The document discusses test validity and reliability. It provides formulas for calculating validity, including point biserial correlation, and shows sample calculations for item validity. Reliability is assessed using split-half reliability and Pearson product-moment correlation. Sample reliability calculations are shown for several test items. The results indicate some items have high reliability while others do not. Guidelines are provided for interpreting reliability coefficients.
Skillogic Knowledge Solutions is one of the top training institute in India. Skillogic is providing Six Sigma Certification training in Bangalore, Chennai, Delhi, Mumbai, Hyderabad, Pune and many other cities of India.
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Application of Statistical and mathematical equations in Chemistry Part 2Awad Albalwi
Application of Statistical and mathematical equations in Chemistry
Part 2
Accuracy
Precision
Propagation of Error
Confidence Limits
F-Test Values
Student’s t-test
Paired Sample t-test
Q test
Least Squares Method
correlation coefficient
Skillogic Knowledge Solutions is one of the top training institute in India. Skillogic is providing Six Sigma Certification training in Bangalore, Chennai, Delhi, Mumbai, Hyderabad, Pune and many other cities of India.
If you are looking for Six Sigma black belt or green belt training course in Chennai, along with certification then visit: http://in.skillogic.com/six-sigma-training/six-sigma-certification-chennai/
Application of Statistical and mathematical equations in Chemistry Part 2Awad Albalwi
Application of Statistical and mathematical equations in Chemistry
Part 2
Accuracy
Precision
Propagation of Error
Confidence Limits
F-Test Values
Student’s t-test
Paired Sample t-test
Q test
Least Squares Method
correlation coefficient
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 9: Inferences from Two Samples
9.1: Inferences about Two Proportions
Optimization techniques in formulation Development- Plackett Burmann Design a...D.R. Chandravanshi
It is the process of finding the best way of using the existing resources while taking in to the account of all the factors that influences decisions in any experiment.
The objective of designing quality formulation is achieved by various optimization techniques.
In Pharmacy word “optimization” is found in the literature referring to study of the formula. In formulation development process generally experiments by a series of logical steps, carefully controlling the variables and changing one at a time until satisfactory results are obtained.
T test - method of testing hypotheses about the mean of a small sample drawn from a normally distributed population when the population standard deviation is unknown.
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Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
simple linear regression - brief introductionedinyoka
Goal of regression analysis: quantitative description and
prediction of the interdependence between two or more variables.
• Definition of the correlation
• The specification of a simple linear regression model
• Least squares estimators: construction and properties
• Verification of statistical significance of regression model
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 9: Inferences from Two Samples
9.1: Inferences about Two Proportions
Optimization techniques in formulation Development- Plackett Burmann Design a...D.R. Chandravanshi
It is the process of finding the best way of using the existing resources while taking in to the account of all the factors that influences decisions in any experiment.
The objective of designing quality formulation is achieved by various optimization techniques.
In Pharmacy word “optimization” is found in the literature referring to study of the formula. In formulation development process generally experiments by a series of logical steps, carefully controlling the variables and changing one at a time until satisfactory results are obtained.
T test - method of testing hypotheses about the mean of a small sample drawn from a normally distributed population when the population standard deviation is unknown.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
simple linear regression - brief introductionedinyoka
Goal of regression analysis: quantitative description and
prediction of the interdependence between two or more variables.
• Definition of the correlation
• The specification of a simple linear regression model
• Least squares estimators: construction and properties
• Verification of statistical significance of regression model
ppt Coefficient Of Correlation By Spearmans Rank Method And Concurrent Deviation Method.
it contains steps to solve questions with these methods along with some example
1. Rentang
2. Rentang antarkuartil
3. Rentang semikuartil
4. Rentang a-b persentil
5. Simpangan baku
6. Variansi
7. Ukuran penyebaaran relatif
8. Bilangan baku
Week 4 Lecture 12 Significance Earlier we discussed co.docxcockekeshia
Week 4 Lecture 12
Significance
Earlier we discussed correlations without going into how we can identify statistically
significant values. Our approach to this uses the t-test. Unfortunately, Excel does not
automatically produce this form of the t-test, but setting it up within an Excel cell is fairly easy.
And, with some slight algebra, we can determine the minimum value that is statistically
significant for any table of correlations all of which have the same number of pairs (for example,
a Correlation table for our data set would use 50 pairs of values, since we have 50 members in
our sample).
The t-test formula for a correlation (r) is t = r * sqrt(n-2)/sqrt(1-r2); the associated degrees
of freedom are n-2 (number of pairs – 2) (Lind, Marchel, & Wathen, 2008). For some this might
look a bit off-putting, but remember that we can translate this into Excel cells and functions and
have Excel do the arithmetic for us.
Excel Example
If we go back to our correlation table for salary, midpoint, Age, Perf Rat, Service, and
Raise, we have:
Using Excel to create the formula and cell numbers for our key values allows us to
quickly create a result. The T.dist.2t gives us a p-value easily.
The formula to use in finding the minimum correlation value that is statistically
significant is r = sqrt(t^2/(t^2 + n-2)). We would find the appropriate t value by using the
t.inv.2T(alpha, df) with alpha = 0.05 and df = n-2 or 48. Plugging these values into the gives us
a t-value of 2.0106 or 2.011(rounded).
Putting 2.011 and 48 (n-2) into our formula gives us a r value of 0.278; therefore, in a
correlation table based on 50 pairs, any correlation greater or equal to 0.278 would be
statistically significant.
Technical Point. If you are interested in how we obtained the formula for determining
the minimum r value, the approach is shown below. If you are not interested in the math, you
can safely skip this paragraph.
t = r* sqrt(n-2)/sqrt(1-r2)
Multiplying gives us t *sqrt (1- r2) = r2* (n-2)
Squaring gives us: t2 * (1- r2) = r2* (n-2)
Multiplying out gives us: t2– t2* r2 = n r2-2* r2
Adding gives us: t2= n* r2-2*r2+ t2 *r2
Factoring gives us t2= r2 *(n -2+ t2)
Dividing gives us t2 / (n -2+ t2) = r2
Taking the square root gives us r = sqrt (t2 / (n -2+ t2)
Effect Size Measures
As we have discussed, there is a difference between statistical and practical
significance. Virtually any statistic can become statistically significant if the sample is large
enough. In practical terms, a correlation of .30 and below is generally considered too weak to be
of any practical significance. Additionally, the effect size measure for Pearson’s correlation is
simply the absolute value of the correlation; the outcome has the same general interpretation as
Cohen’s D for the t-test (0.8 is strong, and 0.2 is quite weak, for example) (Tanner & Youssef-
Morgan, 2013).
Spearman’s Rank Correlation
Another typ.
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.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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.
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.
1.4 modern child centered education - mahatma gandhi-2.pptx
Interpreting test score ~ Language Testing
1. INTERPRETING TEST SCORE
A. VALIDITY
In terms of test validity, we can show the tests to the colleagues for face validity,
compare the course objective and the test items for validity, check whether the students
respond in the way they are expected in doing the test for response validity, and calculate the
point bi-serial correlation for item validity using the following formula:
rpbi = Point bi-serial Correlation
Coefficient, i.e. item validity coefficient.
Mp = Mean score of testees correctly answering the analyzed item.
Mt = Mean score of the total score.
SD = Standard deviation of the total score.
p = Proportion of testees correctly answering the analyzed item.
q = Proportion of testees incorrectly answering the analyzed item.
4. 6. Calculating the item validity coefficient:
Test item 1
Test item 2
Test item 3
Test item 4
Test item 5
Test item 6
Test item 7
Test item 8
Test item 9
5. Test item 10
Test item 11
Test item 12
Test item 13
Test item 14
Test item 15
B. RELIABILITY
In terms of test reliability, we can use single-test single trial method with split-half
reliability, applying Pearson product moment correlation and Spearman-Brown odd even
modal correlation this calculation may be processed through SPSS program, based on the
level of significance of 5. The formula of Pearson product moment correlation is as
follows:
6. rxy = Pearson product moment correlation between variable x and y
N = Number of students taking the test
∑x = sum of variable x
∑y = sum of variable y
∑xy = sum of multiplication of variable x and variable y
∑x2
= sum of square x
∑y2
= sum of square y
- Test Item 1
No x y x2
y2
Xy
1 12 144 12
2 12 144 0
3 10 100 0
4 10 100 0
5 10 100 0
6 9 81 0
7 9 81 9
8 9 81 0
9 8 64 0
10 7 49 0
11 6 36 0
12 6 36 0
13 6 36 0
14 6 36 0
15 5 25 0
2 125 2 1113 21
The result of this calculation is then analyzed using Spearman-Brown odd ven model
correlation to see the realibility of the test.
7. rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is not reliable (r11 = 0.55)
- Test Item 2
No x y x2
y2
Xy
1 12 144 0
2 12 144 0
3 10 100 0
4 10 100 0
5 10 100 0
6 9 81 0
7 9 81 0
8 9 81 0
9 8 64 0
10 7 49 0
11 6 36 6
12 6 36 0
13 6 36 0
14 6 36 0
8. 15 5 25 0
1 125 1 1113 6
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is not reliable (r11 = -0.78)
- Test item 3
No x y x2
y2
Xy
1 12 144 12
2 12 144 12
3 10 100 10
4 10 100 0
5 10 100 0
6 9 81 9
7 9 81 9
8 9 81 9
9 8 64 8
9. 10 7 49 7
11 6 36 0
12 6 36 0
13 6 36 6
14 6 36 6
15 5 25 0
10 125 10 1113 88
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is not reliable (r11 = 0.46)
- Test item 4
No x y x2
y2
Xy
1 12 144 12
2 12 144 12
3 10 100 10
10. 4 10 100 10
5 10 100 10
6 9 81 9
7 9 81 9
8 9 81 0
9 8 64 8
10 7 49 0
11 6 36 6
12 6 36 0
13 6 36 6
14 6 36 0
15 5 25 0
10 125 10 1113 92
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is reliable (r11 = 0.71)
11. - Test item 5
No x y x2
y2
Xy
1 12 144 0
2 12 144 12
3 10 100 0
4 10 100 10
5 10 100 0
6 9 81 9
7 9 81 9
8 9 81 9
9 8 64 0
10 7 49 0
11 6 36 0
12 6 36 6
13 6 36 0
14 6 36 0
15 5 25 5
7 125 7 1113 60
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
12. To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is not reliable (r11 = 0.18)
- Test item 6
No x y x2
y2
Xy
1 12 144 12
2 12 144 12
3 10 100 10
4 10 100 0
5 10 100 10
6 9 81 9
7 9 81 0
8 9 81 0
9 8 64 8
10 7 49 0
11 6 36 6
12 6 36 0
13 6 36 6
14 6 36 6
15 5 25 5
10 125 10 1113 84
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
13. rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is reliable (r11 = 0.76)
- Test item 7
No x y x2
y2
Xy
1 12 144 12
2 12 144 12
3 10 100 10
4 10 100 10
5 10 100 10
6 9 81 9
7 9 81 9
8 9 81 0
9 8 64 0
10 7 49 0
11 6 36 0
12 6 36 0
13 6 36 0
14 6 36 0
15 5 25 5
14. 8 125 8 1113 77
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is reliable (r11 = 0.77)
- Test item 8
No x y x2
y2 xy
1 12 144 12
2 12 144 12
3 10 100 10
4 10 100 10
5 10 100 10
6 9 81 9
7 9 81 9
8 9 81 0
9 8 64 8
10 7 49 0
15. 11 6 36 6
12 6 36 6
13 6 36 0
14 6 36 0
15 5 25 0
10 125 10 1113 92
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the realibility of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is reliable (r11 = 0.71)
- Test item 9
No x y x2
y2
Xy
1 12 144 12
2 12 144 0
3 10 100 0
4 10 100 10
5 10 100 10
16. 6 9 81 0
7 9 81 9
8 9 81 9
9 8 64 8
10 7 49 7
11 6 36 0
12 6 36 6
13 6 36 6
14 6 36 6
15 5 25 5
11 125 11 1113 88
The result of this calculation is then analyzed using Spearman-Brown odd ven model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is not reliable (r11 = -0.67)
17. - Test item 10
No x y x2
y2
Xy
1 12 144 12
2 12 144 12
3 10 100 10
4 10 100 0
5 10 100 10
6 9 81 9
7 9 81 0
8 9 81 9
9 8 64 8
10 7 49 0
11 6 36 6
12 6 36 6
13 6 36 6
14 6 36 0
15 5 25 0
10 125 10 1113 88
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
18. To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is not reliable (r11 = -0.59)
- Test item 11
No x y x2
y2
xy
1 12 144 12
2 12 144 12
3 10 100 10
4 10 100 10
5 10 100 10
6 9 81 0
7 9 81 0
8 9 81 9
9 8 64 0
10 7 49 7
11 6 36 0
12 6 36 0
13 6 36 0
14 6 36 6
15 5 25 0
8 125 8 1113 76
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
19. rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is reliable (r11 = 0.71)
- Test item 12
No x y x2
y2
xy
1 12 144 12
2 12 144 12
3 10 100 10
4 10 100 10
5 10 100 10
6 9 81 9
7 9 81 9
8 9 81 9
9 8 64 8
10 7 49 7
11 6 36 6
12 6 36 6
13 6 36 0
14 6 36 6
15 5 25 5
20. 14 125 14 1113 119
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is not reliable (r11 = 0.43)
- Test item 13
No x y x2
y2
xy
1 12 144 12
2 12 144 12
3 10 100 10
4 10 100 10
5 10 100 10
6 9 81 9
7 9 81 9
8 9 81 9
9 8 64 0
10 7 49 7
21. 11 6 36 0
12 6 36 6
13 6 36 6
14 6 36 0
15 5 25 0
11 125 11 1113 100
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is reliable (r11 = 0.72)
- Test item 14
No x y x2
y2
xy
1 12 144 12
2 12 144 12
3 10 100 0
4 10 100 10
5 10 100 0
22. 6 9 81 0
7 9 81 0
8 9 81 9
9 8 64 0
10 7 49 7
11 6 36 0
12 6 36 0
13 6 36 0
14 6 36 0
15 5 25 0
5 125 5 1113 50
The result of this calculation is then analyzed using Spearman-Brown odd ven model
correlation to see the realibility of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
To interpret the test reliability (r11) Sudjono (2003.209) provides criteria. If the resulted
calculation ((r11) is the same or greater than 0.70, the evaluated test is highly reliable.
Conversely, if the resulted calculation (r11) is smaller than 0.70, the evaluated test is not
highly reliable. Therefore, the result of calculation is reliable (r11 = 0.70)
23. - Test item 15
No x y x2
y2
xy
1 12 144 0
2 12 144 12
3 10 100 10
4 10 100 10
5 10 100 10
6 9 81 0
7 9 81 0
8 9 81 9
9 8 64 8
10 7 49 7
11 6 36 0
12 6 36 0
13 6 36 0
14 6 36 6
15 5 25 0
8 125 8 1113 72
The result of this calculation is then analyzed using Spearman-Brown odd even model
correlation to see the reliability of the test.
rtt = Total test coefficient reliability (tt = total test)
rhh = Product moment Correlation Coefficient between the first half and the
second
half of the test (hh = half – half)
1 & 2 = constant numbers
25. C. ITEM DIFFICULTY
IF1 = Index of facility
UG= the number of correct answers by the upper group
LG = the number of correct answer by the lower group
N = the number students taking the test
(Difficult Question)
(Difficult Question)
(Medium Question)
(Medium Question)
(Medium Question)
(Medium Question)
(Medium Question)
(Medium Question)
(Medium Question)
(Medium Question)
(Medium Question)
(Medium Question)
26. (Medium Question)
(Difficult Question)
(Medium Question)
The conclusion of items covering a wide range of difficulty levels may promote
motivation. The inclusion of very easy items will encourage and motivate the poor student.
On the other hand, the more difficult items may be necessary in order to motivate the good
students.
D. ITEM DISCRIMINATION
ID = index discrimination
N = number of students in one group (1/2N)
UG = frequency of score by upper group (upper half)
LG = frequency of score by lower group (lower half
(Enough)
(Low)
(Low)
(Excellent)
(Enough)
(Low)