A Phi coefficient is a non-parametric test used to measure the strength of association between two dichotomous (binary) variables. It analyzes the variables in a 2x2 contingency table to determine if there is a non-random pattern across the four cells. The Phi coefficient ranges in value from -1 to 1, similar to a correlation coefficient, with values closer to 1 or -1 indicating a stronger association.
The phi coefficient is that system of correlation which is computed between two variables, where neither of them is available in a continuous measures and both of them are expressed in the form of natural or genuine dichotomies. This presentation slides describes the concept and procedures to do the computation of phi coefficient of correlation.
The phi coefficient is that system of correlation which is computed between two variables, where neither of them is available in a continuous measures and both of them are expressed in the form of natural or genuine dichotomies. This presentation slides describes the concept and procedures to do the computation of phi coefficient of correlation.
Biserial correlation is computed between two variables when one of them is in continuous measure and the other is reduced to artificial dichotomy (forced division into two categories). This Presentation slides explains the condition and assumption to use biserial correlation with appropriate illustrations.
Validity:
Validity refers to how well a test measures what it is purported to measure.
Types of Validity:
1. Logic valididty:
Validity which is in the form of theory, statements. It has 2 types.
I. Face Validity:
It is the extent to which the measurement method appears “on its face” to measure the construct of interest.
• Example:
• suppose you were taking an instrument reportedly measuring your attractiveness, but the questions were asking you to identify the correctly spelled word in each list
II. Content Validity:
Measuring all the aspects contributing to the variable of the interest.
Example:
For physical fitness temperature, height and stamina are supposed to be assess then a test of fitness must include content about temperatures, height and stamina.
2. Criterion
It is the extent to which people’s scores are correlated with other variables or criteria that reflect the same construct
Example:
An IQ test should correlate positively with school performance.
An occupational aptitude test should correlate positively with work performance.
Types of Criterion Validity
Concurrent validity:
• When the criterion is something that is happening or being assessed at the same time as the construct of interest, it is called concurrent validity.
• Example:
Beef test.
Predictive validity:
• A new measure of self-esteem should correlate positively with an old established measure. When the criterion is something that will happen or be assessed in the future, this is called predictive validity.
• Example:
GAT, SAT
Other types of validity
Internal Validity:
It is basically the extent to which a study is free from flaws and that any differences in a measurement are due to an independent variable and nothing else
External Validity
• It is the extent to which the results of a research study can be generalized to different situations, different groups of people, different settings, different conditions, etc.
Tetrachoric correlation is used as a measure of relationship between two variables when both are reduced to artificial dichotomy as neither of them is available in terms of continuous measure like scores. This presentation slides explains the concept and procedures to do the computation of tetrachoric correlation.
Stanford-Binet Intelligence Scale is an individually administered test that examines the cognitive ability of children and adults falling the age-range of 2 to 85+ years. It examines children with intellectual and developmental deficiencies as well as intellectually gifted individuals. This test originated from The Binet-Simon Scale (1905) and had undergone five major revisions. This presentation gives an overview of all five of them with most emphasis on the fifth edition by Roid (2003).
Biserial correlation is computed between two variables when one of them is in continuous measure and the other is reduced to artificial dichotomy (forced division into two categories). This Presentation slides explains the condition and assumption to use biserial correlation with appropriate illustrations.
Validity:
Validity refers to how well a test measures what it is purported to measure.
Types of Validity:
1. Logic valididty:
Validity which is in the form of theory, statements. It has 2 types.
I. Face Validity:
It is the extent to which the measurement method appears “on its face” to measure the construct of interest.
• Example:
• suppose you were taking an instrument reportedly measuring your attractiveness, but the questions were asking you to identify the correctly spelled word in each list
II. Content Validity:
Measuring all the aspects contributing to the variable of the interest.
Example:
For physical fitness temperature, height and stamina are supposed to be assess then a test of fitness must include content about temperatures, height and stamina.
2. Criterion
It is the extent to which people’s scores are correlated with other variables or criteria that reflect the same construct
Example:
An IQ test should correlate positively with school performance.
An occupational aptitude test should correlate positively with work performance.
Types of Criterion Validity
Concurrent validity:
• When the criterion is something that is happening or being assessed at the same time as the construct of interest, it is called concurrent validity.
• Example:
Beef test.
Predictive validity:
• A new measure of self-esteem should correlate positively with an old established measure. When the criterion is something that will happen or be assessed in the future, this is called predictive validity.
• Example:
GAT, SAT
Other types of validity
Internal Validity:
It is basically the extent to which a study is free from flaws and that any differences in a measurement are due to an independent variable and nothing else
External Validity
• It is the extent to which the results of a research study can be generalized to different situations, different groups of people, different settings, different conditions, etc.
Tetrachoric correlation is used as a measure of relationship between two variables when both are reduced to artificial dichotomy as neither of them is available in terms of continuous measure like scores. This presentation slides explains the concept and procedures to do the computation of tetrachoric correlation.
Stanford-Binet Intelligence Scale is an individually administered test that examines the cognitive ability of children and adults falling the age-range of 2 to 85+ years. It examines children with intellectual and developmental deficiencies as well as intellectually gifted individuals. This test originated from The Binet-Simon Scale (1905) and had undergone five major revisions. This presentation gives an overview of all five of them with most emphasis on the fifth edition by Roid (2003).
Please answer all questionsDefine1. Wild type –2. Epistasis –.pdfdhavalbl38
Please answer all questions
Define
1. Wild type –
2. Epistasis –
Fill in the Blank
3. Two genes may interact effecting one trait and resulting in novel phenotypes with an F2
phenotypic ________________ ratio.
4. Under ________________ conditions, conditionally lethal mutants will die.
True or False
5. Penetrance is the degree or intensity with which a particular genotype is expressed in a
phenotype.
6. Continuous traits are usually controlled by multiple genes and may be influenced by the
environment.
Short Answer
7. Describe the inheritance pattern(s) of blood types.
8. When studying a newly discovered flowering plant in Costa Rica, you collect data on its petal
color in an effort to determine how this trait may be inherited. You find that 852 flowers are red,
331 are white and 389 are yellow. What are the possible inheritance patterns, and how could you
definitely conclude which of these possibilities is the true mode of inheritance?
Solution
Wild type- wild type phenotype refers to characteristics that occurs naturally in a breeding
population.
Epistasis - it refers to a phenomena when one gene’s expression is dependent on the presence of
another gene(s), called the modifier gene.
9:16
Let\'s take an example of comb varieties in chicken
We have two pure breeds : wyandotte whose phenotype is a rose comb and brahmas having the
phenotype of a pea comb, when crossed with each other, they create a completely new phenotype
of a walnut comb. Let\'s look at the ratio:
Parental cross: rose comb x pea comb
F1 : all walnut combs
F2 : 9 walnut : 3 rose: 3peas : 1 single
Hence the ratio is 9:16
Under the restrictive or nonpermissive condition conditions, conditionally lethal mutants will
die.
Conditionally lethal mutants are those which have an allele that will make the survival of the
species difficult only in a specific condition.
True. for example if a mutation in a gene gives the species a particular characterisitc which has
97%penetrance, only 97% of those with the mutation will develop that characteristic and 3 %
will not.
True. multiple genes affect continuous traits. For such complex gene interactions, the traits are
influenced by environmental factors too. For example, height and skin color.
There are four basic blood types which are A, B, AB and O and together with the Rh factor,
these can be +ve or -ve.
A and B are dominant, while O is recessive.
Blood type
A
B
O
A
A
AB
A
B
AB
B
B
O
A
B
O
For Rh factor, Rh (+)ve is dominant.
So if the any one or both parents have Rh (+)ve factor, the progeny will be Rh(+)ve.
If both parents are Rh(-)ve, the progeny will be Rh (-)ve
Father
Mother
D
D
d
Dd
Dd
d
Dd
Dd
100% Rh (+)ve children
Father
Mother
D
d
d
Dd
dd
d
Dd
dd
50% Rh(+)ve children
Red: 852
White: 331
Yellow:389
According to the given data, red is dominant and white is recessive and yellow is a result of
incomplete dominance of both white and red phenotype.
It can\'t be codominance, because codominance involves both phenotypes being expressed in
di.
I hope you like it. I made this has a review item for my quiz! I'm in 7th grade so I might of not of covered every piece of information but I hope it fits what your looking for. Please give me responses on if its good or bad.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
2. A Phi coefficient is a non-parametric test of
relationships that operates on two dichotomous
(or dichotomized) variables. It intersects
variables across a 2x2 matrix to estimate
whether there is a non-random pattern across
the four cells in the 2x2 matrix. Similar to a
parametric correlation coefficient, the possible
values of a Phi coefficient range from -1 to 0 to
+1.
3. A Phi coefficient is a non-parametric test of
relationships that operates on two dichotomous
(or dichotomized) variables. It intersects
variables across a 2x2 matrix to estimate
whether there is a non-random Dichotomous
pattern across
means that the
the four cells in the 2x2 matrix. data can take Similar on
to a
only two values.
parametric correlation coefficient, the possible
values of a Phi coefficient range from -1 to 0 to
+1.
4. A Phi coefficient is a non-parametric test of
relationships that operates on two dichotomous
(or dichotomized) variables. It intersects
variables across a 2x2 matrix to estimate
whether there is a non-random pattern across
the four cells in the 2x2 matrix. Similar to a
parametric correlation coefficient, the possible
values of a Phi coefficient range from -1 to 0 to
+1.
Like –
• Male/Female
• Yes/No
• Opinion/Fact
• Control/Treatment
5. It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
6. It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
What does
this mean?
7. It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Here is an
example
Data Set
8. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
9. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Two Dichotomous
Variables
10. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A
B
C
D
E
F
G
H
I
J
K
L
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
11. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1
B 1
C 1
D 2
E 2
F 1
G 2
H 2
I 2
J 1
K 1
L 2
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
12. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
13. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Male
14. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Male
15. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Female
16. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Female
17. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Single
18. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Single
19. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Married
20. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
Married
21. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
22. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
coefficient, the possible
values of a Phi coefficient
range from -1 to 0 to +1.
23. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married
Single
24. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married
Single
25. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married
Single
26. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married
Single
27. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single
28. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single
29. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single
1
30. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single
1
2
31. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single
1
2
3
32. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single
1
2
3
4
33. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single
1
2
3
4
5
34. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single
1
2
3
4
5
35. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single 5
36. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single 5
37. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single 5
1
38. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single 5
1
2
39. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single 5
1
2
3
40. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single 5
1
2
3
4
41. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1
Single 5
1
2
3
4
42. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1 4
Single 5
43. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1 4
Single 5
44. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1 4
Single 5
45. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1 4
Single 5
1
46. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1 4
Single 5
1
2
47. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1 4
Single 5
1
2
48. Subjects Gender
1= Male
2= Female
Marital Status
1 = Single
2 = Married
A 1 2
B 1 1
C 1 1
D 2 2
E 2 2
F 1 1
G 2 2
H 2 1
I 2 2
J 1 1
K 1 1
L 2 1
It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1 4
Single 5 2
49. It intersects variables
across a 2x2 matrix to
estimate whether there is
a non-random pattern
across the four cells in the
2x2 matrix. Similar to a
parametric correlation
GENDER
coefficient, the possible
Male Female
values of a Phi coefficient
range from -1 to 0 to +1.
MARITAL
STATUS
Married 1 4
Single 5 2
50. Similar to a parametric correlation coefficient,
the possible values of a Phi coefficient range
from -1 to 0 to +1.
51. A Phi coefficient of 0 would indicate that there is
no systematic pattern across the 2x2 matrix. A
negative Phi coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“lower’ coded values on the other variable. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
52. A Phi coefficient of 0 would indicate that there is
no systematic pattern across the 2x2 matrix. A
negative Phi coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable GENDER
are associated with
“lower’ coded values Male on the Female
other variable. A
Married 3 3
positive MARITAL
STATUS
phi-Single coefficient 3 would 3
indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
53. A Phi coefficient of 0 would indicate that there is
no systematic pattern across the 2x2 matrix. A
negative Phi coefficient would indicate a
systematic pattern in which or
“higher” coded
values on one variable are associated with
“lower’ coded values on the other variable. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
54. A Phi coefficient of 0 would indicate that there is
no systematic pattern across the 2x2 matrix. A
negative Phi coefficient would indicate a
systematic pattern in which or
“higher” coded
values on one variable are associated with
“lower’ coded values on the GENDER
other variable. A
positive phi-coefficient Male would Female
indicate a
systematic MARITAL
pattern Married in which 5 “higher” 5
coded
STATUS
Single 1 1
values on one variable are associated with
“higher” coded values on the other variable.
55. A Phi coefficient of 0 would indicate that there is
no systematic pattern across the 2x2 matrix. A
negative Phi coefficient would indicate a
systematic pattern in which or
“higher” coded
values on one variable are associated with
“lower’ coded values on the GENDER
other variable. A
positive phi-coefficient Male would Female
indicate a
systematic MARITAL
pattern Married in which 5 “higher” 5
coded
STATUS
Single 1 1
values on one variable are associated with
“higher” coded values on the other variable.
Being male or female does not make you any
more likely to be married or single
56. A Phi coefficient of 0 would indicate that there is
no systematic pattern across the 2x2 matrix. A
negative Phi coefficient would indicate a
systematic pattern in which or
“higher” coded
values on one variable are associated with
“lower’ coded values on the GENDER
other variable. A
positive phi-coefficient Male would Female
indicate a
systematic MARITAL
pattern Married in which 5 “higher” 5
coded
STATUS
Single 1 1
values on one variable are associated with
“higher” coded values on the other variable.
Being male or female does not make you any
more likely to be married or single
57. A positive Phi coefficient would indicate that
most of the data are in the diagonal cells.A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
58. A positive Phi coefficient would indicate that
most of the data are in the diagonal cells.
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
59. A positive Phi coefficient would indicate that
most of the data are in the diagonal cells.
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married
Single
60. A positive Phi coefficient would indicate that
most of the data are in the diagonal cells.
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married
Single
61. A positive Phi coefficient would indicate that
most of the data are in the diagonal cells.
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married
Single
For example
62. A positive Phi coefficient would indicate that
most of the data are in the diagonal cells.
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married 4
Single 5
63. A positive Phi coefficient would indicate that
most of the data are in the diagonal cells.
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married 4 1
Single 2 5
64. A positive Phi coefficient would indicate that
most of the data are in the diagonal cells.
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married 4 1
Single 2 5
65. positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
GENDER
Male Female
MARITAL
STATUS
Married 4 1
Single 2 5
Phi-
Coefficient
+.507
66. In terms of how to interpret this value, here is a helpful rule of
thumb:
A positive Phi coefficient would indicate that
most of the data are in the diagonal cells.
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married 4 1
Single 2 5
+.507
Value of r Strength of relationship
-1.0 to -0.5 or 1.0 to 0.5 Strong
-0.5 to -0.3 or 0.3 to 0.5 Moderate
-0.3 to -0.1 or 0.1 to 0.3 Weak
-0.1 to 0.1 None or very weak
67. In terms of how to interpret this value, here is a helpful rule of
thumb:
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
Value of r Strength of relationship
-1.0 to -0.5 or 1.0 to 0.5 Strong
-0.5 to -0.3 or 0.3 to 0.5 Moderate
-0.3 to -0.1 or 0.1 to 0.3 Weak
-0.1 to 0.1 None or very weak
GENDER
Male Female
MARITAL
STATUS
Married 4 1
Single 2 5
+.507
So, the interpretation would be, that there is a strong relationship
between marital status and gender with being male making it
more likely you are married and being female making it more likely
to be single.
68. In terms of how to interpret this value, here is a helpful rule of
thumb:
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
Value of r Strength of relationship
-1.0 to -0.5 or 1.0 to 0.5 Strong
-0.5 to -0.3 or 0.3 to 0.5 Moderate
-0.3 to -0.1 or 0.1 to 0.3 Weak
-0.1 to 0.1 None or very weak
GENDER
Male Female
MARITAL
STATUS
Married 4 1
Single 2 5
+.507
So, the interpretation would be, that there is a strong relationship
between marital status and gender with being male making it
more likely you are married and being female making it more likely
to be single.
69. In terms of how to interpret this value, here is a helpful rule of
thumb:
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
Value of r Strength of relationship
-1.0 to -0.5 or 1.0 to 0.5 Strong
-0.5 to -0.3 or 0.3 to 0.5 Moderate
-0.3 to -0.1 or 0.1 to 0.3 Weak
-0.1 to 0.1 None or very weak
GENDER
Male Female
MARITAL
STATUS
Married 4 1
Single 2 5
+.507
So, the interpretation would be, that there is a strong relationship
between marital status and gender with being male making it
more likely you are married and being female making it more likely
you are single.
70. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on the other variable.
71. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married
Single
72. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married
Single
73. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married 1 4
Single 5 2
74. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Male Female
MARITAL
STATUS
Married 1 4
Single 5 2
Phi-
Coefficient
-.507
75. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are GENDER
associated with
“higher” coded values on Male the other Female
variable.
MARITAL
STATUS
Married 1 4
Single 5 2
Phi-
Coefficient
-.507
So, the interpretation would be, that there is a strong relationship
between marital status and gender with being male making it
more likely that you are single and being female making it more
likely you are married.
76. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are GENDER
associated with
“higher” coded values on Male the other Female
variable.
MARITAL
STATUS
Married 1 4
Single 5 2
Phi-
Coefficient
-.507
So, the interpretation would be, that there is a strong relationship
between marital status and gender with being male making it
more likely that you are single and being female making it more
likely you are married.
77. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are GENDER
associated with
“higher” coded values on Male the other Female
variable.
MARITAL
STATUS
Married 1 4
Single 5 2
Phi-
Coefficient
-.507
So, the interpretation would be, that there is a strong relationship
between marital status and gender with being male making it
more likely that you are single and being female making it more
likely you are married.
78. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are GENDER
associated with
“higher” coded values on Male the other Female
variable.
MARITAL
STATUS
Married 1 4
Single 5 2
Phi-
Coefficient
-.507
So, the interpretation would be, that there is a strong relationship
between marital status and gender with being male making it
more likely that you are single and being female making it more
likely you are married.
79. A negative Phi coefficient would indicate that
most of the data are in the off-diagonal cells. A
positive phi-coefficient would indicate a
systematic pattern in which “higher” coded
values on one variable are associated with
“higher” coded values on tGhENeD oERther variable.
Note: the sign (+ or -) is irrelevant. The main thing to consider is
the strength of the relationship between the two variables and
then look at the 2x2 matrix to determine what it means.
Male Female
MARITAL
STATUS
Married 1 4
Single 5 2
Phi-
Coefficient
-.507
80. Phi Coefficient Example
• A researcher wishes to determine if a significant
relationship exists between the gender of the
worker and if they experience pain while
performing an electronics assembly task.
• One question asks “Do you experience pain
while performing the assembly task? Yes No”
• The second question asks “What is your
gender? ___ Male ___ Female”
81. • A researcher wishes to determine if a significant
relationship exists between the gender of the
worker and if they experience pain while
performing an electronics assembly task.
e question asks “Do you experience pain while
performing the assembly task? Yes No”
• The second question asks “What is your
gender? ___ Male ___ Female”
82. • A researcher wishes to determine if a significant
relationship exists between the gender of the
worker and if they experience pain while
performing an electronics assembly task.
e question asks “Do you experience pain while
performing the assembly task? Yes No”
• The second question asks “What is your
gender? ___ Male ___ Female”
83. Two survey questions are asked of the
workers:
• One question asks “Do you experience
pain while performing the assembly
task? Yes No”
• The second question asks “What is your
gender? ___ Male ___ Female”
84. Two survey questions are asked of the
workers:
• “Do you experience pain while
performing the assembly task? Yes No”
• The second question asks “What is your
gender? ___ Male ___ Female”
85. Two survey questions are asked of the
workers:
• “Do you experience pain while
performing the assembly task? Yes No”
• “What is your gender?
___ Female ___ Male”
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86. Step 1: Null and Alternative Hypotheses
• Ho: There is no relationship
between the gender of the worker
and if they feel pain while
performing the task.
• H1: There is a significant
relationship between the gender of
the worker and if they feel pain
while performing the task.
87. Step 1: Null and Alternative Hypotheses
• Ho: There is no relationship
between the gender of the worker
and if they feel pain while
performing the task.
• H1: There is a significant
relationship between the gender of
the worker and if they feel pain
while performing the task.
88. Step 1: Null and Alternative Hypotheses
• Ho: There is no relationship
between the gender of the worker
and if they feel pain while
performing the task.
• H1: There is a significant
relationship between the gender of
the worker and if they feel pain
while performing the task.
89. Step 2: Determine dependent and
independent variables and their formats.
90. Step 2: Determine dependent and
independent variables and their formats.
91. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is dichotomous, dependent
92. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is dichotomous, dependent
An independent variable
is the variable doing the
causing or influencing
93. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is the dependent variable
94. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is the dependent variable
95. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is the dependent variable
A dependent variable is
the thing being caused
or influenced by the
independent variable
96. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is the dependent variable
97. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is the dependent variable
• Gender is a dichotomous variable
98. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is the dependent variable
• Gender is a dichotomous variable
In this study it can only
take on two variables:
1 = Male
2 = Female
99. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is the dependent variable
• Gender is a dichotomous variable
• Feeling pain is a dichotomous variable
100. Step 2: Determine dependent and
independent variables and their formats.
• Gender is the independent variable
• Feeling pain is the dependent variable
• Gender is a dichotomous variable
• Feeling pain is a dichotomous variable
In this study it can only
take on two variables:
1 = Feel Pain
2 = Don’t Feel Pain
102. Step 3: Choose test statistic
• Because we are investigating the relationship between
two dichotomous variables, the appropriate test statistic
is the Phi Coefficient
103. Step 4: Run the Test
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes
to the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No
to the pain item (8)
104. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes
to the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No
to the pain item (8)
105. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes
to the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No
to the pain item (8)
106. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes
to the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No
to the pain item (8)
107. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No
to the pain item (8)
108. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No
to the pain item (8)
109. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 4 B E
No C D F
Total G H
110. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 4 6 E
No C D F
Total G H
111. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 4 6 E
No 11 D F
Total G H
112. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 4 6 E
No 11 8 F
Total G H
113. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 4 6 E
No 11 8 F
Total G H
114. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 4 6 E
No 11 8 F
Total 15 H
115. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 4 6 E
No 11 8 F
Total 14 H
116. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 4 6 E
No 11 8 F
Total 14 14
117. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 1 12 E
No 13 2 F
Total 14 14
118. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 1 12 13
No 13 2 F
Total 14 14
119. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 1 12 13
No 13 2 F
Total 12 14
120. Step 4: Run the Test
• The Phi Coefficient should be set up as follows:
– Box A contains the number of Males that said Yes to
the pain item (4)
– Box B contains the number of Females that said Yes to
the pain item (6)
– Box C contains the number of Males that said No to
the pain item (11)
– Box D contains the number of Females that said No to
the pain item (8)
Males Females Total
Yes 1 12 13
No 13 2 15
Total 12 14
123. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 13
No c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= .788
124. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 13
No c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
ퟏퟐ∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= .788
125. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 13
No c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗ퟏퟑ −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= .788
126. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 13
No c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(ퟏ∗2)
15∗13∗14∗14
=
154.0
195.5
= .788
127. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 13
No c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗ퟐ)
15∗13∗14∗14
=
154.0
195.5
= .788
128. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 13
No c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(풆푓푔ℎ)
=
12∗13 −(1∗2)
ퟏퟓ∗13∗14∗14
=
154.0
195.5
= .788
129. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 13
No c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒풇푔ℎ)
=
12∗13 −(1∗2)
15∗ퟏퟑ∗14∗14
=
154.0
195.5
= .788
130. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 13
No c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗ퟏퟒ∗14
=
154.0
195.5
= .788
131. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 13
No c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗ퟏퟒ
=
154.0
195.5
= .788
132. Phi Coefficient Test Formula
Males Females Total
Yes a = 1 b = 12 e = 15
No c = 13 d = 2 f = 13
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
ퟏퟓퟒ.ퟎ
ퟏퟗퟓ.ퟓ
= .788
133. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 1 b = 12 e = 13
No - Pain c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= -.788
134. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 1 b = 12 e = 13
No - Pain c = 13 d = 2 f = 15
Total g = 14 h =14
Result: there is a strong relationship between gender and feeling pain with
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= -.788
females feeling more pain than males.
135. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 1 b = 12 e = 13
No - Pain c = 13 d = 2 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= -.788
Remember that with the Phi-coefficient the sign (-/+) is irrelevant
136. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 1 b = 12 e = 13
No - Pain c = 13 d = 2 f = 15
Total g = 14 h =14
We could have switched the columns and have gotten the same value but
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= -.788
with a different sign.
137. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 1 b = 12 e = 13
No - Pain c = 13 d = 2 f = 15
Total g = 14 h =14
We could have switched the columns and have gotten the same value but
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= -.788
with a different sign.
138. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 12 b = 1 e = 13
No - Pain c = 13 d = 2 f = 15
Total g = 14 h =14
We could have switched the columns and have gotten the same value but
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= -.788
with a different sign.
139. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 12 b = 1 e = 13
No - Pain c = 13 d = 2 f = 15
Total g = 14 h =14
We could have switched the columns and have gotten the same value but
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= -.788
with a different sign.
140. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 12 b = 1 e = 13
No - Pain c = 2 d = 13 f = 15
Total g = 14 h =14
We could have switched the columns and have gotten the same value but
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
12∗13 −(1∗2)
15∗13∗14∗14
=
154.0
195.5
= -.788
with a different sign.
141. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 12 b = 1 e = 13
No - Pain c = 2 d = 13 f = 15
Total g = 14 h =14
We could have switched the columns and have gotten the same value but
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
ퟏퟐ∗ퟏퟑ −(ퟏ∗ퟐ)
15∗13∗14∗14
=
154.0
195.5
= -.788
with a different sign.
142. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 12 b = 1 e = 13
No - Pain c = 2 d = 13 f = 15
Total g = 14 h =14
We could have switched the columns and have gotten the same value but
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
ퟏ∗ퟐ −(ퟏퟐ∗ퟏퟑ)
15∗13∗14∗14
=
154.0
195.5
= -.788
with a different sign.
143. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 12 b = 1 e = 13
No - Pain c = 2 d = 13 f = 15
Total g = 14 h =14
We could have switched the columns and have gotten the same value but
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
ퟏ∗ퟐ −(ퟏퟐ∗ퟏퟑ)
15∗13∗14∗14
=
154.0
195.5
= +.788
with a different sign.
144. Phi Coefficient Test Formula
Males Females Total
Yes - Pain a = 12 b = 1 e = 13
No - Pain c = 2 d = 13 f = 15
Total g = 14 h =14
Φ =
(푏푐 −푎푑)
(푒푓푔ℎ)
=
ퟏ∗ퟐ −(ퟏퟐ∗ퟏퟑ)
15∗13∗14∗14
=
154.0
195.5
= +.788
The Result is the Same: there is a strong relationship between gender and
feeling pain with females feeling more pain than males.
146. Step 5: Conclusions
There is a strong relationship between gender and pain
• Both males and females have pain (or no pain) at equal
frequencies.
147. Step 5: Conclusions
There is a strong relationship between gender and pain
with more females reporting pain than males.
• Both males and females have pain (or no pain) at equal
frequencies.
148. Step 5: Conclusions
There is a strong relationship between gender and pain
with more females reporting pain than males.
• Both males and females have pain (or no pain) at equal
frequencies.
Males Females
Yes - Pain 1 12
No - Pain 13 2
149. Step 5: Conclusions
There is a strong relationship between gender and pain
with more females reporting pain than males.
• Both males and females have pain (or no pain) at equal
frequencies.
Males Females
Yes - Pain 1 12
No - Pain 13 2
150. Step 5: Conclusions
There is a strong relationship between gender and pain
with more females reporting pain than males.
• Both males and females have pain (or no pain) at equal
frequencies.
Males Females
Yes - Pain 1 12
No - Pain 13 2