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Discussion # 1 Due Weds 08/19/21

Wk 1 Discussion 1 – Statistics [due Thurs]

Discussion Topic

Top of Form

Please refer to the resources provided on CDS Central. They are intended to help you engage effectively on the discussion board.

Due Thursday

This course provides foundational information about statistics.

Frankfort-Nachmias & Leon-Guerrero (2018) explain “statistics is a set of procedures used by social scientists to organize, summarize, and communicate numerical information. Only information represented by numbers can be the subject of statistical analysis” (p.18).

Write a 250- to 300-word response to the following:

  • How do      you plan to use what you learn in this course in your personal or      professional life?
  • What      specific information are you hoping to learn to apply in your      dissertation?

Reference: Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: SAGE Publications, Inc. 

Due Monday

Review others’ questions and using your professional experience, provide an answer or related insights in 150 words.

Copyright 2020 by University of Phoenix. All rights reserved.

Discussion # 2 Due Fri 08/20/21

Wk 1 Discussion 2 – Research [due Sat]

Discussion Topic

Top of Form

Please refer to the resources provided on CDS Central. They are intended to help you engage effectively on the discussion board.

Due Saturday 

Frankfort-Nachmias and Leon-Guerrero (2018) depict research as a five-stage iterative process (Ch. 1, Figure 1.1).

Write a 250- to 300-word response to the following:

  • How      theory informs and is informed by:
  • Asking      the research question
  • Formulating      hypotheses
  • Collecting      data
  • Analyzing      data
  • Evaluating      hypotheses

Reference: Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: SAGE Publications, Inc. 

Due Monday

Review others’ questions and using your professional experience, provide an answer or related insights in 150 words.

Copyright 2020 by University of Phoenix. All rights reserved.

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1 The What and the Why of Statistics

Chapter Learning Objectives
1. Describe the five stages of the research process
2. Define independent and dependent variables
3. Distinguish between the three levels of measurement
4. Apply descriptive and inferential statistical procedures

Are you taking statistics because it is required in your major—not because you find it
interesting? If so, you may be feeling intimidated because you associate statistics with
numbers, formulas, and abstract notations that seem inaccessible and complicated.
Perhaps you feel intimidated not only because you’re uncomfortable with math but also
because you suspect that numbers and math don’t leave room for human judgment or
have any relevance to your own personal experience. In fact, you may even question the
relevance of statistics to understanding people, social behavior, or society.

In this book, we will show you that statistics can be a lot more interesting and easy to
understand than you may have been led to believe. In fact, as we draw on your previous
knowledge and experience and relate statistics to interesting and important social issues,
you’ll begin to see that statistics is not just a course you have to take but a useful tool as
well.

There are two reasons why learning statistics may be of value to you. First, you are
constantly exposed to statistics every day of your life. Marketing surveys, voting polls, and
social research findings appear daily in the news media. By learning statistics, you will
become a sharper consumer of statistical material. Second, as a major in the social
sciences, you may be expected to read and interpret statistical information related to your
occupation or work. Even if conducting research is not a part of your work, you may still be
expected to understand and learn from other people’s research or to be able to write
reports based on statistical analyses.

Just what is statistics anyway? You may associate the word with numbers that indicate
birthrates, conviction rates, per capita income, marriage and divorce rates, and so on. But
the word statistics also refers to a set of procedures used by social scientists to organize,
summarize, and communicate numerical information. Only information represented by
numbers can be the subject of statistical analysis. Such information is called data;
researchers use statistical procedures to analyze data to answer research questions and
test theories. It is the latter usage—answering research questions and testing theories—
that this textbook explores.

Statistics A set of procedures used by social scientists to organize, summarize, and
communicate numerical information.

Data Information represented by numbers, which can be the subject of statistical analysis.

The Research Process
To give you a better idea of the role of statistics in social research, let’s start by looking at
the research process. We can think of the research process as a set of activities in which
social scientists engage so that they can answer questions, examine ideas, or test
theories.

Research process A set of activities in which social scientists engage to answer questions,
examine ideas, or test theories.

As illustrated in Figure 1.1, the research process consists of five stages:

1. Asking the research question

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2. Formulating the hypotheses
3. Collecting data
4. Analyzing data
5. Evaluating the hypotheses

Each stage affects the theory and is affected by it as well. Statistics is most closely tied to
the data analysis stage of the research process. As we will see in later chapters, statistical
analysis of the data helps researchers test the validity and accuracy of their hypotheses.

Asking Research Questions
The starting point for most research is asking a research question. Consider the following
research questions taken from a number of social science journals:

How will the Affordable Care Act influence the quality of health care?
Has support for same-sex marriage increased during the past decade?
Does race or ethnicity predict voting behavior?
What factors affect the economic mobility of female workers?

Figure 1.1 The Research Process

These are all questions that can be answered by conducting empirical research—research
based on information that can be verified by using our direct experience. To answer
research questions, we cannot rely on reasoning, speculation, moral judgment, or
subjective preference. For example, the questions “Is racial equality good for society?” and
“Is an urban lifestyle better than a rural lifestyle?” cannot be answered empirically because
the terms good and better are concerned with values, beliefs, or subjective preference and,
therefore, cannot be independently verified. One way to study these questions is by
defining good and better in terms that can be verified empirically. For example, we can
define good in terms of economic growth and better in terms of psychological well-being.
These questions could then be answered by conducting empirical research.

Empirical research Research based on evidence that can be verified by using our direct
experience.

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You may wonder how to come up with a research question. The first step is to pick a
question that interests you. If you are not sure, look around! Ideas for research problems
are all around you, from media sources to personal experience or your own intuition. Talk
to other people, write down your own observations and ideas, or learn what other social
scientists have written about.

Take, for instance, the relationship between gender and work. As a college student about
to enter the labor force, you may wonder about the similarities and differences between
women’s and men’s work experiences and about job opportunities when you graduate.
Here are some facts and observations based on research reports: In 2015, women who
were employed full time earned about $726 (in current dollars) per week on average; men
who were employed full time earned $895 (in current dollars) per week on average.1
Women’s and men’s work are also very different. Women continue to be the minority in
many of the higher ranking and higher salaried positions in professional and managerial
occupations. For example, in 2014, women made up 25.3% of architects, 16.5% of civil
engineers, 12.4% of police and sheriff’s patrol officers, and 2.4% of electricians. In
comparison, among all those employed as preschool and kindergarten teachers, 98% were
women. Among all receptionists and information clerks in 2014, 91% were women.2
Another noteworthy development in the history of labor in the United States took place in
January 2010: Women outnumbered men for the first time in the labor force by holding
50.3% of all nonfarm payroll jobs.3 These observations may prompt us to ask research
questions such as the following: How much change has there been in women’s work over
time? Are women paid, on average, less than men for the same type of work?

Learning Check 1.1

Identify one or two social science questions amenable to empirical research. You can almost bet that
you will be required to do a research project sometime in your college career.

The Role of Theory
You may have noticed that each preceding research question was expressed in terms of a
relationship. This relationship may be between two or more attributes of individuals or
groups, such as gender and income or gender segregation in the workplace and income
disparity. The relationship between attributes or characteristics of individuals and groups
lies at the heart of social scientific inquiry.

Most of us use the term theory quite casually to explain events and experiences in our
daily life. You may have a theory about why your roommate has been so nice to you lately
or why you didn’t do so well on your last exam. In a somewhat similar manner, social
scientists attempt to explain the nature of social reality. Whereas our theories about events
in our lives are commonsense explanations based on educated guesses and personal
experience, to the social scientist, a theory is a more precise explanation that is frequently
tested by conducting research.

A theory is a set of assumptions and propositions used by social scientists to explain,
predict, and understand the phenomena they study.4 The theory attempts to establish a
link between what we observe (the data) and our conceptual understanding of why certain
phenomena are related to each other in a particular way.

Theory A set of assumptions and propositions used to explain, predict, and understand social
phenomena.

For instance, suppose we wanted to understand the reasons for the income disparity
between men and women; we may wonder whether the types of jobs men and women
have and the organizations in which they work have something to do with their wages.
One explanation for gender wage inequality is gender segregation in the workplace—the
fact that American men and women are concentrated in different kinds of jobs and
occupations. What is the significance of gender segregation in the workplace? In our
society, people’s occupations and jobs are closely associated with their level of prestige,
authority, and income. The jobs in which women and men are segregated are not only
different but also unequal. Although the proportion of women in the labor force has
markedly increased, women are still concentrated in occupations with low pay, low
prestige, and few opportunities for promotion. Thus, gender segregation in the workplace

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is associated with unequal earnings, authority, and status. In particular, women’s
segregation into different jobs and occupations from those of men is the most immediate
cause of the pay gap. Women receive lower pay than men do even when they have the
same level of education, skill, and experience as men in comparable occupations.

Formulating the Hypotheses
So far, we have come up with a number of research questions about the income disparity
between men and women in the workplace. We have also discussed a possible
explanation—a theory—that helps us make sense of gender inequality in wages. Is that
enough? Where do we go from here?

Our next step is to test some of the ideas suggested by the gender segregation theory. But
this theory, even if it sounds reasonable and logical to us, is too general and does not
contain enough specific information to be tested. Instead, theories suggest specific
concrete predictions or hypotheses about the way that observable attributes of people or
groups are interrelated in real life. Hypotheses are tentative because they can be verified
only after they have been tested empirically.5 For example, one hypothesis we can derive
from the gender segregation theory is that wages in occupations in which the majority of
workers are female are lower than the wages in occupations in which the majority of
workers are male.

Hypothesis A statement predicting the relationship between two or more observable attributes.

Not all hypotheses are derived directly from theories. We can generate hypotheses in many
ways—from theories, directly from observations, or from intuition. Probably, the greatest
source of hypotheses is the professional or scholarly literature. A critical review of the
scholarly literature will familiarize you with the current state of knowledge and with
hypotheses that others have studied.

Table 1.1 Variables and Value
Categories

Variable Categories

Social class

Lower

Working

Middle

Upper

Gender
Male

Female

Education

Less than high school

High school

Some college

College graduate

Let’s restate our hypothesis:

Wages in occupations in which the majority of workers are female are lower than
the wages in occupations in which the majority of workers are male.

Note that this hypothesis is a statement of a relationship between two characteristics that
vary: wages and gender composition of occupations. Such characteristics are called
variables. A variable is a property of people or objects that takes on two or more values.
For example, people can be classified into a number of social class categories, such as
upper class, middle class, or working class. Family income is a variable; it can take on
values from zero to hundreds of thousands of dollars or more. Similarly, gender
composition is a variable. The percentage of females (or males) in an occupation can vary

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from 0 to 100. Wages is a variable, with values from zero to thousands of dollars or more.
See Table 1.1 for examples of some variables and their possible values.

Variable A property of people or objects that takes on two or more values.

Social scientists must also select a unit of analysis; that is, they must select the object of
their research. We often focus on individual characteristics or behavior, but we could also
examine groups of people such as families, formal organizations like elementary schools
or corporations, or social artifacts such as children’s books or advertisements. For
example, we may be interested in the relationship between an individual’s educational
degree and annual income. In this case, the unit of analysis is the individual. On the other
hand, in a study of how corporation profits are associated with employee benefits,
corporations are the unit of analysis. If we examine how often women are featured in
prescription drug advertisements, the advertisements are the unit of analysis. Figure 1.2
illustrates different units of analysis frequently employed by social scientists.

Unit of analysis The object of research, such as individuals, groups, organizations, or social
artifacts.

Learning Check 1.2

Remember that research question you came up with? Formulate a testable hypothesis based on your
research question. Remember that your variables must take on two or more values and you must
determine the unit of analysis. What is your unit of analysis?

Figure 1.2 Examples of Units of Analysis

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Independent and Dependent Variables: Causality
Hypotheses are usually stated in terms of a relationship between an independent and a
dependent variable. The distinction between an independent and a dependent variable is
important in the language of research. Social theories often intend to provide an
explanation for social patterns or causal relations between variables. For example,
according to the gender segregation theory, gender segregation in the workplace is the
primary explanation (although certainly not the only one) of the male-female earning gap.
Why should jobs where the majority of workers are women pay less than jobs that employ
mostly men? One explanation is that

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societies undervalue the work women do, regardless of what those tasks are,
because women do them. . . . For example, our culture tends to devalue caring or
nurturant work at least partly because it is done by women. This tendency
accounts for child care workers’ low rank in the pay hierarchy.6

In the language of research, the variable the researcher wants to explain (the “effect”) is
called the dependent variable. The variable that is expected to “cause” or account for the
dependent variable is called the independent variable. Therefore, in our example, gender
composition of occupations is the independent variable, and wages is the dependent
variable.

Dependent variable The variable to be explained (the effect).

Independent variable The variable expected to account for (the cause of) the dependent
variable.

Cause-and-effect relationships between variables are not easy to infer in the social
sciences. To establish that two variables are causally related, your analysis must meet
three conditions: (1) The cause has to precede the effect in time, (2) there has to be an
empirical relationship between the cause and the effect, and (3) this relationship cannot
be explained by other factors.

Let’s consider the decades-old debate about controlling crime through the use of
prevention versus punishment. Some people argue that special counseling for youths at
the first sign of trouble and strict controls on access to firearms would help reduce crime.
Others argue that overhauling federal and state sentencing laws to stop early prison
releases is the solution. In the early 1990s, Washington and California adopted “three
strikes and you’re out” legislation, imposing life prison terms on three-time felony
offenders. Such laws are also referred to as habitual or persistent offender laws. Twenty-
six other states and the federal government adopted similar measures, all advocating a
“get tough” policy on crime; the most recent legislation was in 2012 in the state of
Massachusetts. In 2012, California voters supported a revision to the original law,
imposing a life sentence only when the new felony conviction is serious or violent. Let’s
suppose that years after the measure was introduced, the crime rate declined in some of
these states (in fact, advocates of the measure have identified declining crime rates as
evidence of its success). Does the observation that the incidence of crime declined mean
that the new measure caused this reduction? Not necessarily! Perhaps the rate of crime
had been going down for other reasons, such as improvement in the economy, and the
new measure had nothing to do with it. To demonstrate a cause-and-effect relationship,
we would need to show three things: (1) The reduction of crime actually occurred after the
enactment of this measure, (2) the enactment of the “three strikes and you’re out” measure
was empirically associated with a decrease in crime, and (3) the relationship between the
reduction in crime and the “three strikes and you’re out” policy is not due to the influence
of another variable (e.g., the improvement of overall economic conditions).

Independent and Dependent Variables: Guidelines
Because it is difficult to infer cause-and-effect relationships in the social sciences, be
cautious about using the terms cause and effect when examining relationships between
variables. However, using the terms independent variable and dependent variable is still
appropriate even when this relationship is not articulated in terms of direct cause and
effect. Here are a few guidelines that may help you identify the independent and
dependent variables:

1. The dependent variable is always the property that you are trying to explain; it is
always the object of the research.

2. The independent variable usually occurs earlier in time than the dependent variable.
3. The independent variable is often seen as influencing, directly or indirectly, the

dependent variable.

The purpose of the research should help determine which is the independent variable and
which is the dependent variable. In the real world, variables are neither dependent nor
independent; they can be switched around depending on the research problem. A variable
defined as independent in one research investigation may be a dependent variable in
another.7 For instance, educational attainment may be an independent variable in a study
attempting to explain how education influences political attitudes. However, in an
investigation of whether a person’s level of education is influenced by the social status of
his or her family of origin, educational attainment is the dependent variable. Some

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variables, such as race, age, and ethnicity, because they are primordial characteristics that
cannot be explained by social scientists, are never considered dependent variables in a
social science analysis.

Learning Check 1.3

Identify the independent and dependent variables in the following hypotheses:

Older Americans are more likely to support stricter immigration laws than younger Americans.
People who attend church regularly are more likely to oppose abortion than people who do not
attend church regularly.
Elderly women are more likely to live alone than elderly men.
Individuals with postgraduate education are likely to have fewer children than those with less
education.

What are the independent and dependent variables in your hypothesis?

Collecting Data
Once we have decided on the research question, the hypothesis, and the variables to be
included in the study, we proceed to the next stage in the research cycle. This step includes
measuring our variables and collecting the data. As researchers, we must decide how to
measure the variables of interest to us, how to select the cases for our research, and what
kind of data collection techniques we will be using. A wide variety of data collection
techniques are available to us, from direct observations to survey research, experiments, or
secondary sources. Similarly, we can construct numerous measuring instruments. These
instruments can be as simple as a single question included in a questionnaire or as
complex as a composite measure constructed through the combination of two or more
questionnaire items. The choice of a particular data collection method or instrument to
measure our variables depends on the study objective. For instance, suppose we decide to
study how one’s social class is related to attitudes about women in the labor force. Since
attitudes about working women are not directly observable, we need to collect data by
asking a group of people questions about their attitudes and opinions. A suitable method
of data collection for this project would be a survey that uses some kind of questionnaire
or interview guide to elicit verbal reports from respondents. The questionnaire could
include numerous questions designed to measure attitudes toward working women, social
class, and other variables relevant to the study.

How would we go about collecting data to test the hypothesis relating the gender
composition of occupations to wages? We want to gather information on the proportion
of men and women in different occupations and the average earnings for these
occupations. This kind of information is routinely collected and disseminated by the U.S.
Department of Labor, the Bureau of Labor Statistics, and the U.S. Census Bureau. We
could use these data to test our hypothesis.

Levels of Measurement
The statistical analysis of data involves many mathematical operations, from simple
counting to addition and multiplication. However, not every operation can be used with
every variable. The type of statistical operation we employ depends on how our variables
are measured. For example, for the variable gender, we can use the number 1 to represent
females and the number 2 to represent males. Similarly, 1 can also be used as a numerical
code for the category “one child” in the variable number of children. Clearly, in the first
example, the number is an arbitrary symbol that does not correspond to the property
“female,” whereas in the second example the number 1 has a distinct numerical meaning
that does correspond to the property “one child.” The correspondence between the
properties we measure and the numbers representing these properties determines the type
of statistical operations we can use. The degree of correspondence also leads to different
ways of measuring—that is, to distinct levels of measurement. In this section, we will
discuss three levels of measurement: (1) nominal, (2) ordinal, and (3) interval-ratio.

Nominal Level of Measurement

At the nominal level of measurement, numbers or other symbols are assigned a set of
categories for the purpose of naming, labeling, or classifying the observations. Gender is
an example of a nominal-level variable (Table 1.2). Using the numbers 1 and 2, for
instance, we can classify our observations into the categories “females” and “males,” with
1 representing females and 2 representing males. We could use any of a variety of

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symbols to represent the different categories of a nominal variable; however, when
numbers are used to represent the different categories, we do not imply anything about the
magnitude or quantitative difference between the categories. Nominal categories cannot
be rank-ordered. Because the different categories (e.g., males vs. females) vary in the
quality inherent in each but not in quantity, nominal variables are often called qualitative.
Other examples of nominal-level variables are political party, religion, and race.

Nominal measurement Numbers or other symbols are assigned to a set of categories for the
purpose of naming, labeling, or classifying the observations. Nominal categories cannot be
rank-ordered.

Nominal variables should include categories that are both exhaustive and mutually
exclusive. Exhaustiveness means that there should be enough categories composing the
variables to classify every observation. For example, the common classification of the
variable marital status into the categories “married,” “single,” and “widowed” violates the
requirement of exhaustiveness. As defined, it does not allow us to classify same-sex
couples or heterosexual couples who are not legally married. We can make every variable
exhaustive by adding the category “other” to the list of categories. However, this practice is
not recommended if it leads to the exclusion of categories that have theoretical
significance or a substantial number of observations.

Table 1.2 Nominal Variables
and Value Categories

Variable Categories

Gender
Male

Female

Religion

Protestant

Christian

Jewish

Muslim

Marital status

Married

Single

Widowed

Other

Mutual exclusiveness means that there is only one category suitable for each observation.
For example, we need to define religion in such a way that no one would be classified into
more than one category. For instance, the categories Protestant and Methodist are not
mutually exclusive because Methodists are also considered Protestant and, therefore,
could be classified into both categories.

Learning Check 1.4

Review the definitions of exhaustive and mutually exclusive. Now look at Table 1.2. What other
categories could be added to each variable to be exhaustive and mutually exclusive?

Ordinal measurement Numbers are assigned to rank-ordered categories ranging from low to
high.

Ordinal Level of Measurement

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Whenever we assign numbers to rank-ordered categories ranging from low to high, we
have an ordinal level of measurement. Social class is an example of an ordinal variable.
We might classify individuals with respect to their social class status as “upper class,”
“middle class,” or “working class.” We can say that a person in the category “upper class”
has a higher class position than a person in a “middle-class” category (or that a “middle-
class” position is higher than a “working-class” position), but we do not know the
magnitude of the differences between the categories—that is, we don’t know how much
higher “upper class” is compared with the “middle class.”

Many attitudes that we measure in the social sciences are ordinal-level variables. Take, for
instance, the following statement used to measure attitudes toward working women:
“Women should return to their traditional role in society.” Respondents are asked to
identify the number representing their degree of agreement or disagreement with this
statement. One form in which a number might be made to correspond with the answers
can be seen in Table 1.3. Although the differences between these numbers represent
higher or lower degrees of agreement with the statement, the distance between any two of
those numbers does not have a precise numerical meaning.

Like nominal variables, ordinal variables should include categories that are mutually
exhaustive and exclusive.

Table 1.3 Ordinal Ranking Scale

Rank Value

1 Strongly agree

2 Agree

3 Neither agree nor disagree

4 Disagree

5 Strongly disagree

Interval-Ratio Level of Measurement

If the categories (or values) of a variable can be rank-ordered and if the measurements for
all the cases are expressed in the same units, and equally spaced, then an interval-ratio
level of measurement has been achieved. Examples of variables measured at the interval-
ratio level are age, income, and SAT scores. With all these variables, we can compare
values not only in terms of which is larger or smaller but also in terms of how much larger
or smaller one is compared with another. In some discussions of levels of measurement,
you will see a distinction made between interval-ratio variables that …

Discussion # 1 Due Weds 08/19/21

Wk 1 Discussion 1 – Statistics [due Thurs]

Discussion Topic

Top of Form

Please refer to the resources provided on CDS Central. They are intended to help you engage effectively on the discussion board.

Due Thursday

This course provides foundational information about statistics.

Frankfort-Nachmias & Leon-Guerrero (2018) explain “statistics is a set of procedures used by social scientists to organize, summarize, and communicate numerical information. Only information represented by numbers can be the subject of statistical analysis” (p.18).

Write a 250- to 300-word response to the following:

· How do you plan to use what you learn in this course in your personal or professional life?

· What specific information are you hoping to learn to apply in your dissertation?

Reference: Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: SAGE Publications, Inc. 

Due Monday

Review others’ questions and using your professional experience, provide an answer or related insights in 150 words.

Copyright 2020 by University of Phoenix. All rights reserved.

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Discussion # 2 Due Fri 08/20/21

Wk 1 Discussion 2 – Research [due Sat]

Discussion Topic

Top of Form

Please refer to the resources provided on CDS Central. They are intended to help you engage effectively on the discussion board.

Due Saturday

Frankfort-Nachmias and Leon-Guerrero (2018) depict research as a five-stage iterative process (Ch. 1, Figure 1.1).

 

Write a 250- to 300-word response to the following:

· How theory informs and is informed by:

· Asking the research question

· Formulating hypotheses

· Collecting data

· Analyzing data

· Evaluating hypotheses

Reference: Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: SAGE Publications, Inc. 

Due Monday

Review others’ questions and using your professional experience, provide an answer or related insights in 150 words.

 

Copyright 2020 by University of Phoenix. All rights reserved.

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