Aug 28th
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The document outlines learning objectives and homework assignments related to organizing and analyzing national statistics data. Students are asked to define income inequality, create a histogram of income inequality data with specified window values, determine the mean and median of the data and describe its shape. They are further tasked with identifying US income inequality based on the histogram and calculating how many countries have better or worse income inequality compared to the US.
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---Quantitative Project World Income and Health Inequality.docx
---
Quantitative Project: World Income and Health Inequality
Based on what we have discussed so far, it seems that
there
is
a lot of variation around the world in terms of income, wealth, education,
health
status, and many other characteristics. And these characteristics seem to be related
with
one another. For example, people
from
wealthier countries tend to live longer. In this project, you are asked to
use
international data to empirically investigate the relationship between
income
and health status. The following
sections
provide a general description of this project and raise questions that
you
need to answer.
Objectives:
A. Substantive
: Students will
be
able to
1.
investigate
world inequality in income.
2.
investigate
world inequality in health
status
.
3.
investigate
the relationship between income and
health
status.
B.
Quantitative Skills
: Students will be able to
1.
sort
a single variable and examine
its
distribution
2.
calculate
within-group adjusted-means
weighted
by populations
3.
produce
a scatter plot to investigate the
relationship
between two variables
Data and Variables
The data are from “2008 World Population Data Sheet” published by the Population Reference Bureau (
http://prb.org/Publications/Datasheets.aspx
).
Three
variables
are used for this project:
Gross National Income (GNI) PPP per capita
Life
expectancy
Population (
in
millions)
These three variables for more
than
100 countries are already compiled in an Excel file.
Validity of the Measurement
Income level
Q_1
: Why can’t Gross National Income be directly used as a
measure
of income level? What does the PPP adjustment
take
into account? Why has it to be per capita?
Health Status
Q_2
: How is life expectancy defined? Why not to use Crude
Death Rate (CDR)? What is the advantage of using life
expectancy
?
Data Analysis
Corresponding to the three
objectives
stated above, the analysis section is composed of the following
three
parts:
1. Investigation of income inequality between rich and poor countries
Q_3
: Find out the top five countries with the highest GNI PPP per
capita
and
the bottom five countries with the
lowest
values. List these
countries’
names and their income.
Q_4
: How much is the difference between the highest and lowest
country
?
Q_5
: If we want to find out the overall difference between these
two
groups
, can we
simply
take an average of the five values of GNI PPP
per
capita within each group and
compare
the two means? Why or
why
not?
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population
-weighted means. We first need
to
calculate the total income for each country by multiplying GNI PPP per
capita
by its population. Then, add
all
five
total income within each group. Finally.
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---Quantitative Project World Income and Health Inequality.docx
---
Quantitative Project: World Income and Health Inequality
Based on what we have discussed so far, it seems that
there
is
a lot of variation around the world in terms of income, wealth, education,
health
status, and many other characteristics. And these characteristics seem to be related
with
one another. For example, people
from
wealthier countries tend to live longer. In this project, you are asked to
use
international data to empirically investigate the relationship between
income
and health status. The following
sections
provide a general description of this project and raise questions that
you
need to answer.
Objectives:
A. Substantive
: Students will
be
able to
1.
investigate
world inequality in income.
2.
investigate
world inequality in health
status
.
3.
investigate
the relationship between income and
health
status.
B.
Quantitative Skills
: Students will be able to
1.
sort
a single variable and examine
its
distribution
2.
calculate
within-group adjusted-means
weighted
by populations
3.
produce
a scatter plot to investigate the
relationship
between two variables
Data and Variables
The data are from “2008 World Population Data Sheet” published by the Population Reference Bureau (
http://prb.org/Publications/Datasheets.aspx
).
Three
variables
are used for this project:
Gross National Income (GNI) PPP per capita
Life
expectancy
Population (
in
millions)
These three variables for more
than
100 countries are already compiled in an Excel file.
Validity of the Measurement
Income level
Q_1
: Why can’t Gross National Income be directly used as a
measure
of income level? What does the PPP adjustment
take
into account? Why has it to be per capita?
Health Status
Q_2
: How is life expectancy defined? Why not to use Crude
Death Rate (CDR)? What is the advantage of using life
expectancy
?
Data Analysis
Corresponding to the three
objectives
stated above, the analysis section is composed of the following
three
parts:
1. Investigation of income inequality between rich and poor countries
Q_3
: Find out the top five countries with the highest GNI PPP per
capita
and
the bottom five countries with the
lowest
values. List these
countries’
names and their income.
Q_4
: How much is the difference between the highest and lowest
country
?
Q_5
: If we want to find out the overall difference between these
two
groups
, can we
simply
take an average of the five values of GNI PPP
per
capita within each group and
compare
the two means? Why or
why
not?
A better way is to compare the
population
-weighted means. We first need
to
calculate the total income for each country by multiplying GNI PPP per
capita
by its population. Then, add
all
five
total income within each group. Finally.
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used to explain statistical concepts. It does not
attempt to be exhaustive, but rather, provide a few
examples for selected concepts based on what students
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UNITED STATES
fill in country name
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What is the Crude Death Rate?
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What is the infant mortality rate?
What is the Total Fertility Rate (FTR)?
What is the growth rate today?
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Repeat your for second country
UNITED STATES
fill in country name
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% of population
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% of population
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2. What do you think are the ethical issues involved in the scenario? Explain.
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7. How significant is it to you that a superior officer is implying that you should make an unethical decision? Explain.
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Total Percentage
100
New York Times
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Id
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Works full-time
Annual income cat
1
Did not grad HS
Yes
Low
2
HS dip
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Low
3
HS dip
No
Med
4
BA
No
Low
5
BA
Yes
Med
6
MA
Yes
High
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HS dip
Yes
Med
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b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For eachvariable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement?
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
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Id
num_bdrms
num_bthrms
sqft
Ranch
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4
3
3200
Yes
2
2
1.5
2800
Yes
3
2
1
1200
Yes
4
3
2
1500
No
5
2
2
1100
No
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For each variable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement? Before answering, be sure to consult the slide called “Level of measurement – language to use”. Use the formal language!
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
3. For each of the following questions (1) construct a dataset with one variable and three observations (2) add data that could have theoretically been collected (just make up the actual responses to the question); and (3) indicate the level-of-measurement of the variable. I’ve done two examples for you.
Example#1:
What is your current age? (individual is the unit-of-analysis)
idage
1 25
2 32
3 61
The age variable is continuous/interval ratio.
Example#2:
What is the size of this hospital based on number of beds? (hospital is the unit-of-analysis)? Answers can be small (1-100 beds), medium (101-500 beds), large (501 beds to 1000 beds), extra large (1001+ beds)
idhosp_size
1 med
2 med
3 ext ...
« PreviousHomeNext »Home » Measurement » Levels of Measure.docx
« PreviousHomeNext »
Home » Measurement » Levels of Measurement
The level of measurement refers to the relationship among the values that are assigned
to the attributes for a variable. What does that mean? Begin with the idea of the
variable, in this example "party affiliation." That variable has a number of attributes. Let's
assume that in this particular election context the only relevant attributes are
"republican", "democrat", and "independent". For purposes of
analyzing the results of this variable, we arbitrarily assign the values 1, 2 and 3 to the
three attributes. The level of measurement describes the relationship among
these three values. In this case, we simply are using the numbers as shorter placeholders
for the lengthier text terms. We don't assume that higher values mean "more" of
something and lower numbers signify "less". We don't assume the the value of 2
means that democrats are twice something that republicans are. We don't assume that
republicans are in first place or have the highest priority just because they have the
value of 1. In this case, we only use the values as a shorter name for the attribute.
Here, we would describe the level of measurement as "nominal".
Why is Level of Measurement Important?
First, knowing the level of measurement helps you decide how to interpret the data from
that variable. When you know that a measure is nominal (like the one just described), then
you know that the numerical values are just short codes for the longer names. Second,
knowing the level of measurement helps you decide what statistical analysis is appropriate
on the values that were assigned. If a measure is nominal, then you know that you would
never average the data values or do a t-test on the data.
There are typically four levels of measurement that are defined: Nominal Ordinal Interval Ratio
In nominal measurement the numerical values just "name" the attribute
uniquely. No ordering of the cases is implied. For example, jersey numbers in basketball
are measures at the nominal level. A player with number 30 is not more of anything than a
player with number 15, and is certainly not twice whatever number 15 is.
In ordinal measurement the attributes can be rank-ordered. Here, distances
between attributes do not have any meaning. For example, on a survey you might code
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Linear day 17
The document outlines a lesson plan for October 9th with the learning goal of solving linear inequalities. The plan includes a launch activity to introduce solving inequalities followed by an explore activity to further practice solving inequalities, and concludes with a summary worksheet problem.
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Presenting data using_social_math
This document discusses how to effectively communicate large numbers and statistics to non-expert audiences. It explains that simply stating large numbers does not usually inform people or help them understand the significance. Instead, it recommends using "social math" which involves placing large numbers in a social context to make them more comprehensible and meaningful. Some techniques for social math include breaking numbers down by time, place, or local references, comparing them to familiar things, and using ironic comparisons. The document provides examples for how to apply these techniques and encourages the reader to practice social math with their own data.
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MDC and LDC Populations Worksheet
Demography is the statistical study of human populations, especially with reference to size, density, distribution, and vital statistics (relating to births, deaths, marriages, health and disease, etc). In making population projections for different countries, demographers look at the profile of the countries’ residents. They ask: What are the ages of the people? How many are men? How many are women? Using this information, they construct “population pyramids” (a.k.a. age histograms) like the ones the class will use in this activity. These graphs illustrate the configuration of a country’s population as shaped by 70 to 80 years of economic, political and natural events.
Procedure:
You will find information about the populations of two countries; the United States and one other country picked from a list on the last page.
Countries Assigned = USA and ____________________________
Log onto the web and go to International Data Base (IDB) part of the www.census.gov site
(The URL is https://www.census.gov/data-tools/demo/idb/#/table?YR_ANIM=2021)
1. For the report tab, select “Custom Report”.
2. In custom report columns tab select all data in the tables below.
3. In the select by County/Area tab, select the Country.
4. Use the data for this year to answer the questions on this page below.
5. In the resulting webpage, click on your Country’s name to access the population pyramid graph.
6. Right click on the graph so you can copy it and then past it at the end of this worksheet.
7. Repeat for your second country.
Using the information from the internet for this year, fill out the tables for both of your countries
UNITED STATES
fill in country name
What is the Crude Birth Rate?
What is the Crude Death Rate?
What is the life expectancy at birth?
What is the infant mortality rate?
What is the Total Fertility Rate (FTR)?
What is the growth rate today?
What is the doubling time for the population?
(You will have to work this out so look at the population lecture!)
Show your math work!
Still on the International Data Base (IDB) site
(The URL is https://www.census.gov/data-tools/demo/idb/#/table?YR_ANIM=2021)
1. To obtain the population by age group, scroll/mouse over the population pyramid. The population totals for each 5-year group will appear.
2. Add up the numbers in each group to comprise the three categories below. Once you have the population size of each category you can calculate the % of the population made up by that age group using the following calculation:
3. (Population size for the age group ÷ total population size) X 100 = % of population
4. Calculate this information and add the results to complete the table below.
Repeat your for second country
UNITED STATES
fill in country name
Age Group
Population size for both Sexes
% of population
Population size for both Sexes
% of population
0-14
Pre-Reproductive
Pre-Reproductive
15-44
Re ...
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Scenario:
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You have been advised by the chief that all he wants is results, not excuses. He wants you to use whatever means are necessary to apprehend fugitives because anything less would reflect badly on the department and his leadership. He reminds you that he has the firm backing of the mayor and city commission in how he runs the department.
The next day, a news reporter informs you that he is working on a story regarding the apprehension of child rapist. Information he has gathered indicates that the arresting officers on the team, under your supervision, may have used questionable methods during the apprehension, which resulted in significant injuries to the individual. He asks for you to comment on the potential violation, and you inform him that you will look into the matter and get back with him later.
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Ethics and Police Administration
Respond to the given scenario in 500-600 words addressing the following 8 questions
Due March 5th
Primary Task Response: Write 500–600 words that respond to the following questions with your thoughts, ideas, and comments. Be substantive and clear, and use examples to reinforce your ideas:
1. What do you think are the legal issues involved in the scenario? Explain.
2. What do you think are the ethical issues involved in the scenario? Explain.
3. What are the possible consequences of not addressing these ethical issues? Explain.
4. Considering the directive given to you by your chief that he wants results and not excuses, what are some of the factors that you should take into consideration?
5. How would you respond to the follow-up questions from the reporter? Why?
6. What will most likely result from your responses, and how will you protect yourself and your career? Explain.
7. How significant is it to you that a superior officer is implying that you should make an unethical decision? Explain.
8. How did this affect what you would say to the reporter? Explain.
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2017 World Happiness Report Data Analysis
Analysis of the factors that affect the World happiness score and investigation of possible correlations with Python.
Introduction to Statistics
This document provides an introduction to statistics. It discusses why statistics is important and required for many programs. Reasons include the prevalence of numerical data in daily life, the use of statistical techniques to make decisions that affect people, and the need to understand how data is used to make informed decisions. The document also defines key statistical concepts such as population, parameter, sample, statistic, descriptive statistics, inferential statistics, variables, and different types of variables.
How to understand basic statistics
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RTI Overview 20.0 Includes an RTI overview that is comprehensi.docx
RTI Overview
20.0
Includes an RTI overview that is comprehensive and includes a thorough explanation of the RTI tiers.
Tier Placement
20.0
Includes a thorough and insightful explanation of what factors determine appropriate student placement within the RTI tiers.
RTI and Individuals with Disabilities
15.0
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Intervention Strategies
15.0
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Presentation
10.0
The work is well presented. The overall appearance is neat and professional. Work would be highly desirable for public dissemination.
Research
5.0
Research strongly supports the information presented. Sources are timely, distinctive and clearly address all of the criteria stated in the assignment.
Language Use and Audience Awareness
5.0
Word choice in slides and speaker's notes is distinctive, creative and well-suited to purpose, discipline, scope, and audience of the presentation.
Mechanics of Writing (includes spelling, punctuation, grammar, and language use)
5.0
Submission is virtually free of mechanical errors. Word choice reflects well-developed use of practice and content-related language.
Documentation of Sources (citations, footnotes, references, bibliography, etc., as appropriate to assignment and style)
5.0
Sources are completely and correctly documented, as appropriate to assignment and style, and format is free of error.
Total Percentage
100
New York Times
“The American Dream, Quantified at Last”
By David Leonhardt
Dec. 8, 2016
The phrase “American dream” was invented during the Great Depression. It comes from a popular 1931 book by the historian James Truslow Adams, who defined it as “that dream of a land in which life should be better and richer and fuller for everyone.”
In the decades that followed, the dream became a reality. Thanks to rapid, widely shared economic growth, nearly all children grew up to achieve the most basic definition of a better life — earning more money and enjoying higher living standards than their parents had.
These days, people are arguably more worried about the American dream than at any point since the Depression. But there has been no real measure of it, despite all of the data available. No one has known how many Americans are more affluent than their parents were — and how the number has changed.
It’s a thorny research question, because it requires tracking individual families over time rather than (as most economic statistics do) taking one-time snapshots of the country.
The beginnings of a breakthrough came several years ago, when a team of economists led by Raj Chetty received access to millions of tax records that stretched over decades. The records were anonymous and came with stric.
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Session 8 a clark discussionzellipittau
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The Demographic Transition Theory
The Brisbane Good Food and Wine Show attracts a wide range of patrons, with the majority being females between 25-54 years old who have a relatively high income and education levels. Many patrons attend the event to experience new flavors, foods, and culinary traditions from around the world. Geographically, most patrons come from Brisbane and the surrounding areas to enjoy the local and international cuisine offerings at the annual show.
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docx
Homework #1
SOCY 3115
Spring 20
Read the Syllabus and FAQ on how to do your homework before beginning the assignment!
To get consideration for full credit, you must:
· Follow directions;
· Show all work required to arrive at answer (statistical calculations often require multiple steps, so you need to write these down, not just skip to the final answer)
· Use appropriate statistical notation at all times (e.g. if you are calculating a population mean, begin with the equation for population mean)
· Use units in your answer, where appropriate (e.g. a mean time would be “6.5 hours” rather than just “6.5”)
Understanding the Structure of Data
1. For the following rectangular dataset:
Id
Highest degree
Works full-time
Annual income cat
1
Did not grad HS
Yes
Low
2
HS dip
Yes
Low
3
HS dip
No
Med
4
BA
No
Low
5
BA
Yes
Med
6
MA
Yes
High
7
HS dip
Yes
Med
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For eachvariable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement?
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
2. For the following rectangular dataset:
Id
num_bdrms
num_bthrms
sqft
Ranch
1
4
3
3200
Yes
2
2
1.5
2800
Yes
3
2
1
1200
Yes
4
3
2
1500
No
5
2
2
1100
No
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For each variable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement? Before answering, be sure to consult the slide called “Level of measurement – language to use”. Use the formal language!
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
3. For each of the following questions (1) construct a dataset with one variable and three observations (2) add data that could have theoretically been collected (just make up the actual responses to the question); and (3) indicate the level-of-measurement of the variable. I’ve done two examples for you.
Example#1:
What is your current age? (individual is the unit-of-analysis)
idage
1 25
2 32
3 61
The age variable is continuous/interval ratio.
Example#2:
What is the size of this hospital based on number of beds? (hospital is the unit-of-analysis)? Answers can be small (1-100 beds), medium (101-500 beds), large (501 beds to 1000 beds), extra large (1001+ beds)
idhosp_size
1 med
2 med
3 ext ...
« PreviousHomeNext »Home » Measurement » Levels of Measure.docx
« PreviousHomeNext »
Home » Measurement » Levels of Measurement
The level of measurement refers to the relationship among the values that are assigned
to the attributes for a variable. What does that mean? Begin with the idea of the
variable, in this example "party affiliation." That variable has a number of attributes. Let's
assume that in this particular election context the only relevant attributes are
"republican", "democrat", and "independent". For purposes of
analyzing the results of this variable, we arbitrarily assign the values 1, 2 and 3 to the
three attributes. The level of measurement describes the relationship among
these three values. In this case, we simply are using the numbers as shorter placeholders
for the lengthier text terms. We don't assume that higher values mean "more" of
something and lower numbers signify "less". We don't assume the the value of 2
means that democrats are twice something that republicans are. We don't assume that
republicans are in first place or have the highest priority just because they have the
value of 1. In this case, we only use the values as a shorter name for the attribute.
Here, we would describe the level of measurement as "nominal".
Why is Level of Measurement Important?
First, knowing the level of measurement helps you decide how to interpret the data from
that variable. When you know that a measure is nominal (like the one just described), then
you know that the numerical values are just short codes for the longer names. Second,
knowing the level of measurement helps you decide what statistical analysis is appropriate
on the values that were assigned. If a measure is nominal, then you know that you would
never average the data values or do a t-test on the data.
There are typically four levels of measurement that are defined: Nominal Ordinal Interval Ratio
In nominal measurement the numerical values just "name" the attribute
uniquely. No ordering of the cases is implied. For example, jersey numbers in basketball
are measures at the nominal level. A player with number 30 is not more of anything than a
player with number 15, and is certainly not twice whatever number 15 is.
In ordinal measurement the attributes can be rank-ordered. Here, distances
between attributes do not have any meaning. For example, on a survey you might code
Educational Attainment as 0=less than high school; 1=some high school.; 2=high school degree; 3=some college;
4=college degree; 5=post college. In this measure, higher numbers mean more education.
But is distance from 0 to 1 same as 3 to 4? Of course not. The interval between values is
not interpretable in an ordinal measure.
In interval measurement
the distance between attributes does have meaning. For example, when we measure
temperature (in Fahrenheit), the distance from 30-40 is same as distance from 70-80. The
interval between values is interpretable. Because of this, it makes sense to compute an
average of an interval variable, where it doesn't make sens ...
Focus Group Snapshot Review
This document provides insights from testing of creative content and messaging with African American participants for a research program called All of Us. Key findings include:
- The Miguel and Tallulah personas appealed most to participants and messaging around altruism and community resonated highest.
- Illustrations were preferred over photography. Younger participants gravitated more to images while older generations preferred headlines.
- Privacy and data security were the top concerns about joining the program mentioned by participants.
- After exposure to program information, 75% said they would join, including 60% of those who initially had concerns. Reasons cited included advancing research to cure diseases and addressing historical health inequities.
1.1 to 1.3
This document summarizes key concepts from the first three sections of Chapter 1 of an introductory statistics textbook. It defines important statistical terms like population, sample, parameter, and statistic. It also distinguishes between descriptive and inferential statistics, and different types of data like categorical, quantitative, discrete, and continuous data. Measurement levels like nominal, ordinal, interval, and ratio are explained. Examples are provided to illustrate the concepts.
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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