Some hypotheses from ASRA hypothesis connects an operationali.docx

Some hypotheses from ASR:
A hypothesis connects an operationalized concept(s)
(variable(s)) with another variable or a few others. So, there are
three things to pay attention to:
1) the variable(s) that the author wants to explain
2) the variable(s) that the author explains (1) with
3) connection between (1) and (2)
“a higher presence of women in professional associations will
contribute to greater gender earnings equality”
“Some NHRIs will be more effective than others based on (1)
the length of time each form has been in existence and
characteristics such as (2) substantive mandate (whether the
human rights focus is explicit) and (3) structural capacities (the
power to investigate government wrongdoing).”
“NHRIs will have a stronger effect on physical integrity
violations than on civil and political rights abuses.”
A few examples of SOCI380 student hypotheses:
H1a: applied science students will display lower levels of
willingness to act against ACC compared to non-applied science
students
H1b: science students will display higher levels of willingness
to act against ACC compared to non-science students
H2: there is a positive relationship between a student's attitude
towards climate change and their willingness to act against ACC
H3: there is a positive relationship between a student's
knowledge about climate change and their willingness to act
against ACC

H1: (a) there is a positive relationship between the number of
hours someone spends on Facebook per day and the personality
traits of extroversion, agreeableness and openness to
experience.
(b) There is a negative relationship between the number of
hours spent on Facebook per day and the personality traits of
conscientiousness and neuroticism.
H2: There is a negative relationship between time spent on
Facebook and self-esteem
Research Project Information
Part 1: Literature Review
Your literature review should be 4-5 pages (6-7 if working in
pairs) in length (excluding title page and reference section),
typed (12 point font), and double spaced. Paper topics must
relate to studies in sociology and must be geared toward survey
research. NOTE: papers submitted for another class cannot be
submitted for this assignment – this is academic dishonesty. Use
peer reviewed articles or chapters, do not use reviews of the
literature. Be sure to narrow you topic so that your paper has
depth. Students are asked to come to my office to discuss paper
topics and prepare an outline for the paper to review with me
before beginning. Projects MUST fall under minimal risk
criteria (see ethics guidelines that will be distributed in class).
You may work in pairs or individually. If you work with a
partner you must submit a peer evaluation of your partner for
each section of the project.
Introduction (10 points)
· Introduce the topic
· State the argument or purpose of the review in a clear thesis
(Why is this topic relevant?)
· State topic limits you have set (how have your narrowed your
topic?)

Synthesis and critique of relevant research materials (25 points)
This is the most important part of the paper. In this section,
relevant research
literature should be discussed and critically evaluated. This
section should:
· Inform the reader of the relevant theoretical background to the
topic
· Provide an integrated discussion of previous research related
to the topic (do not simply list studies – integrate)
· Prepare the reader for the hypotheses you will make in the last
part of your paper
- Consider the limitations and flaws of the literature in the
topic area (think
about methods used in the studies reviewed)
Conclusion (10 points)
· A paragraph which affirms the purpose or argument of the
introductory paragraph. Integrate and summarize how the ideas
presented are connected and linked
· Be sure to show that your review has lead you to the
consideration of your research question(s)
· Set the stage for your own research project (why do we need
to do the study you are proposing?)
· Hypotheses – close with your specific research hypotheses
Style (5 points)
· References made according to APA style conventions
· Writing skills (spelling, grammar, sentence structure etc)
without flaws
· Presented in a professional manner
Carefully proofread your papers before you hand them in!
Students should retain a copy of all submitted assignments (in
case of loss), or the need for a Review of Assigned Standing
arises.

Writing and Reference Resources:
· American Psychological Association (2009). Publication
Manual of the APA (6th ed.). Washington, DC: Author.
Part 2: Ethics tutorial and Consent Form
1) Students are to complete the Ethics tutorial found at:
http://www.pre.ethics.gc.ca/eng/education/tutorial-didacticiel/
and submit the certificate of completion – 10 points
2) Students are to complete a consent form that relates to their
survey project following the “consent form guidelines” found at
(and posted in Canvas):
https://ethics.research.ubc.ca/behavioural-research-ethics/breb-
guidance-notes
- 10 points
Part 3: Methods Section and Survey
Please note: For part 3 of the assignment you will be graded on
the description of the measures (methods section) and survey
development (appendix). Please also attach either your
literature review or enough of it to show your research
question(s)/hypotheses. You will lose 5 points for not
providing some background context for me to look at when
assessing your survey (e.g. a copy of the literature review or
introduction).
See rubric on the following page.
Excellent
Good
Adequate

Poor
Total Points
Survey
Development
/20
Survey items are well thought out and include all important
items to answer the research question(s).
Survey items sufficiently cover important items to answer the
research question(s).
Survey questions are limited and offer some information
required to answer the research question(s).
Survey questions do not adequately present enough information
to answer the research questions.
Organization & Mechanics
/5
Survey questions are asked in a logical and well thought out
sequence which respondents can follow. No misspellings or
grammatical errors.
Survey questions are asked in logical sequence which
respondents can follow. No more than two misspellings and/or
grammatical errors.
Survey questions are not listed or asked in a logical format.
There are four misspellings and/or grammatical errors.
Survey questions are not appropriate for the requested
information. There are more than 4 spellings errors and/or
grammatical errors.
Format
/5
The format is correct, title appropriate, and laid out wisely,
with no typing errors.
The format is correct, title is appropriate, and has no more than
2 typing errors.

Format has some inconsistencies and missing areas, and/or more
than 4 typing errors.
Format is sloppy, margins, layout is inconsistent, and many
typing errors.
Methods Section (description of measures)
/10
The methods section includes detailed information about how
the research will be completed (clear definition/description of
measures to be used)
The methods section lacked some important details regarding
how the research will be completed (definition/description of
measures somewhat unclear)
The methods section did not explain how the research will be
completed (by not sufficiently describing the measures to be
used)
Does not include a description of measures
Total
/40
Part 4: Final Project Grading Rubric
For the final project you are asked complete revisions on the
prior project pieces and submit the whole project in one file:
Intro/literature review, methods, results, discussion/conclusion,

references and appendix (cover letter and survey). Please note
there are many resources on the Canvas course site to help you
with the analysis and interpretation of results. Please see the
following rubric:
Criteria
Excellent
Good
Average
Poor
Total Points
Purpose of research
(Revisions completed)
Clearly identified/Lit review precisely summarized; clear thesis
statement
Understandable and brief. Only part of lit review discussed;
unclear thesis statement
Vague and wordy; vague thesis statement
Incomprehensible and no mention of lit review; no thesis
statement
/10
Research questions or Hypotheses
(Revisions completed)
Clearly stated
Understandable
Vague and wordy
Incomprehensible
/5
Research design
(Revisions completed and include analysis plan)
Clearly summarized and accurate
Understandable but not completely accurate
Vague with some accuracy
Incomprehensible and completely inaccurate
/10
Findings
(Use tables where appropriate)

Clearly summarized, precise, accurate, giving both demographic
data and data to support claim.
Understandable with only part of the data to support claim.
Vague and not completely accurate. Only giving data to
partially support claim.
Totally inaccurate discussion of data.
/20
Discussion/Conclusions/Interpretation of results
Extremely clear and precise and accurately identified.
Understandable and brief, but accurate.
Vague and wordy, only partially accurate.
Incomprehensible and totally inaccurate.
/20
Limitations
Very clear and accurately reported.
Understandable and partially accurate.
Vague and wordy.
Incomprehensible
/5
Research utilization (future directions)
Strategies for implementation succinctly identified
Some strategies for implementation identified
Strategies and implementation is unclear
No strategies for implementation
/5
Follows assignment directions, including APA format
Grammar, spelling, and sentence structure
Follows directions and turns paper in on time.
Perfect.
Directions followed with some minimal deviations.
Minor errors.
Missed most of the instructions.
Major errors.
Did not turn in assignment on time.
Many major errors.
/5

/80
SOCIOLOGY RESEARCH METHODS QUESTIONNAIRE
Instructions: Please answer all questions as truthfully and
accurately as possible. Check or mark the appropriate box/space
for each question or print an answer in blanks provided. All
answers are confidential and complete anonymity is assured.
Your participation is voluntary and will help us greatly. Thank
you.
Part I: Individual Background and Demographic Information
1. Do you play video games? Yes___ No _____
0. What is your gender? __________
0. What is your age? _____
0. Are you still studying? Yes__ No __
a. If yes, what is your current GPA/average? _____
i. If you do not have any idea of what your GPA is check here
___
0. Highest level of education completed.
(less than highschool, highschool or equivalent / associate /
bachelor / graduate degree)
0. What is your current marital status?

Single___ Divorced___Widowed___Separated Married___
0. Do you have any children? _____
0. Do you have a job? (Full-time / Part time / seeking
employment / unemployed)
0. How much are you getting paid per year?
(Less than 10,000 / 11,000-30,000 / 31,000-50,000 / 51,000 -
70,000 / 70,000+)
0. Living situation (Own / Rent / Parent’s house / other ______
)
Part II. Sociability
Circle the option that most accurately applies to you.
1. I often think about how other perceives me.
(Strongly agree / Agree / neutral / disagree / strongly disagree)
0. I am able to openly able to talk about my feelings to other
people.
(Strongly agree / Agree / neutral / disagree / strongly
disagree)
0. I often get panic or anxiety attacks
(Strongly agree / Agree / neutral / disagree / strongly
disagree)
0. I am confident in my abilities and myself in general
0. I am unsure of what the future holds for me
0. I am often unsure of how to present myself in public

0. I am someone people can easily talk to about their problems
0. I am easy to talk to and be friends with
0. I can walk up to others and socialize
0. I work well in groups
0. I like to be depended on my others
Part III. Video Games
1. What video games are you currently playing?
______________________________
0. Do you play video games by yourself or with friends?
________________________
0. Would you consider yourself closer to your friends in game
or in real life? ________
0. Aside from video games, what else do you do on your free
time? _______________
0. How much money in general do you spend in video games per
month? __________
0. How long do you play video games per week on average?
(1 - 3 hours / 4 - 6 hours / 6 - 10 hours / 11 - 15 hours / 16+
hours)

0. Do you rage quit? (Often / more than average / sometimes /
rarely / never)
0. Have you ever been subject to disciplinary actions from the
game (Ban/suspension) due to ingame behavior? (Yes/No)
1. How seriously do you take winning in games?
(Very serious / Somewhat serious / neutral / not serious /
indifferent)
0. Do you consider yourself a hardcore or a Casual player?
(Hardcore / Casual / inbetween)
Part IV: Additional information
Circle the option that most accurately applies to you.
1. I am confident in my ability to react to changing situations.
0. I can manage multiple tasks at once.
0. I welcome change.
0. Working alone, I can produce the best result.
0. I am organized. Everything around me has its place.
0. I am like filling a unique spot on a group that only I can do.
0. I prefer to work with a team.

0. I work best and most efficiently when I am busy.
0. I am constantly aware of my surroundings.
******************** END OF QUESTIONNAIRE, THANK
YOU ************** ******
Hypothesis
CONS
H1. Players using video games as an escape instead of a hobby
is likely to be less sociable in the real world.
H2. Players who play video games in excess often forgo their
responsibilities such as work, family, friends, or school.
H3. Players who play action oriented games at a competitive
setting are more likely to develop violent tendencies.
PROS
H1. Players who play video games as a hobby or past time
instead of an escape is likely to develop better social skills.
H2. Players who focus on skill/reaction/strategic games have
increased attention to everything around and can react better to
changing situations.
H3. Players who play video games display a better organization
and multi-tasking abilities.
Lab 3 – SPSS [ANOVA & REGRESSION]
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The Road So Far
Lab 1: Data Cleaning and Univariate Analysis
Lab 2: Bivariate Analysis: Correlation and t-test
Lab 3 OBJECTIVE:
1. Familiarizing yourself with Analysis of Variance (ANOVA)
o When can you use it
o How to run it
o How to interpret results
o Limitation
2. Re-familiarizing yourself with Regression Analysis
o Linear regression with no controls
o Linear regression with controls
o Linear regression with multiple independent variables
o How to run regression in SPSS
o How to interpret results

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REVIEWING T-TEST
EXAMPLE 1 – GENDER AND DEPRESSION
From marking LAB 2, I identified a few patterned problems
with the “mechanic” and interpretation of t-
test results:
about the significance of the t-test for
Equality of Means. It tells you about whether you can reject the
null hypothesis of equality of
variances.
EXERCISE 1 – No. Publication and Discipline PhD
-test, the interpretation of
the Mean Difference in the result
table might not be 100% clear for all of you.

o (1) Is Levene’s significant, (2) is t-test significant, and (3)
what does 3.6121 means
SUBSTANTIALLY?
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Correlation
-Test
-
Square
One-way ANOVA (Analysis of Variance [amongst the means])
RELATIONSHIP BETWEEN CATEGORICAL &
CONTINUOUS VARIABLES
The one-way analysis of variance (ANOVA) is used to
determine whether there are any statistically
significant differences between the means of two or more
independent (unrelated) groups (although you
tend to only see it used when there is a minimum of three,

rather than two groups).
For three groups and more, a statistically significant ANOVA
tells you that is at least one mean comparaison
between the groups is statistically significant. (Hint: between
lower- vs. highest mean group.)
From StackOverFlow:
“In the typical ANOVA we have a categorical variable with
different groups, and we attempt to determine
whether the measurement of a continuous variable differs
between groups.”
Exercise 2 – One-way ANOVA – DEGREE_TYPE_LAB_3 and
SALARY
One-Way ANOVA can tell us if there is a statistically
significant difference in “mean” salary between UBC
BA, BSc, and Bcom degree holders who have a LinkedIn
account.
Q.2.1: what is the independent variable and dependent variable?
(4 pts)
The mechanic of running a one-way ANOVA:
-Way Anova
o Factor == Independent Categorical Variable
s” and Check “Descriptive” and “Means
plot”.

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-way ANOVA.
not too scary. You are looking at
salary measures for three different groups.
The first thing to do is to look at the Graph “Means Plots”:
Q.2.2: By looking at both the graph and the descriptive
statistics table, what can you say about the
salary of UBC graduates? (4 pts)
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Now that you have a basic understanding of the descriptive
results, let’s look at the ANOVA table:

There is always a lot of information, but the important thing to
pay attention is:
doing before looking at the results.
-way ANOVA. We want to know if there
is a statistically significant difference
between the salary of BA, BSc, and BCom holders?
Put differently, what is the probability that the difference is
due to chance alone?
o Null hypothesis: no difference
o Alternative hypothesis: not due to chance and related to
categorical variable
-value, that is, the
significance statistics.
Q.2.3: What is the result of the one-way ANOVA test? What
does it tell us about the relationship
between salary and bachelor degree types? (4 pts)
Important Point (and frustrating things with one-way ANOVA)
he difference in salary statistically significant between BA
and BSc, or between BSc and Bcom?
-way ANOVA doesn’t tell us that!
-way ANOVA is telling you is this: “there is at least
ONE mean comparison between the
groups that is statistically significant”.

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EXERCISE 3 – Two-way ANOVA – DEGREE_TYPE_LAB_3,
GENDER and SALARY
Q.3.1: what are the two independent variables and one
dependent variable? (6 pts)
ind with two-way ANOVA, we are still looking at
difference in means
o Salary (annual income in $K)

o Gender (Female and Male )
o Degree_Type_Lab_2 (BA, BSC, and BCOM)
In SPSS, you need General Linear Model:
The mechanic of running a two-way ANOVA :
STEP 1
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STEP 2 (Click on Options)
STEP 3 (Click on Plots)

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TA, TA, how do I know what goes where in STEP 3?
attributes
Add and Continue
A two-way ANOVA OUTPUT file can be a bit intimidating,
let’s look at it together:
1. Tips from the Pro: Scroll to Plots at the bottom of Output
first
Q.3.2: A one-way ANOVA couldn’t tell us which mean salary
difference amongst the 3
degree types WAS significant. From the plot above, can you
start to evaluate which salary
difference between degree type is probably NOT significant?
Which is it? Why is that? (6
pts)
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Ok, still a lot of results in the OUTPUT, how do we look at all
that stuff?
Plan of Attack I:
1. Scroll back up to Descriptive Table
o Try to make connection with what you just saw in the
GRAPH.
2. Scroll down Pairwise Comparions for Gender of Graduate
o Remember when I said that ANOVA is like a t-test for
categorical variable with
more than two attributes…what do you see?
Descriptive Table
Q.3.3: By looking at both the graph and the descriptive
statistics table, what can you say
about the salary of UBC graduates based on degree type and
gender? What stand out to you?
What seems important to mention? (6 pts)
Pairwise Comparions for Gender of Graduate
at you are doing here:
COMPARING MEAN DIFFERENCE

remind you of a significance test from
LAB 2? Which one?
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Pairwise Comparions for Degree_Type
Q.3.4: From looking at the two Pairwise Comparisons Tables,
what can you tell about the
differences of salary based on Gender and Degree Type? What
stand out for you? What will
probably be significant? (6 pts)
Plan of Attack II:
e looked at:

-way ANOVA statistical
results: Test of Between-
Subjects Effect
cant
-Squared: 79.3%
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Q.3.5: What does the Tests of Between-Subjects Effects tells
you? What is statistically
significant? (10 pts)
FROM ANOVA TO REGRESSION
The best way to think of the differences between ANOVA and
REGRESSION is to look at the
kind of variables used for each:

variable of choice
o 1 dependent continuous variable
o 1 or multiple independent categorical variable(s)
o In terms of statistical language, in comparing means, ANOVA
ask how much the
difference between these groups (e.g. race, gender, job type)
explain variation in
salary
ith dummy variables
o 1 dependent continuous variable (but can also be categorical)
*key difference
o 1 or multiple independent and control variable(s)
o Can discriminate between the effect of each attribute for
categorical variable
significance?
*key difference
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EXERCISE 4 – LINEAR REGRESSION– SALARY
Q4.1: Ask yourself, if you think gender, race, and GPA can help
predict salary, could you use a two-way
ANOVA? Why? (4 pts)
BUILDING YOUR REGRESSION MODEL
o What do you think can help predict the variation in our
graduate’s annual salary on the job
market?
Skills
* List dependent variable you want to predict in your regression
model (2 pts)
* List 2 control variables you want to include in your regression
model (4 pts)
* List 3 independent variables you want to include in your
regression model (6 pts)
STEP 1: Dealing with Independent Categorical Variables

* In linear regression, the arbitrary assignment of numerical
values to each degree type variable’s
attributes (1, 2, 3) would be interpret numerically. As we know,
apart from distinguishing each
attribute from one another, the numerical value doesn’t mean
anything in themselves.
* In linear regression, all independent categorical variables
need to be recoded as dummy
variables as well to allow the model to differentiate between the
effect of each degree type on
annual salary.
* Remember differentiating between each degree type is
something ANOVA was not able to tell
us. This is key to remember.
* Recoding dummy variables is what allowed to look at the
specific effect of each attribute on
annual income.
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Look in Variable View, the recode of Degree Type into three
dummy variables:
Q4.2: Why is Degree Type the only independent variable
recoded into multiple dummy variables? (4
pts)
Let’s Build Our Model:
salary:
o For independent categorical variable, you cannot include all 3
dummy variables.
o One need to be the baseline [when you will see the results it is
become clear what
that means]. Let’s leave Bcom out of the model
o Add BA_DUMMY & BCOM_DUMMY as independent
variables
o Click OK and run the model

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LINEAR REGRESSION OUTPUT I
Coefficients TABLE
What do you see? Two things should be noteworthy:
1. degree-type-BA is statistically significant
2. degree-type-Bsc is statistically significant
Remark I
In plain language, the effect of holding a BA vs. two other types
of degree help explain the
variation in annual salary. More precisely, holding a BA has a
negative effect on annual salary.
Remark II
Remember when we left Bcom out of the regression model
earlier? Bcom is the referent. What is
that means? Holding a BA has a negative effect on annual
salary. Compare to Bcom holder, BA
holder earn on average 17, 454.18$ less annually. You can see
here that what a REGRESSION

MODEL can do that a two-
the statistical significance of
difference on a continuous variable (Salary) between two
attributes (BA vs. BCOM) of a
categorical variable.
Q4.3: Use the same interpretative logic contained in Remark I &
II for degree-type-BSC. (6 pts)
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LINEAR REGRESSION OUTPUT II
Model Summary Table
What does the Adjusted R Square tell us?
two years after graduate can be
-Type

CONTROL VARIABLE(S)
Since the beginning of the semester, Dr. Bartolic and I have
been bugging you with control variable(s). We
shall now see why.
We discussed the limitation of ANOVA earlier. That said, the
two-way ANOVA reveals that gender was
significant. It is thus important to add GENDER and see if its
change anything to the explanatory power of
degree-type on salary. In statistics language, we want to see if a
portion of the variation that was attributed
to degree-type in our previous model could not, in fact, due to
gender.
Let’s add gender:
o Add GENDER to Independent(s) block
o Click OK and run the regression
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REGRESSION MODEL WITH GENDER

Q4.4: Looking at the Coefficients and Summary Table, what can
you conclude on the effect of gender
on salary? Also, did introducing gender to the regression model
changed the overall effect of degree-
type? (10 pts)
EXERCISE 5 – LINEAR REGRESSION– SALARY (more)
Keep working with Salary as a dependent variable. Look at the
list of variables and find two new
independent variables (not degree-type) you think might have an
effect on salary.
Q5.1: What are the two independent variables you choose? (4
pts)
Q5.2: Why do you think they might influence salary? (6 pts)
Q5.3: Build and run the regression model. (10 pts)
Q5.4: Interpret the results. (10 pts)
Pick a first control variable (not gender) and add it to the
model.
Q5.5: Run the regression model with control variable. (10 pts)
Add Gender and Degree-Type to your final model

Q5.7: Run the regression model. (10 pts)
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pLAB-3 REVISION
Q1: Based on the plot, gender has significance for BS and Bcom
and not for Ba.
True or False. Why?
Q2: Ask yourself, if you think gender, race, and GPA can help
predict salary, could you
use a two-way ANOVA? Why?

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Q3: On average, economists published 4 academic articles in
their entire life.
True or False. Why?
INTERPRETATION OF MEAN DIFFERENCE AND
CONFIDENCE INTERVAL
This specific test tells us that students in the Econ field will
publish less than those in
other fields. In fact, by the end of PhD graduation non-
economics PhDs have published,
on average, 3.6121 papers more than those in the Econ field.
Looking at the 95%
confidence interval of difference we can assume that 95% of
these cases will fall
between 2.7939 or 4.4303 more papers published by those
outside of econ field for their
PhD.
Q4: The effect of holding a BSc degree compare to the other
types of degrees, BA and
Bcom, has positive effect on annual salary of the individual.
Based on Coefficients Table below, is this statement true or
false? Why?

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EXCELLENT UNDERSTANDING OF CONTROL VARIABLE
Q4.4: Looking at the Coefficients and Summary Table, what can
you conclude on
the effect of gender on salary? Also, did introducing gender
to the
regression model changed the overall effect of degree-type?
(10 pts)
As shown in the tables, the unstandardized coefficient for the
dummy variable
“Gender of Graduate” is -16412.081 which suggests the average
annual salary
for female graduates is 16412.081 units of salary less when
compare to that of
male graduates while holding all other variables constant. And
the p-value
(0.000) of this coefficient indicates the salary difference
between male and
female is statistically significant. Moreover, the adjusted R
square for this
model is 0.710 which is larger than the adjusted R square
(0.538) of the model
without the variable “Gender of Graduate”. It means the control
variable “Gender
of Graduate” helps to explain the variation in the dependent
variable “salary”. By
comparing the unstandardized coefficients of degree type in two

regression
tables, we can see the coefficients of the variable “degree-type-
BA” and “degree-
type-BSC” in the model with the variable “Gender of Graduate”
are smaller than
that of the model without the variable “Gender of Graduate”,
which means the
variable “Gender of Graduate” reduced the explanatory power
of degree-type on
salary.
Therefore, we could conclude that the control variable “Gender
of Graduate”
changed the overall effect of degree-type on average annual
salary.
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NO CONTROL
WITH GENDER AS CONTROL

HOW TO INTERPRET UNSTADARDIZED COE. FOR
CONTINUOUS VARIABLE
INTERTATION OF REGRESSION TABLE
As shown in the tables, the unstandardized coefficient of the
variable “# of Job
Experience” is 1276.059 (p=0.033), we expect 1276.059 units
increase in salary of
graduates for every unit increase in the number of job
experience while holding all other
variables constant. Similarly, we expect 306.390 (p=0.090)
units decrease in salary of
graduates for every unit increase in the number of skills while
holding all other
variables constant, but the p-value (0.090) indicates the
coefficient is not statistically
significant. Besides, the R square (0.040) suggests only about
4% of the variation in the
dependent variable (salary) are explained by these two
independent variables (# of Job
Experience and # of skills).
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Q5: Why is Degree Type the only independent variable
recoded into multiple
dummy variables? (4 pts)
Because it is a categorical variable. In linear regressions all
independent variables that are
categorical need to be recorded into multiple dummy variables.
What is missing in the statement above?
GOOD EXAMPLE – USE OF DUMMY VARIABLES
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Lab 1 – SPSS univariate statistics
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Lab Assignment 1: Descriptive Analysis
What does the trajectory of UBC graduates look like on the job
market? Institutional and provincial
survey initiatives all assure us that graduated students are doing
great! They are mostly all employed
and extremely satisfied with what their post-secondary
education brought them. What kinds of jobs
are they holding? Survey’s data-points are highly aggregate and
tend to answer this question with
concepts like “public sector”, “private sector”, or “service
industry”? How many jobs can a recent
graduate go through in their first few years on the job market?
Do UBC domestic graduates stay in the
province or do they tend to be mobile?
To start answering labor-market questions with a finer level of
granularity, Dr. Bartolic’s students built
a dataset using UBC graduate LinkedIn profiles. Using the
UBC 2016 graduation convocation guide,
they created a dataset that included all bachelor-level 2016
spring graduates (n=5437) and collected
LinkedIn data for an initial sample of 130 cases. Preliminary
analysis indicates that 23% (n=1177) of
all UBC 2016 spring bachelor graduates have a LinkedIn
account.
In today’s lab, we will start exploring this unique dataset using
SPSS. Let’s figure out what your
future might looks like together!
SPSS WORKFLOW AND DATA MANAGEMENT
1. Open SPSS

2. Load Dataset File (ubc_grad_2016_SPSS.sav)
3. Today we are working with AN EXISTING DATASET, but
for your project, your first step
in SPSS would be:
download your data file
from Qualtrics
4. Spend a few minutes to explore the dataset. Look at the
bottom left:
ariable]; row [case]
details]
Fig. 1: Data View and Variable View
2 | P a g e
Exercise 1 – ANSWER SOME BASIC QUESTIONS NOW (10
pts)

Now that you are a bit more familiar with the dataset, please
answer the four following questions:
1. What is the # of cases in the dataset (2 pts):
2. What does each “case” represent? (2 pts)
3. What are the # of variables in the dataset (2 pts):
4. Name two variables from the dataset and explain what they
are measuring (4 pts):
Exercise 2 – CREATE A NEW VARIABLE: RELIGION (10 pts)
Pay attention students, this is one of the first steps you will
have to do after you have collected your
data. You will open SPSSS, create a new dataset (step 3 below),
and create new variables to populate
your dataset (all those independent and dependent variables we
have been bugging you about for 1
whole month now!)
PAY ATTENTION DEAR STUDENTS!!!!!
Two ways to create a new variable, both from DATA or
VARIABLE VIEW
The best way is the following:
1. Click on Variable View (see Fig. 1 for help)
2. Say you want to add the variable RELIGION after RACE,

right click on the number
associated with the variable after RACE and select Insert
Variable:
3. Start with Column “Name” and Name the Variable:
RELIGION (no space)
3 | P a g e
4. Go to Column “Label” and describe in a few words what the
variable is
5. Go to Column “Values” and click on the small three dots.
This should open a pop-up.
Fig. 2: CREATE A NEW VARIABLE (variable view)
Column “Values” (continued):
Here you have some thinking to do. How do you want to code
the variable?
o Christian code as “1” and non-Christian as “0”

to capture
o Christian; “0”, Muslim; “1”, Jewish; “2”; Other; “3”, Missing;
“888”
o Sounds good? Well, let’s code. The results should be:
Fig. 3: Value Label for New Variable
4 | P a g e
Are we done creating the new variable? No!
Next Two Steps:
6. Go to Column “Missing” and click on three small dots. A
pop-up should open.
7. Go to Column “Measure” and select the appropriate type of
variable. See you’re putting
your hard-bookish knowledge into practice here.
8. SAVE YOUR FILE OFTEN TO NOT LOOSE YOUR

PRECIOUS WORK!
9. When you save, SPSS will open a pop-up “output file”. You
don’t need to save it. Just close
it and don’t think about it.
Next Final Steps:
10. Switch to “Data View” (see Fig. 1 for help)
11. Find your new variable RELIGION.
12. Manually input an attribute for each respondent. (Obviously
here, this is a make-believe
variable, but with your own data you would input real data for
each case.)
13. SAVE YOUR WORK.

5 | P a g e
Exercice 3 – RECODING A VARIABLE: DEPRESSION ITEMS
(10 pts)
1. In the dataset (either Data or Variable View Mode), find the
11 depression-related variables
and indicate their name below (2 pts).
2. Reeeeeee-coding time. Why do we need to recode data?
a. Imagine for the Religion Variable, you didn’t assign
numerical values to each
attribute and instead your variable Religion in your dataset was
RAW. Raw means
that instead of number as attributes, each respondent’s response
would be a “string”:
e.g. Muslim, Jewish…. Why is that a problem? (1 BONUS pt)
Find Coding inconsistencies
3. The depression questionnaire included 11 items. The general
idea is that the higher a
respondent’s score on the scale the more severe/likely is their
depression.
a. Carefully read each item and find inconsistencies with the
idea that a “high score”
indicates “depression” and “lower score” indicates “non-

depression”.
b. How many did you find? Indicate question [#] here. (2 pts)
DEPRESSION QUESTIONNAIRE
[1] In the past week, I felt depressed
[2] In the past week, I felt that everything I did
was an effort
[3] In the past week, my sleep was restless
[4] In the past week, I was happy
[5] In the past week, I felt lonely
[6] In the past week, people were unfriendly
[7] In the past week, I enjoyed life
[8] In the past week, I did not feel like eating.
My appetite was poor
[9] In the past week, I felt sad
[10] In the past week, I felt people dislike me
[11] In the past week, I could not get going
Answer choice:
(1) Hardly ever

(2) Some of the time
(3) Most of the time
6 | P a g e
4. You need to reverse the numerical attributes for the item(s).
(6 pts)
Solution
: is to do it manually. Go into Data View and then manually
recode each
“1” into “3”, etc.

Variables
o Select a variable you wish to RECODE
o Press “Old and New Values”
o Enter Old value and New value, press add, and repeat
o Press Continue
o Make sure everything is fine
o Press ok
o Go check and make sure the variable(s) were recoded (how to
do this?)
Compare OLD variable to the RECODED variable – they should
be reversed.

7 | P a g e
Exercise 4 – CREATING A NEW (SCALE) VARIABLE: FROM

INDIVIDUAL
DEPRESSION ITEMS to a DEPRESSION (SCALE) VARIABLE
(5 pts)
depression, a global variable.
“mechanics”:
o Creating a new variable. What kind? A RECODE
o A RECODE of what? A sum of the score of all depression
items
o To create a Depression Variable.
o But your Student Version is lame!
o How can you do it? Time to “pivot”, to think on your feet and

solve the problem
Variable? Explain your steps. (2 pts)

8 | P a g e
Exercise 5 – DESCRIPTIVE/UNIVARIATE ANALYSIS (10 pts)
LANGUAGE_RE1, and JOB
appropriate). CHOOSE the
correct one! (based on level of measurement)
Fig. 4 – Running Frequency Analysis

Fig. 5 – Advanced Options for Frequencies Statistics
9 | P a g e
2. Click on “Statistics” on the right-hand side to select
measures of cent. and disp.
3. Click “Continue”
4. SPSS creates an OUTPUT file with the results. Don’t close it.
5. Frequencies Results are two-fold:
o Statistics Table

o Individual Frequency Table for each variable
6. NOTE: DON’T BE ALARMED BY A MISSING NUMBER.
Table 1 – MISSING DATA
Gender of
Graduate
COUNTRY
RECODE 1
# of Languages
(fixing missing)
# of Job(s)
currently hold by
graduate

N Valid 130 111 130 130
Missing 65405 65424 65405 65405
7. BONUS POINT ALERT. Can you explain these huge numbers
for missing data? (1 pt)
8. Interpret your results. Use a succinct paragraph to describe
the variables (10 pts).
o Tip: what stands-out to you? What do these basic stats tell
you about the respondent?
o Tip: Focus on Valid Percent and Cumulative Percent

10 | P a g e
ANSWER SHEET
MAKE SURE YOU ANSWER ALL THE QUESTIONS
Exercise 1 – GET SOME BASIC THINGS NOW (10 pts)
1. # of cases in the dataset (2 pts):

2. What each “case” represents? What are they? (2 pts)
3. # of variables in the dataset (2 pts):
4. Name two variables from the dataset and explain what they
are measuring (4 pts):
Exercise 2 – CREATE A NEW VARIABLE: RELIGION (10 pts)
-shot of RELIGION VARIABLE
(Data View) (5 pts)
-shot of RELIGION VARIABLE
(Variable View) (5 pts)
Exercice 3 – RECODING A VARIABLE: DEPRESSION ITEM
(5 pts)
-shot of RECODED

VARIABLE(S) (View) (2 pts)
attributes with numerical values?
Indicate question #. (2 pts)
Exercise 4 – COMPUTING A VARIABLE: FROM
DEPRESSION ITEMS to VARIABLE (5
pts)
-shot of COMPUTED
VARIABLE(S) (Data View)
How did you decide to go about and create a Depression
Variable? Explain your steps. (2 pts)

11 | P a g e
Exercise 5 – DESCRIPTIVE/UNIVARIATE ANALYSIS (10 pts)
plain these huge
numbers for missing data?
everything. What makes sense to discuss,
what gives us a feel and an overview of the data.
o Tip: what stands-out to you? What do these basic stats tell
you about the respondent?
o Tip: Focus on Valid Percent and Cumulative Percent

SPSS LAB 2: Bivariate Analysis
1 | P a g e
OBJECTIVES

(cross-tabs)
ich correlation test to use given level of
measurement (PC, Ch-S., ANOVA)
PLESE NOTE
statistics (t-test, p-value, etc)
(nom., ordinal, interval, ratio)
DEADLINE (the good news)

2 | P a g e
What is correlation?
RELATIONSHIPS BETWEEN TWO CONTINUOUS
VARIABLES (PEARSON_CORRELATION)
EXERCISE 1
Variable 1: TOTAL_SKILL_ENDORSEMENT
Variable 2: TOTAL_BIO_INFO_PROFILE

what they measure.
e learned in
LAB 1.
variable
of association that exists between two
continuous variables
Three ways to run Pearson Correlation
3. SPSS SYNTAX

tax
b. Copy Paste the Following Code and Run it
CORRELATIONS
/VARIABLES = TOTAL_SKILL_ENDORSEMENT
TOTAL_BIO_INFO_PROFILE
/PRINT = TWOTAIL NOSIG
/MISSING=PAIRWISE.
* Explain in a few words your interpretation of the Pearson
Correlation results. (8 pts)
EXERCISE 2 – RUNNING PEARSON COR. with
CATEGORICAL VARIABLES
Let’s look at the difference in the correlation between variable
1 and 2 for GENDER AND RACE.
n add GENDER and RACE

3 | P a g e
EXERCISE 3 – RUNNING T-TEST (categorical and
continuous)
T-Test: Statistical differences between the means of two groups
-Test is different from
Pearson’s Correlation
-Test and
PC. (2 pts)

Hand-on Example with Francois and Adam: Race and # of
Language
Run Two T-Tests:
(1) TOTAL_SKILL_ENDORSEMENT & GENDER
(2) X_TITLE_WORK_1_DEGREE_CONNECTION &
BACHELOR_DEGREE_RE2_DUMMY
-Sample T Test
o Test Variable = Dependent Variable
o Grouping Variable = Independent Variable
*Explain in a few words your interpretation of the results. (8
pts)
RELATIONSHIPS BETWEEN TWO CATEGORICAL

VARIABLES (CHI_SQUARE)
categorical variables, you
CANNOT use Pearson’s correlation.
-Square Test
EXERCISE 4
Variable 1: GENDER
Variable 2: BACHELOR_DEGREE_RE2_DUMMY
coded for a second
time.
second time. (2 pts)
Before you run the Chi-Square,

1. let’s run a cross-tabs to look at the data (make sure to check
off “correlations”):
2. VISUALIZATION:
Try to produce a meaningful
visual representation of the data in a way that helps you make
sense of pattern(s)
Run Chi-Square:
4 | P a g e

-Square”
3. SPSS SYNTAX
b. Copy Paste the Following Code and Run it
CROSSTABS
/TABLES=LANGUAGES_RE2 BY
/FORMAT=AVALUE TABLES
/STATISTICS=CHISQ CORR
/CELLS=COUNT
/COUNT ROUND CELL.
EXERCISE 5

Do you think it would be important to control for RACE when
looking at language spoken? Put differently,
do you think there is a statistical difference between the # of
language spoken between white and non-white?
(tips: use LANGUAGES_RE1, not “RE2”)
and try to produce a meaningful visual
representation of the data in a way that helps you make sense of
pattern(s)
* Which test can you use to test that hypothesis of difference?
(2 pts)
* Explain in a few words your interpretation of the statistical
test. (8 pts)

5 | P a g e
Frank and Adam’s Cheat Sheet

Categorical variables are also known as discrete or qualitative
variables. Categorical variables can be
further categorized as either nominal, ordinal or dichotomous.
Continuous variables are also known as quantitative variables.
Continuous variables can be further
categorized as either interval or ratio variables.
Pearson’s
The Pearson product-moment correlation coefficient, often
shortened to Pearson correlation or Pearson's
correlation, is a measure of the strength and direction of

association that exists between two continuous
variables.
Chi-Square
The Chi-Square Test of Independence is commonly used to test
the following:
Statistical independence or association between two or more
categorical variables.
The Chi-Square Test of Independence can only compare
categorical variables. It cannot make
comparisons between continuous variables or between
categorical and continuous variables. Additionally,
the Chi-Square Test of Independence only assesses associations
between categorical variables, and can
not provide any inferences about causation.

6 | P a g e
t-test
o Skew/Kurtosis
o F-Max
o Levene’s F-test
nd degree

7 | P a g e
Statistically Significant Analysis (as a reference)
CORRELATIONS
TOTAL_BIO_INFO_PROFILE
CORRELATIONS (ONE TAIL)
/VARIABLES = BACHELOR_DEGREE_RE2_DUMMY
PERSONAL_INFO_SUMMARY_WORD_COUNT

CORRELATIONS (ONE TAIL)
/VARIABLES = BACHELOR_DEGREE_RE2_DUMMY
PERSONAL_INFO_SUMMARY_WORD_COUNT_CAT
-SQUARE
CORRELATIONS
/VARIABLES = GENDER DEPRESSION_SCALE
CORRELATIONS
/VARIABLES = BACHELOR_DEGREE_RE1
DEPRESSION_SCALE
CORRELATIONS
/VARIABLES = BACHELOR_DEGREE_RE1
TOP_TEN_SKILLS_RE1

CORRELATIONS
LANGUAGES_RE2
CORRELATIONS
/VARIABLES = LANGUAGES_RE2
-SQUARE
/TABLES=X_TITLE_WORK_1_DEGREE_CONNECTION BY
/FORMAT=AVALUE TABLES
/STATISTICS=CHISQ
ONEWAY X_TITLE_WORK_1_DEGREE_CONNECTION BY
BACHELOR_DEGREE_RE1
/STATISTICS DESCRIPTIVES
/MISSING ANALYSIS.

8 | P a g e
Introduction
Video games for a very long time had been exclusive for those
often called nerds or geeks and often suffer abuse or bullying
because of it. But over time, video games as a hobby is
becoming more acceptable with their peers as handheld consoles

and home consoles are being enjoyed by both children and
adults alike. With the introduction of smartphones and the
thousands upon thousands of accessible games often
downloadable for free on the app store, the hobby is becoming
less of a niche and more of a norm. It is no longer a rare sight
to see a middle age man playing some degree of video game on
their smartphone, computer, or a dedicated home console. The
hobby is now being openly enjoyed by everyone away from the
scrutiny of others. But as video games start to gain traction in
the mainstream media platforms, many begin to wonder or
speculate the negative and positive impacts video games have
on the player’s life. Video games have always been a
controversial topic for debate. It’s often that we hear news
shows blaming violence on video games due to the playful use
of firearms in these games. It is also equally frequent to see
news articles on the internet denying the relationship and
causation of video games and violence. It is to this end that I
would dedicate my time to explore all the impacts video games
have on the player base. I would like to look through both the
positive and negative effect video games have on multiple
aspects of the players life such as their social behaviours in and
out of the game, emotional state, increased aggression (if any),
reasons for playing, and how they view video games as a part of
their life.
I believe that due to the recent rise in popularity and general

public acceptance (mainstream media and personalities openly
admitting or supporting video game titles), it is necessary to
fully understand the effect it has on those that spend most of
their days playing. I see a necessity in understanding these
effects to be able to move forward and focus on the positive
impacts and hopefully lessen the negatives.
For this paper, I would like to focus on the player base that
spends a great deal of time playing video games. I will exclude
more or less casual gamers who play for a few minutes on a bus
stop or those that spend a few hours on a crossword puzzle
game on their phone. I will also narrow down my research on
those video games that include a community to talk, interact,
and play with or against to be able to easily compare and
contrast the players’ online persona with their real life
counterpart. I would further narrow this down to the largest
target demographic of video games, teenagers (13-19 years old)
and young adults (20-30), due to their larger amount of free
time to dedicate to the hobby.
Literature Review
I have first taken a look at the article, “Influences of motives to
play and time spent gaming on the negative consequences of
adolescent online computer gaming,” by Charlotta Hellstrom,
Kent Nilsson, Jerzy Leppert, and Cecilia Aslund. In this article,
it was found that most adolescents reported negative

consequences from their gaming habits such as lack of sleep,
unable to complete or start their school works, and conflict with
their parents or siblings. Aside from these common
consequences, some also report skipping school for video
games, and having no time to play or interact with their friends
in real life. The article also found a relation between the
amounts of time spent gaming and experiencing negative
consequences due to it. Finally, the authors focused on motives
as a driving factor for the negative consequences it has on the
players. It was found that if a player uses video game as an
escape from their daily lives, they are at risk for greater
negative consequences as opposed to those that play video
games for fun or for social motives. Comment by François
Lachapelle: No need for that. Use APA format
From the article, “The Multiple Dimensions of video game
effects,” by Douglas Gentile, found similar trend of increased
negative consequence with increased time spent playing video
games. In addition, this research also found a link or relation
between time spent playing video games and obesity. But
despite that, it also focused on the potential positives video
games could have such as the integration of educational video
games for schools. This article further looks into different
dimensions of video games such as content, context, structure,
and mechanics to fully grasp different effects since video games
can be a nebulous subject. Content can vary greatly from

educational to violent, and context can include mindless
violence to organized team work. “Critics often cite the
research on the effects of violent video games, whereas
proponents often cite the research on perceptual skills. The
irony is that both the critics and proponents are correct about
the effects that games can have. The flaw is that they extend
their arguments to conclude that video games are ultimately
harmful or beneficial.” (Gentile, 2011). Comment by François
Lachapelle: Quote too long. Also need page number. See APA
format.
Through this research, I found it abundantly clear that there are
negative consequences for playing video games. But unlike how
it’s painted in the media, there are positive effects as well.
Things like increased performance in team oriented activities,
decision making abilities, improved hand-eye coordination are
just some common positive effect video games have on the
players. Much like the first article reviewed, many researches
fall into the category of pros and cons and often find results
supporting their conclusions. While not necessarily incorrect, it
ignores a large chunk of consequences of video games. Similar
to the second article, I wish to pursue this topic from both sides
of the spectrum and expose all consequences both positive and
negative to let the audience decide whether or not video games
is ultimately good for their lives. I also believe that as video
game reach spreads towards both the younger and the young

adult population, I find it necessary to extend the sample age
range from solely teenagers to include young adults as well.
Conclusion
Video games today are more than just a hobby to some. It can
be used as an escape from their mundane lives or a mean to
socialize or interact with others. Due to the recent acceptance of
video games to our society, I can firmly state that it is
necessary to put further research on effects it has on the
populations. Understanding these effects to the fullest would
allow us to make changes or regulate certain types of games to
better cater to the audience to focus on the positive impacts and
reduce the negatives. While I don’t believe it’s possible to
eliminate the negatives entirely, I know that awareness of the
negatives is a great first step towards the betterment of society
and the video gaming community all together.

References
Hellström, C., Nilsson, K. W., Leppert, J., Åslund, C.,
Medicinska och farmaceutiska vetenskapsområdet, Uppsala
universitet, . . . Centrum för klinisk forskning, Västerås. (2012).
Influences of motives to play and time spent gaming on the
negative consequences of adolescent online computer gaming.
Computers in Human Behavior, 28(4), 1379-1387.
doi:10.1016/j.chb.2012.02.023

Gentile, D. A. (2011). The multiple dimensions of video game
effects. Child Development Perspectives, 5(2), 75-81.
doi:10.1111/j.1750-8606.2011.00159.
Clement, a few comments here. First, your paper is 3 and a half
pages. We asked for 4-5 pages. Secondly, how can you do a lit
review while discussing two articles as listed in your
references? In the future, I would expect more efforts.
HYPOTHESES:
The conclusion was supposed to be where you explain in detail
all your hypotheses. Please work on it and resubmit as soon as
possible. I saw online that you tried to re-submit your
hypotheses. The problem is that in the conclusion here, I don’t
see one hypothesis. Again, as Dr. Bartolic mentioned in her
feedback to you online, you are not ready for the next step.
Need to work hard on your hypotheses now.
POPUPATION:
In theory, your target population is good, but in practice, I don’t
think its realistic. Remember you need to find 100 participants
that will fit that. Please review accordingly.
For this paper, I would like to focus on the player base that
spends a great deal of time playing video games. I will exclude

more or less casual gamers who play for a few minutes on a bus
stop or those that spend a few hours on a crossword puzzle
game on their phone. I will also narrow down my research on
those video games that include a community to talk, interact,
and play with or against to be able to easily compare and
contrast the players’ online persona with their real life
counterpart. I would further narrow this down to the largest
target demographic of video games, teenagers (13-19 years old)
and young adults (20-30), due to their larger amount of free
time to dedicate to the hobby.
Grade
Intro: 7
Synthesis and critique of relevant research materials: 10
Conclusion: 5
Style: 3.5
#75465 Topic: Strategies that Promote Culturally Sensitive
Health Care
Number of Pages: 4 (Double Spaced)
Number of sources: 1

Writing Style: APA
Type of document: Statistics Project
Academic Level:Undergraduate
Category: Sociology
Language Style: English (U.S.)
Order Instructions: Attached
This is the last part of the research project. I did pretty awful
for the previous parts because the prof's instructions are so
unclear, probably need a lot of communication to do through
this last part. The last part is mainly about what is result of the
research and survey you created. You will need to use
SPSS(using T-test/one way anova etc.) to make graphs and
statistics in order to show the professor what you get from the
survey.

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