, antiglobalists generally view
globalization as a tide that lifts a few boats while leaving the
majority mired at the bottom. Even when global companies
create jobs within societies, the race to the bottom in labor
standards and wages inevitably results in the poor in developing
countries being unable to escape poverty while, at the same
time, reducing the wages for workers in rich countries or
depriving them of employment. This development is intertwined
with the precipitous decline of private sector labor unions. Kim
Phillips-Fein argues that unions mobilize their members to vote
for government policies that help redistribute wealth and
reinforce upward mobility, which strengthen the middle class.10
Antiglobalists contend that globalization compounds existing
inequalities and creates more inequality. By giving priority to
privatization, globalization weakens governments’ commitment
to the public sector. Vito Tanzi states that “even as the forces of
26. globalization boost the demand for strong social safety nets to
protect the poor, these forces also erode the ability of
governments to finance and implement large-scale social
welfare policies.”11 The emphasis on integrating poor nations
into the global economy diverts resources from more urgent
development needs, such as education, public health, industrial
capacity, and social cohesion. Many trade agreements impose
tight prerequisites on developing countries in exchange for
crumbs of enhanced market access. The African Growth and
Opportunity Act is an example. It provides increased access to
the U.S. market only if African apparel manufacturers use fabric
and yarns produced in the United States, instead of using their
own or supplies from less expensive sources. In other words,
the antiglobalists perceive globalization as perpetuating
inequality by impeding development. Furthermore, they argue,
countries such as South Korea and Taiwan, that globalists
frequently held up as models for the benefits of globalization
developed under radically different conditions. These countries
were not required to pay the costs that are now an integral
component of integration into global markets. During the 1960s
and 1970s, when they were rapidly growing, Taiwan and South
Korea did not face contemporary globalization’s pressures to
privatize their economies and open their borders to capital
flows. The demands of globalization undermine efforts essential
for a comprehensive development agenda.
9.3: Global Inequality
1. 9.3 Examine the causes and the impact of domestic or global
inequality between nations
Discussions of global inequality remind us of many of the
reasons some societies created powerful and prosperous
civilizations while others did not. Western Europe emerged as
the most prosperous region of the world. Areas that are now the
United States, Canada, Australia, and New Zealand were
conquered and settled by Europeans, many of whom embodied
the characteristics that contributed to Europe’s rise to global
27. prominence and economic prosperity. The advantages Europeans
enjoyed have been consolidated. This, in turn, contributes to
global inequality today. Several factors combined to produce
Europe’s economic success and profound global economic
inequality. A major factor is freedom of expression. Societies
that encouraged people to have their own ideas, to be
innovative, and to interact with each other eventually surpassed
societies that were totalitarian or authoritarian. The latter
generally stifled innovation because of their preoccupation with
traditions, conformity, and respect for authority. Initiative was
often equated with heresy. Another factor encompasses social
values. Chief among these is an emphasis on economic
opportunity and social equality. In his Wealth and Poverty of
Nations, David S. Landes stresses that China’s restrictions on
women hampered its growth, whereas women in Europe, who
were less confined to the home and were free to find
employment in certain occupations, were instrumental in that
region’s industrial development and expansion.12 A third factor
is the functioning of a free market and institutionalized property
rights. Chinese authorities became antagonistic toward free
enterprise and eventually regulated it out of existence. Muslim
countries failed to develop institutions that would have enabled
businesses to expand. Islamic partnership law and inheritance
law worked against the growth of large corporations. In Europe,
a partner in a business could designate heirs, thereby providing
continuity in the business after the partner’s death. Islamic law
did not provide mechanisms for partnerships to be easily
reconstituted following a partner’s death. Similarly, Islamic law
prescribed in rigid detail both immediate and extended family
members who had to inherit property. Europe, on the other
hand, allowed property to be inherited by one person, thereby
minimizing the chances that a business would disintegrate and
be prevented from getting larger. Virginia Postrel points out
that “the fragmentation produced by inheritance law, combined
with the structures of partnership law, kept Middle Eastern
enterprises small. That, in turn, limited the pressure to evolve
28. new economic forms.”13 However, increasing wealth from
petroleum has significantly strengthened many companies in the
Persian Gulf area, especially those involved in finance.
A final factor undergirding Europe’s economic success and
setting the foundation for global inequality is the separation of
the secular from the religious. Whereas Islam became
inseparable from the state, the origins of Christianity and its
spread to Rome forced it to compromise with secular authority,
a compromise encapsulated in the warning that Christians
should give to Caesar what belongs to him and give God what is
God’s. However, Muslim societies prospered when religion was
less restrictive. Muslims, commanded by the Koran to seek
knowledge, became leading scientists, physicians, artists,
mathematicians, philosophers, architects, and builders. For
more than five hundred years, Arabic was the language of
scholars and scientists. Muslims transmitted Chinese scientific
inventions, Greek and Persian texts, and their own impressive
scientific discoveries and inventions to Europe. From the tenth
to the thirteenth centuries, Europeans translated Arabic works
into Hebrew and Latin, thereby giving impetus to a rebirth of
learning that ultimately transformed Western civilization.
9.3.1: Inequality between Developed and Developing Countries
Despite rising living standards throughout most of the world,
the gap between rich and poor countries has steadily
widened. Tables 9.1 and 9.2 show some of those disparities in
greater detail. Historic trends suggest that most of the richest
countries will maintain their lead over most of the poorest
countries. The gap between the richest country and the poorest
country was 3 to 1 in 1820, 11 to 1 in 1913, 35 to 1 in 1950, 44
to 1 in 1973, and 72 to 1 in 1992. By the end of the twentieth
century, the richest 20 percent of the world’s population had
eighty-six times as much income as the poorest 20 percent. At
the beginning of the twenty-first century, the average income in
the richest twenty
Table 9.1 Income Inequality among Countries, 2011 (in terms of
GDP per capita)
29. Adapted from UN Development Programme, Human
Development Report 2013: The Rise of the South. Human
Progress in a Diverse World (New York: UN Development
Program, 2013). GDP per capita is given in international dollars
using purchasing power parity rates (PPP).
Some Rich Countries
Qatar
77,987
Luxembourg
68,458
Singapore
53,591
Norway
46,982
Brunei Darussalam
45,507
Hong Kong, China (SAR)
43,844
United States
42,486
United Arab Emirates
42,293
Switzerland
37,979
Netherlands
37,251
Australia
34,548
Japan
30,660
Republic of Korea (South Korea)
27,541
Some Poor Countries
Ethiopia
979
30. Mali
964
Togo
914
Mozambique
861
Madagascar
853
Malawi
805
Sierra Leone
769
Central African Republic
716
Niger
642
Burundi
533
Eritrea
516
Liberia
506
Democratic Republic of the Congo
329
countries was thirty-seven times that in the poorest twenty
countries.14 As Table 9.1 indicates, income disparities between
developed and developing countries are very wide. Economic
development, while dramatically improving the standard of
living in most countries, has not significantly closed the gap
because of differential growth rates between rich and poor
countries. Rich countries have experienced higher economic
growth rates than poor countries. Furthermore, per capita
income actually declined in more than one hundred of the
world’s poorest countries, many of them in Africa. Even
developing countries that have enjoyed unprecedented economic
growth, such as China and India, have failed to close the gap
31. between themselves and rich countries. It is estimated that it
would take China and India a hundred years of constant growth
rates higher than those now experienced by industrialized
countries just to reach current American income levels.
However, given the extraordinarily high standard of living in
the United States, both China and India would be relatively
prosperous if they achieved half the income level of Americans.
Furthermore, globalization is profoundly altering many old
assumptions. Because the income gap between rich and poor
countries has widened historically, it does not necessarily
follow that this will always be the case. Singapore and Kuwait,
two high-income countries, illustrate that poor countries can
become prosperous by implementing astute political, social, and
economic policies (in the case of Singapore) or by having
valuable natural resources (in the case of Kuwait). Economic
disparities between the developed and the developing world
have focused on the global digital divide. But access to the
Internet and improved telecommunications are not automatic
panaceas for solving the problems of developing societies.
9.3.2: Causes of Inequality between Rich and Poor Countries
In this section, we will briefly discuss some causes of the
widening gap between rich and poor countries. It is important to
remember that several factors combine to contribute to
inequality: (1) geography, (2) colonialism and its legacies, (3)
the structure of the global economy, (4) population growth, (5)
government policies, (6) political instability, and (7) natural
disasters.
Geography
Countries that are poor, some argue, have certain geographic
characteristics that contribute to their economic problems. For
example, they are in tropical regions or face high transportation
costs in accessing global markets because of their location.
Apart from the prevalence of tropical diseases, which have been
controlled to a large extent by modern medicines and practices,
countries in the Southern Hemisphere also tend to suffer from
being landlocked. Countries with extensive coastlines and good
32. harbors tend to be better off economically than landlocked
countries that lack the physical infrastructure (i.e., systems such
as roads and railroads) essential for gaining access to navigable
rivers and the sea. Landlocked countries or countries located far
from global markets are disadvantaged by high transportation
costs.
Colonialism
Many argue that European colonization of Africa, Asia, and
Latin America laid the foundation for economic disparities
between rich and poor nations. Inequality breeds inequality.
Just as wealth tends to perpetuate wealth, poverty tends to
perpetuate poverty. Countries that grew rich two hundred years
ago, partly because of their colonization of the developing
world, are generally still rich today. European groups that
migrated to Australia, Canada, the United States, South Africa,
New Zealand, and throughout Latin America continue to enjoy
significant advantages
Table 9.2 Health Inequalities
Adapted from UN Development Programme, Human
Development Report 2013: The Rise of the South. Human
Progress in a Diverse World (New York: UN Development
Program, 2013).
Physicians, 2005–2010 (per 1,000 people)
Life Expectancy at Birth, 2012 (years)
Maternal Mortality Ratio, 2010 (deaths per 100,000 live births)
Country
Rich Countries
Norway
4.1
34. 83.6
5
Republic of Korea (South Korea)
2.0
80.7
16
Poor Countries
Guinea
0.1
54.5
610
Central African Republic
0.1
49.1
890
Burkina Faso
0.1
55.9
300
Democratic Republic of Congo
0.1
48.7
540
Sierra Leone
0.0
48.1
890
Burundi
0.0
50.9
800
PPOL 650
35. Discussion Board Grading Rubric
Student:
Criteria
Points
Possible
Points Earned
Instructor’s Comments
Structure
· Presentation is strong and focused.
· Paragraphs are organized and coherent.
· Transitions are clear and maintain flow of thought.
· Conclusion is objective and rises from entry.
5
Content
· Thread is substantive, well-developed, and fully addresses all
aspects of the task.
· Replies demonstrate analysis of classmates’ posts.
· Replies extend meaningful discussion by building on previous
posts.
· Replies demonstrate an understanding of subject.
· Assertions are clearly supported and/or illustrated.
· Biblical integration is evident in threads and replies.
· Uses factually correct references to weekly readings,
presentations, and other scholarly sources to support comments.
30
Grammar and Mechanics
· Sentence structure is complete, clear, and concise.
· Spelling and punctuation are correct.
· Word choice is precise, unambiguous, and appropriate.
5
36. Submission
· Meets the required word limit (at least 400 words for the
thread and at least 250 words for each reply).
5
Format
· Pleasing general appearance.
· Correct and current Turabian style in internal citations and
references.
5
Total
50
CHAPTER
14
Communicating Findings
Strategic managers and entrepreneurial researchers know that
unless research findings are reported effectively nothing
happens. You may report findings at meetings, in press releases,
brochures, project reports, annual reports, and academic papers.
In this chapter we argue for clear, focused presentations tailored
to the needs of a particular audience. At the end of the chapter
we touch on ethical concerns that occur in connection with
reporting data.
VARIATIONS IN AUDIENCES AND THEIR NEEDS
Whether you plan to make an oral presentation or write a report,
the first steps are to focus on your purpose and the
characteristics of the intended audience(s). First, you need to
get the audience’s attention. Virtually everyone is overloaded
37. with information. Effective administrators and policy makers
may be particularly adept at protecting their time and ignoring
information that they do not need or want. Second, you want to
prevent having the listeners or readers miss your main point. If
you aren’t clear and don’t get their attention they can
misunderstand or ignore important findings. Third, you may
want to teach your audience members something, influence their
thinking, or motivate them to act.
Identifying the audience for an oral presentation is
straightforward. Your first question should be “why will people
attend the presentation?” Once you have the answer you can
tailor the presentation to the audience’s concerns, its level of
knowledge, and its motivation to act. Identifying potential
readers is more difficult. A report may be passed on to
supervisors, staff, agency analysts, interest-group members,
professional acquaintances, legislators, or students. Reports
may be placed in an agency library or posted on a Web site. To
satisfy diverse readers, reports must be clearly written and
research procedures should be fully documented. On the other
hand, including full details, especially about the methodology,
can diminish a report’s readability or an audience’s attention.
You can resolve this apparent conflict by putting important
information, such as the report’s findings and recommendations,
first and placing complicated or technical details in footnotes or
appendices. You may also direct an audience to Web sites or
other easily accessed sources for additional information.
If you are conducting a study for an organization that you don’t
work for, you may want to learn how it normally organizes and
presents information. You may attend oral briefings or ask
potential audience members to identify presentations that they
thought were especially effective. You can use this information
to infer what features generate audience interest and
involvement. When you read reports, save ones that seem
particularly well done.
ORAL PRESENTATIONS OF RESEARCH FINDINGS
No matter whether you see yourself as a strategic manager or an
38. analyst, you need to hone your oral presentation skills. As
teachers we have observed talented students who avoid making
oral presentations. These students lose valuable opportunities to
practice presenting their ideas, listening to others, and phrasing
and answering questions. Whether you normally speak to one
person, a small group, or a large, formal audience, your ability
to explain your work clearly will serve you well. Professionals
who feel pressed for time may prefer to hear about a study
rather than read through a report. Some people are “oral
learners,” that is, they efficiently absorb and understand
information they hear. Others value debating information and
discussing it with investigators and colleagues.
An oral presentation provides an excellent opportunity to have
an impact. Audience members may feel compelled to pay
attention. Their interactions may motivate the group or
individuals to discuss and follow up on the findings. You can
prepare by asking yourself, “Why will people attend this
presentation? What do I want them to learn? What action do I
want them to take? How can I convince them to take it?” The
answers should guide how you organize a presentation.
You should not discount the importance of one-on-one informal
discussions of your research. Their informality can be
deceptive—don’t overlook the opportunities they provide. They
offer an important occasion for others to develop interest in
your project. What would you say if your agency head were to
ask what you are working on? Wouldn’t you want to generate
interest in your current project? Wouldn’t you want to lay the
groundwork for a decision based on your findings? A trick that
some researchers employ is to prepare an “elevator speech.”
That is, they prepare a very short description of what they are
working on that is short enough to be said during an elevator
ride. You may never be caught in an elevator with a person you
want to impress, but if you are prepared you will not waste an
unexpected opportunity to sell your project.
An effective presentation requires planning and practice. Select
the points you want to emphasize, the evidence you will use to
39. support these points, the order in which the information will be
presented, and visual aids. The traditional order for a research
presentation—background, methodology, findings, and
discussion—usually works well. It develops the material
logically. People with training in the sciences, including the
social and behavioral sciences, have come to expect it. If
audience members are already informed about the program or
policy, identifying the study’s purpose may be sufficient.
Otherwise, you should describe the program or policy to put the
information in context and to help audience members follow the
presentation. Usually a brief discussion of the methodology is
sufficient. Except for specialized audiences, you can skip the
technical details. Remember that while you have learned to pay
careful attention to detail and to examine findings from various
perspectives, these skills can translate into tedious, unfocused
presentations. Avoid trying to cover too much information.
Instead concentrate on a few important points and encourage the
audience to ask about the details, especially those details that
may affect their willingness to accept the findings.
Visual aids may be used throughout a presentation. PowerPoint
slides, tables, or graphs focus the presenter and the audience.
To select a visual aid, consider whether it communicates the
information clearly and effectively, requires special equipment,
or slows down the presentation. Too many visuals can bore an
audience. Detailed tables and graphics leave people in the back
rows squinting or feeling left out. Wordy slides focus the
audience on trying to decipher the slide instead of listening to
you.
The slide shown in Figure 14.1 is from a presentation on the
legislative history of a U.S. health care policy. You might
rightly point out the slide is too wordy, but even more
confusing is that it lacks coherence. We might start our revision
by focusing only on the Social Security Act and Wagner-
Murray-Dingell Bill. We would give the year of the Wagner-
Murray-Dingell bill. The citations probably could be deleted
from the slide. Figure 14.2 shows an improved version. Using
40. PowerPoint you might try your hand at further improving the
slide in Figure 14.1.
If your presentation contains a number of tables or slides, or if
you expect the audience to take notes from the PowerPoints,
you should prepare a handout containing the same information.
Alternately you may post the slides on a Web site or e-mail
them to participants.
Presentations with lively graphics can be fun to put together,
but make sure that they don’t draw attention away from the
presentation’s content. Similarly, fumbling around with
unfamiliar equipment creates a serious distraction.
Inexperienced presenters may overlook the importance of
practice. A researcher who has poured over a study may feel
confident in her ability to ad lib the presentation.
Unfortunately, she may bog down on the study’s minutiae or
move erratically from point to point. Typically, one should
practice with an audience of colleagues, team members, or
friends. Practice-session observers should make sure that the
major points are clearly presented, the statement of key points
does not become repetitious or condescending, the transitions
are smooth, and the equipment operates correctly. The observers
should ask questions about the methodology and the
interpretation of the findings. Preparing answers to “hard”
questions avoids the embarrassment of stumbling around during
the actual presentation. If questions challenging the credibility
of the study or its findings go unanswered or are poorly
answered, the written report may never be read and its potential
impact may be undermined.
FIGURE 14.1
An Ineffective Slide
FIGURE 14.2
A More Effective Slide.
41. WRITTEN PRESENTATIONS
A written research report should cover the study’s purpose,
relevant background articles and reports, its methodology,
findings, discussion, and recommendations. An executive
summary, a report summary that goes at the beginning of the
report, is actually the last part of the report you write. The
written report should be a permanent record of what was done,
why and how it was done, and what was found. Although the
number of people who actually read the report may be small,
this written record remains and may be available to all
interested parties.
Research findings may be summarized on handouts and Web
sites, in brochures or press releases. Typically, to prepare such
summaries you extract material from the research report.
Readers of summaries miss the details they need to judge the
credibility of the findings, to pick up information that may be
pertinent to them, or to justify making a decision or taking
action. However, the summaries may be the only part of the
report that some policy makers and others read. The executive
summary provides an overview of the important aspects of the
research report. It often is included as the first part of the
research report and can also be used as a separate document. We
first discuss the structure and content of the research report and
then discuss the executive summary.
Background Information
You should begin the report by identifying the question you are
asking and the value of answering it. As appropriate, a report
may discuss the program or policy’s origins, implementation
history, goals, relevant stakeholders, resources, and activities.
The specific information included depends on the report’s
audience and its purpose.
To develop the background information, you may cite
interviews, documents, and the research literature. You are most
likely to include a formal literature review in program
evaluations. The literature may justify the study’s design, the
variables you chose, the relationships you examined, and how
42. you interpreted the findings. You may present previous research
in chronological order or you may organize the discussion
around key variables or concepts. You may weave information
from the literature into the background presentation, assign it to
an appendix, or include it in an annotated bibliography.
Methodology Section
The final project report should be comprehensive enough for
others to use the report, verify its findings, or replicate the
research. The methodology section is key to providing this
information. At a minimum it should have enough detail so that
readers can decide if the findings are credible and policy
makers can use them as evidence. You should discuss how you
defined and measured the study’s variables, any intervention
you introduced, your sample, when you collected the data, and
how often you collected them.
If your study design was an experiment or a quasi-experiment
you should describe the intervention, the study population, and
how you created the study group(s). Your goal is to provide
sufficient information on the design and its implementation so
readers can assess the study’s internal validity, and subsequent
investigators and policy makers can assess how the findings
might apply in other settings. When reporting performance
measures or survey results you can limit the methodological
discussion to writing about the measures and samples.
In the measurement section you should identify the operational
definitions, how you categorized or assigned numerical values,
how you grouped values and combined variables to create
indicators, and evidence supporting the reliability and
operational validity of the measures. Customarily, in
quantitative studies researchers report only the findings from
mathematical tests of reliability and empirical evidence of
operational validity. To illustrate what is included we give a
hypothetical example of how to report the operational
definition. If an analyst divided the scale into categories, such
as high trust, somewhat trustful, and low trust, she could
include the information in a footnote.
43. REPORTING ON A MEASURE
To measure trust we asked respondents to rate the following
statements using a 7-point scale where 1 = strongly disagree and
7 = strongly agree; the alpha coefficient was .88.
• The people who represent the funder are trustworthy.
• My organization can count on the funder to meet its
obligations to the program.
• My organization feels it worthwhile to continue to work with
the funder. ■
In the discussion of the sample you should identify the target
population, sampling frame, sampling design, response rate, and
when the data were collected. To avoid ambiguity, you should
report the initial sample size, how many members of the sample
were contacted, how many of those contacted belonged to the
target population, how many refused to provide data, and how
many supplied incomplete data. If possible you should compare
respondents and nonrespondents. Any other sources of
nonsampling error should be identified.
Findings
Whether you are writing a report or preparing an oral
presentation the key considerations of how to present your
findings are the same. You need to (1) organize the findings
into a coherent presentation, (2) focus on the important findings
and avoid overwhelming the audience with unnecessary detail,
and (3) decide on how to present the data. Presented with an
uninteresting analysis or an overwhelming amount of detail,
audience members may stop listening or reading.
Your graphs and tables should complement the verbal
presentation and exhibit data efficiently. Attractive graphics
and clear explanations allow readers to assess the richness of
the data. The location of graphics and explanations and the
amount of space devoted to them signal the importance of the
information they contain. You should not waste space on
graphics that illustrate unimportant or trivial findings; they do
not deserve major emphasis.
Tables may show exact numerical values. They are effective
44. when you want to encourage many specific comparisons. Graphs
are especially effective for time series and to make simple
comparisons. They permit an audience: to pick out long-term
trends, cycles, and seasonal fluctuations; to compare different
groups or organizations; and to see differences before and after
an intervention.
You should take care to avoid ambiguous labels. Spell words
out and avoid abbreviations. The following summarizes
practices associated with constructing effective tables and
graphs.
1. Tables or graphs should have a precise, descriptive title. A
title may list by name the dependent variable by independent
variable by control variable (if any). Alternatively, a title may
summarize a major finding supported by a graphic, for example,
“City homicide rates have dropped over the past twenty years.”
All variables and their corresponding categories should be
clearly labeled and appropriate units (e.g., years) should be
indicated.
2. The independent variable normally heads the columns of a
table and the dependent variable heads the rows.
3. If percents are used, the percent sign (%) should be entered
at the top of each column.
4. The number of cases on which percent figures are based
should be indicated. The total number of cases used in the
analysis also should be indicated.
5. Statistical measures, if any, should be placed at the bottom
of the table.
6. Definitions of key terms should appear as a table or graph
footnote.
7. Data source(s) should be identified in the table or graph’s
footnote.
8. A good table supplements, not duplicates, the text. The table
and its data should be referred to in the text, but you need to
discuss only the highlights. As well, tables and graphs should
be able to stand alone, that is, readers should be able to grasp
the essential information without referring to the text.
45. 9. As you work on preparing tables and graphs, remember to
date them; you may even want to note the time. This is because
as you analyze the data you may note and correct errors and you
may decide on a different, more effective way to group your
data. Unless your graphs or tables are dated you may not
remember which represents the most recent version.
Discussion
In the findings section you report objective, verifiable
information. In the findings section you organize and present
the quantitative and qualitative data; in the discussion section
you discuss what you observed about the information and
interpret the findings. You may note
■ what seems important;
■ how the findings compare with the literature or stakeholders’
perceptions;
■ findings that were unexpected and your thoughts about why
they occurred;
■ implications of the findings for policy making, action, or
further research.
Recommendations
Program evaluations, policy analyses, and other studies done for
a legislative or administrative body may include
recommendations. Recommendations are normative statements
about changes that should be made in the program or policy.
Although you may feel ill-equipped or uncomfortable in making
normative statements or telling clients what they should do, the
study’s sponsors may expect recommendations.
You may find that recommendations focus decision makers on
what needs to be done and increase the utilization of your
findings. Recommendations should naturally follow from the
research findings, that is, a reader should be able to figure out
from the report’s content why the recommendations were made.
In making recommendations you should address only those
changes the agency can make; for example, recommending a
change in federal program requirements will not be of any value
to a local social service agency. In some cases the costs and
46. benefits of adopting a recommendation may be identified and
included. Alternatively, you may suggest several options for
agencies to consider.
Executive Summary
The executive summary highlights a report’s content. The
intended audience is the executive who has little time to read
complete reports. An executive summary is also useful for many
different audiences. Busy administrators and policy makers scan
an executive summary to decide if and when to read the entire
report or to refer it to an associate. Administrators with a
limited interest in the topic skim a summary to keep themselves
current. Policy actors may distribute summaries to communicate
and endorse the report’s findings. Investigators doing literature
reviews can infer if the report is relevant and if they should
read it.
Although usually included at the front of the research report,
the executive summary is the last part of a report to be written.
It includes only information contained in the report, but it can
be read and understood independently of the report. In writing
an executive summary, you decide what you want a reader to
know. For example, you may visualize the impatient
administrator who asks “What’s the headline?” You can go
through the report and find sentences that concisely describe
why the study was done, who the subjects were, how the data
were collected, major limitations in the methodology or its
implementation, and what the major findings were. You should
include any recommendations that were part of the report.
You should use clear direct sentences and visual cues to allow
an individual to read the summary quickly. Keep its length and
degree of detail consistent with the length and complexity of the
report, agency expectations, and the importance of the findings.
In preparing an executive summary, you should avoid including
too many details; otherwise, its benefits are defeated. The
following sample executive summary may serve as a model of
how to organize and summarize a report.
ETHICAL ISSUES
47. Completing a research project, presenting findings, and storing
information have ethical dimensions. A joint committee
convened by the National Academy of Sciences, the National
Academy of Engineering, and the Institute of Medicine
identified three sets of ethical issues: research misconduct,
questionable research practices, and other
misconduct.1 Research misconduct consists of acts of
fabrication, falsification, or plagiarism. Questionable practices
refer to decisions with regard to data retention and sharing,
record quality, authorship, statistical analysis, and release of
information. Other misconduct refers to acts that are
unacceptable but not unique to researchers—for example,
misuse of funds, vandalism, violations of government research
regulations, and conflicts of interest. In this section we focus on
research misconduct, handling research errors, and record-
keeping issues as these are ones that you are most likely to
encounter in the course of your work.
Research Misconduct
Fabrication is defined as making up data or results, and
falsification as changing data or results. We assume that you
know that fabrication is wrong. Falsification can be a bit more
ambiguous. An easy way to falsify results is to drop cases from
a dataset. Dropping selected cases can strengthen your
statistical evidence or even rescue a weak statistical model. For
example, you might eliminate cases that you think have
measurement errors, such as when you suspect that incorrect
data were reported. If you think that measurement error
occurred, you should try to confirm it. However, if you cannot
confirm the error you may decide to remove the cases, in which
case you must report that you removed the cases, explain why
you removed the cases, and indicate how their elimination
affected the results. The greater the effect, the more diligent
you must be in reporting the decision. A decision that markedly
affects the findings should not be buried in fine print.
AN EXECUTIVE SUMMARY
A Program Evaluation of the Vocational Training Programs at
48. Portal
Portal [pseudonym] is a community-based rehabilitation facility
whose mission is “to help people with vocational disabilities
achieve a sense of self-worth by optimizing their potential to
earn their own wages through work.”The facility has two
programs to provide persons with disabilities vocational
training suited to their needs and abilities.
Study Questions
■ How successful are Portal trainees in obtaining permanent
jobs?
■ What characteristics are associated with successful job
placement?
■ Is one of the training programs more successful than the
other?
Findings
A customer satisfaction survey of trainees of both programs
indicated that 22 of the 23 contacted were satisfied with the
services they received; and were currently working in
permanent positions.
An examination of Portal’s databases found that based on case
closure its success rate has declined in the past 3 years. In the
first year it had 100 percent successes as compared to the state
average of 87.2 percent. By the third year it had 81.3 percent
successes as compared to the state average of 82.7 percent. The
decrease may be attributed to a change in definition of the
term success, which no longer considers probationary
employment as a success. Analysis disclosed no differences in
race, sex, or disability of clients in either program between
those who were successful and those who were unsuccessful.
Inconsistencies in Portal’s databases limited the evaluators’
ability to find information and clarify definitions. A substantial
amount of useful information was missing from the databases.
Recommendations
■ Developing a centralized database for the entire agency with
clear definitions of database fields, leaving little need for
interpretation of information by data entry staff.
49. ■ Collecting more information on processes that could lead to
better service for clients and employers.
■ Connecting billing sheets to database to monitor hours of job
development and job coaching for each client.
■ Surveying or interviewing clients who have been placed in a
permanent job to monitor their long-term success. ■
You need not be overly concerned about avoiding charges of
falsification. You simply need to be careful in documenting
your decisions and why you made them. The documentation
makes your decisions accessible for peer review.
Plagiarism is falsely presenting another’s ideas or words as
one’s own. Quoted material should be placed in quotation marks
and references cited. Closely following another author’s diction
is wrong. You should either use your own words and sentence
structure or quote directly from your sources. Relying on the
works of others is inevitable in research. No one knows this
better than a textbook writer. We have referenced sources that
we relied on to write segments of this text or that provided a
unique or valuable perspective on the material. We have not
referenced sources for ideas and perspectives that we know are
part of the common knowledge of social science researchers.
Diligent referencing and use of your own words should be
adequate to avoid charges of plagiarism. In our experience, the
most common instances of plagiarism are using information
from Web sites without citing sources, or editing and presenting
another’s work as one’s own. Changing words and dropping
sentences also constitutes plagiarism. If a report is to be
published, you need to pay attention to copyright laws. You
must get permission from the copyright holder to reproduce
graphs, tables, long quotes, and other materials, including song
lyrics, poetry, and cartoons. However, government documents
are not covered by copyright, and their contents can be
reproduced without obtaining permission. Nevertheless, you
should use standard referencing procedures to cite a government
document.
Handling Research Errors
50. Error is inevitable in research. The joint committee convened by
the National Academy of Sciences, the National Academy of
Engineering, and the Institute of Medicine identified four
potential sources of error: the accuracy and precision of
measurements, the generalizability of experiments, the quality
of the experimental design, and the interpretation of the
practical significance of the findings. To reduce the diffusion of
incorrect knowledge, you should fully disclose your research
procedures, and acknowledge and correct errors.
Full disclosure allows others to scrutinize the research. They
may find errors by examining the research documents or by
attempting to replicate the research. Concealing limitations in
your methodology amounts to deception. Research reports
should clearly identify and evaluate the limitations. In fact, the
more troublesome a limitation, the more emphasis it should
receive.
Complete information on research procedures can overwhelm
readers with details and seriously diminish a report’s
effectiveness. The professional standards for program
evaluation may serve as a useful guide. To provide useful
information, the standards advise evaluators to write clearly,
present information that their audiences can understand, and
indicate the relative importance of their findings and
recommendations. To achieve full disclosure, the standards
advise evaluators to state their assumptions, their constraints,
and how readers may obtain full information on research
procedures, including data analysis.2 The standards relieve
evaluators of the burden of providing complete research
information in every report, but they must take reasonable
actions to ensure the accessibility of the database and
documentation.
SAVING DATA
Data must be saved and be accessible to allow research audits,
replication of results, refinement of the analysis, additional
analyses, or incorporation of data into other research designs
Research data include completed data collection instruments,
51. protocols for collecting and entering data, descriptions of
experimental procedures, data files, computer printouts, field
notes, videotapes, audio tapes, CDs, and DVDs. With this
information the research can be reconstructed or replicated.
Audits may be a component of ensuring integrity; auditors can
investigate charges of falsification or fabrication.
Misunderstandings can be avoided if you and other involved
parties agree on who will retain the data, how long they will be
kept, and the conditions governing …