The document provides guidance on reporting the results of a paired sample t-test in APA format. It includes templates for reporting the study design, results, and statistical analysis. Key details include reporting the means, standard deviations, and standard errors for each condition. It also notes reporting the t-statistic, degrees of freedom, and significance level based on the t-test output.
This is the basic explanation on what are ANCOVA and MANCOVA in research study in which provides the definitions and the illustration on how can these both be use in SPSS tool analysis. If you's like to get practice file, do not hesitate to contact me.
This presentation explains the procedure involved in two-way repeated measures ANOVA(within-within design). An illustration has been discussed by using the functionality of SPSS.
NGR 7848 Fundamentals of Statistics for Clinicians Ho.docxhenrymartin15260
NGR 7848: Fundamentals of Statistics for Clinicians
Homework #3
Student Name: __________________________
1. Assume that you want to calculate a confidence interval for a mean (i.e. a continuous variable). Refer to the appendix tables in
your textbook, and identify the correct value to be used for the calculation (i.e. from either the t or z distribution) (5 points)
N
Desired
Confidence Interval
t or z distribution
(specify which one)
Correct
t or z Value
16 95% _____ 2.131
22 90% _____ 1.721
28 99% _____ 2.771
64 95% _____ 1.96
128 90% _____ 1.645
2. From the information below, calculate the desired confidence for a one sample continuous variable (mean systolic blood
pressure). Show your work. (5 points)
Parameter: Mean systolic blood pressure
Sample N: 420
Sample Mean: 132.8
Sample SD: 17.4
Confidence Level: 95% Z value: ________ or t value: _________
Answer: _________________________________________________________________
3. Using your notes from class, repeat this same calculation in SPSS. Make sure to paste your output (i.e. the 95% confidence
interval) below to verify your results for question #2. (5 points)
Parameter: Mean systolic blood pressure
Sample N: 420
Sample Mean: 132.8
Sample SD: 17.4
Confidence Level: 95%
4. For a one sample dichotomous outcome, calculate the desired confidence interval for the proportion of the population with
hypertension. Show your work. (5 points)
Parameter: Proportion of population with diabetes
Sample N: 194
Sample Proportion: (16 / 194) = _________
Confidence Level: 90% Z value: __________
90% Confidence Interval: ________________________________________
5. For a two sample continuous outcome, independent groups, calculate the desired confidence interval for the mean difference in
resting heart rate (beats per minute) between smokers and non-smokers. Show your work. (5 points)
Parameter: Mean difference in resting heart rate between a sample of smokers and non-smokers
Xsmokers = 76.4; n1 = 58; s1 = 9.2
Xnon-smokers = 71.8; n2 = 210; s2 = 8.4
6. Using your notes from class, use SPSS to calculate a 95% confidence interval for the mean difference in resting heart rate by
smoking status. Make sure to paste your output below. (5 points)
Parameter: Mean difference in resting heart rate between a sample of smokers and non-smokers
Xsmokers = 76.4; n1 = 58; s1 = 9.2
Xnon-smokers = 71.8; n2 = 210; s2 = 8.4
7. Compute the desired confidence interval for the risk ratio comparing the risk of memory problems among
statin users versus non-statin users. Show your work..
Memory OK Memory Problems Total Incidence
Statin user 232 32 264 p1 =
Non-statin user 949 88 1,037 p2 =
Total 1,181 120 1,301
Example: Compare the future risk of memory problems among statin users
(exposed) versus non-statin users (not e.
Assessment 3 – Hypothesis, Effect Size, Power, and t Tests.docxcargillfilberto
Assessment 3 – Hypothesis, Effect Size, Power, and
t
Tests
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
Hypothesis, Effect Size, and Power
Problem Set 3.1: Sampling Distribution of the Mean Exercise
Criterion:
Interpret population mean and variance.
Instructions:
Read the information below and answer the questions.
Suppose a researcher wants to learn more about the mean attention span of individuals in some hypothetical population. The researcher cites that the attention span (the time in minutes attending to some task) in this population is normally distributed with the following characteristics: 20
36
. Based on the parameters given in this example, answer the following questions:
1. What is the population mean (μ)? __________________________
2. What is the population variance
? __________________________
3. Sketch the distribution of this population. Make sure you draw the shape of the distribution and label the mean plus and minus three standard deviations.
Problem Set 3.2: Effect Size and Power
Criterion:
Explain effect size and power.
Instructions:
Read each of the following three scenarios and answer the questions.
Two researchers make a test concerning the effectiveness of a drug use treatment. Researcher A determines that the effect size in the population of males is
d
= 0.36; Researcher B determines that the effect size in the population of females is
d
= 0.20. All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning the levels of marital satisfaction among military families. Researcher A collects a sample of 22 married couples (
n
= 22); Researcher B collects a sample of 40 married couples (
n
= 40). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning standardized exam performance among senior high school students in one of two local communities. Researcher A tests performance from the population in the northern community, where the standard deviation of test scores is 110 (
); Researcher B tests performance from the population in the southern community, where the standard deviation of test scores is 60 (
). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Problem Set 3.3: Hypothesis, Direction, and Population Mean
Criterion:
Explain the relationship between hypothesis, tests, and population mean.
Instructions:
Read the following and answer the questions.
This is the basic explanation on what are ANCOVA and MANCOVA in research study in which provides the definitions and the illustration on how can these both be use in SPSS tool analysis. If you's like to get practice file, do not hesitate to contact me.
This presentation explains the procedure involved in two-way repeated measures ANOVA(within-within design). An illustration has been discussed by using the functionality of SPSS.
NGR 7848 Fundamentals of Statistics for Clinicians Ho.docxhenrymartin15260
NGR 7848: Fundamentals of Statistics for Clinicians
Homework #3
Student Name: __________________________
1. Assume that you want to calculate a confidence interval for a mean (i.e. a continuous variable). Refer to the appendix tables in
your textbook, and identify the correct value to be used for the calculation (i.e. from either the t or z distribution) (5 points)
N
Desired
Confidence Interval
t or z distribution
(specify which one)
Correct
t or z Value
16 95% _____ 2.131
22 90% _____ 1.721
28 99% _____ 2.771
64 95% _____ 1.96
128 90% _____ 1.645
2. From the information below, calculate the desired confidence for a one sample continuous variable (mean systolic blood
pressure). Show your work. (5 points)
Parameter: Mean systolic blood pressure
Sample N: 420
Sample Mean: 132.8
Sample SD: 17.4
Confidence Level: 95% Z value: ________ or t value: _________
Answer: _________________________________________________________________
3. Using your notes from class, repeat this same calculation in SPSS. Make sure to paste your output (i.e. the 95% confidence
interval) below to verify your results for question #2. (5 points)
Parameter: Mean systolic blood pressure
Sample N: 420
Sample Mean: 132.8
Sample SD: 17.4
Confidence Level: 95%
4. For a one sample dichotomous outcome, calculate the desired confidence interval for the proportion of the population with
hypertension. Show your work. (5 points)
Parameter: Proportion of population with diabetes
Sample N: 194
Sample Proportion: (16 / 194) = _________
Confidence Level: 90% Z value: __________
90% Confidence Interval: ________________________________________
5. For a two sample continuous outcome, independent groups, calculate the desired confidence interval for the mean difference in
resting heart rate (beats per minute) between smokers and non-smokers. Show your work. (5 points)
Parameter: Mean difference in resting heart rate between a sample of smokers and non-smokers
Xsmokers = 76.4; n1 = 58; s1 = 9.2
Xnon-smokers = 71.8; n2 = 210; s2 = 8.4
6. Using your notes from class, use SPSS to calculate a 95% confidence interval for the mean difference in resting heart rate by
smoking status. Make sure to paste your output below. (5 points)
Parameter: Mean difference in resting heart rate between a sample of smokers and non-smokers
Xsmokers = 76.4; n1 = 58; s1 = 9.2
Xnon-smokers = 71.8; n2 = 210; s2 = 8.4
7. Compute the desired confidence interval for the risk ratio comparing the risk of memory problems among
statin users versus non-statin users. Show your work..
Memory OK Memory Problems Total Incidence
Statin user 232 32 264 p1 =
Non-statin user 949 88 1,037 p2 =
Total 1,181 120 1,301
Example: Compare the future risk of memory problems among statin users
(exposed) versus non-statin users (not e.
Assessment 3 – Hypothesis, Effect Size, Power, and t Tests.docxcargillfilberto
Assessment 3 – Hypothesis, Effect Size, Power, and
t
Tests
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
Hypothesis, Effect Size, and Power
Problem Set 3.1: Sampling Distribution of the Mean Exercise
Criterion:
Interpret population mean and variance.
Instructions:
Read the information below and answer the questions.
Suppose a researcher wants to learn more about the mean attention span of individuals in some hypothetical population. The researcher cites that the attention span (the time in minutes attending to some task) in this population is normally distributed with the following characteristics: 20
36
. Based on the parameters given in this example, answer the following questions:
1. What is the population mean (μ)? __________________________
2. What is the population variance
? __________________________
3. Sketch the distribution of this population. Make sure you draw the shape of the distribution and label the mean plus and minus three standard deviations.
Problem Set 3.2: Effect Size and Power
Criterion:
Explain effect size and power.
Instructions:
Read each of the following three scenarios and answer the questions.
Two researchers make a test concerning the effectiveness of a drug use treatment. Researcher A determines that the effect size in the population of males is
d
= 0.36; Researcher B determines that the effect size in the population of females is
d
= 0.20. All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning the levels of marital satisfaction among military families. Researcher A collects a sample of 22 married couples (
n
= 22); Researcher B collects a sample of 40 married couples (
n
= 40). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning standardized exam performance among senior high school students in one of two local communities. Researcher A tests performance from the population in the northern community, where the standard deviation of test scores is 110 (
); Researcher B tests performance from the population in the southern community, where the standard deviation of test scores is 60 (
). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Problem Set 3.3: Hypothesis, Direction, and Population Mean
Criterion:
Explain the relationship between hypothesis, tests, and population mean.
Instructions:
Read the following and answer the questions.
ANOVA, Chi-Square Tests, and RegressionComplete the followin.docxamrit47
ANOVA, Chi-Square Tests, and Regression
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
ANOVA
Problem Set 4.1: Critical Value
Criterion:
Explain the relationship between
k
and power based on calculated
k
values.
Instructions:
Read the following and answer the questions.
Work through the following and write down what you see in the
F-
table. This will help familiarize you with the table.
The
F-
table: The degrees of freedom for the numerator (
k
− 1) are across the columns; the degrees of freedom for the denominator (
N
−
k
) are across the rows in the table. A separate table is included for a .05 and .01 level of significance.
Increasing the levels of the independent variable (
k
):
Suppose we have a sample size of 24 participants (
N
= 24). Record the critical values given the following values for
k
:
.05
.01
k
= 2
k
= 4
k
= 6
k
= 8
___
___
___
___
___
___
___
___
As
k
increases (from 1 to 8), does the critical value increase or decrease? Based on your answer, explain how
k
is related to power.
Problem Set 4.2: One-way ANOVA in SPSS
Criterion:
Calculate an ANOVA in SPSS.
Data:
The following is the amount of fat (in grams) consumed in a buffet-style lunch among professional bodybuilders under conditions of high, moderate, and low stress:
Stress Levels
High
Moderate
Low
10
9
9
7
4
4
8
7
6
12
6
5
6
8
7
Instructions:
Complete the following steps:
a. Open SPSS and open a
New DataSet
.
b. Click the
Variable View
tab at the bottom and enter
Stress
and enter
Fat
as the variables. Click the
Values
box for the
Stress
row and define 1 as high, 2 as medium, and 3 as low.
c. Enter the data. For example, type 1 in row 1 under
Stress
and type 10 in row 1 under
Fat
. Continue typing in all the data. Please remember to change to 2 in column 1 when the stress is moderate and change to 3 in column 1 when the stress is low
d. In the
Toolbar
, click
Analyze
, select
Compare Means
, and then select
One-Way ANOVA.
e. Select
Fat
and then click
Arrow
to send it over to the
Dependent List
box.
f. Select
Stress
and then click
Arrow
to send it over to the
Factor
box.
g. Click
OK
and copy and paste the output below.
Problem Set 4.3: One-way ANOVA in Excel
Criterion:
Calculate an ANOVA in Excel.
Instructions:
Use the data from Problem Set 4.3 to complete the following steps:
a. Open
Excel
to an empty sheet.
b. Enter the data from
Problem Set 4.3.
c. In
Row 1
, enter High in cell A1, Moderate in cell B1, and Low in cell B1.
d. In the toolbar, click
Data Analysis
, select
Anova: Single Factor,
and click
OK.
e. In
Input Range
: $A$1:$C$6, put a check next to
Lab.
1.What would be the appropriate statistical procedure to test t.docxhyacinthshackley2629
1. What would be the appropriate statistical procedure to test the following hypothesis: “Triglyceride values are a good predictor of weight in obese adults.”
__________________________________________________________________
2. What is (are) the function(s) of parametric statistical procedures?
__________________________________________________________________
3. What is Type I Error?
__________________________________________________________________
__________________________________________________________________
4. What are the assumptions underlying the use of parametric, statistical procedures?
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
__________________________________________________________________
5. If a critical value is greater than the test statistic, would you accept or reject the null hypothesis?
__________________________________________________________________
6. Under what circumstance(s) is it appropriate to use a 2-tailed test of significance?
__________________________________________________________________
__________________________________________________________________
7. What is the appropriate statistical procedure to use when your interest is in detecting a bivariate, curvilinear association?
__________________________________________________________________
8. For a study comparing outcomes under alternate treatment conditions, when the null hypothesis is rejected, the researcher concludes that a difference among groups exists.
_____True
_____False
9. A researcher, for reasons passing understanding, wishes to assess the association between gender and total cholesterol values. What would be the appropriate statistical procedure?
__________________________________________________________________
10. An HIV educator wishes to determine whether the method of delivering teaching influences adherence with antiretroviral therapy. She decides to measure adherence as viral load (a ratio measure). She teaches one group using lecture-discussion techniques. She adapts the information for access on the internet and gives another group the information using this medium. For yet another group, she decides to give a CD Rom for home study and then meets with individuals to answer any questions. She obtains viral loads for all clients for comparison. What procedure will determine the significance of any differences?
__________________________________________________________________
Items 11-15 relate to the following study results:
Study AStudy BStudy C
2 = 1.683 F = 7.357 r = .83
df = 4 df = 3/203 df = 98
p > .05 p < .05 p < .01
11. What statistical procedure was used to analyze data in study B?
__________________________________________________________________
12. How many groups were compared in stu.
Work hard to make certain that the results you have are accurate b.docxkeilenettie
Work hard to make certain that the results you have are accurate based on class material.
Use T- table and Z-table when needed.
Feel free to consult and cite the notes and previous assignments in preparing this exam.
Please show all of your working out so I am able to see your path to your answer. Mistakes will be penalized however showing your working out will allow me to deduct fewer points. If no working out is shown, I will be forced to deduct full points for mistakes.
**
.
Z table and T table are attached.
Please read carefully
!
When appropriate and possible, express your answer in the same units as the variable.
For example, if the question asks for the mean years of formal education and you have calculated the mean to be 18.44, your answer should be expressed as “
18.44 years of formal education
.”
Equations to Use
Median Position = N+1/2
The
Median Value
is the midpoint between the scores.
Mean
=
å
x
/ N
Standard Deviation =
Z score =
x – mean / standard deviation
CI =
For samples sizes ≥ 100,
the formula for the
CI
is:
CI
=
(the sample mean) + & - Z(
s / √N – 1)
CI =
For samples sizes < 100,
the formula for the
CI
is:
CI
=
(the sample mean) + & - T(
s / √N – 1)
Please answer the following questions:
You are interested in the effects of release with aftercare for a small number of drug offenders. The number of additional months without drug use for a sample
of 6 offenders
is recorded. The data on the six (6) subjects are as follows:
2
8
5
2
8
2
What are the
median position
and the
median value
?
(3 points)
What is the mean?
(
2 points)
What is the most frequently occurring score in this distribution of scores - mode?
(2 point)
2. Computation of a mode is most appropriate when a variable is measured at which level?
(2 points)
A. interval-ratio
B. ordinal
C. nominal
D. discrete
Answer: ________________________
3.
Assume that the distribution of a college entrance exam is normal with
a mean of 500 and a standard deviation of 100
.
For each score below, find the equivalent Z score, the percentage of the area above the score, and the percentage of the area below the score.
( 5 each = total 10 points)
Score Z score % Area Above % Area Below
a) 437
b) 526
4. The class intervals below represent ages of respondents. Which list is both exhaustive and mutually exclusive?
(2 points)
A. 119–120, 120–121, 121–122
B. 119–120, 121–122, 123–124
C. 119–121, 123–125, 127–129
D. 119–120, 122–123, 125–126
Answer: ______________________
5. The parole board is alarmed by the low number of years actually spent in prison for those inmates sentences to 15-year sentences. To help them make parole recommendations they gather data on the number of years served for a small sample of 7 (
seven) p
otential parolees. The number of years served for these seven parol.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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.
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
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
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.
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.
2. Reporting a Paired Sample t-test
Note – that the reporting format shown
in this learning module is for APA. For
other formats consult specific format
guides.
3. Reporting a Paired Sample t-test
Note – that the reporting format shown
in this learning module is for APA. For
other formats consult specific format
guides.
It is also recommended to consult the
latest APA manual to compare what is
described in this learning module with
the most updated formats for APA.
5. • Reporting the Study using APA
• You can report data from your own experiments by
using the template below.
6. • Reporting the Study using APA
• You can report data from your own experiments by
using the template below.
• “A paired-samples t-test was conducted to compare (your DV
measure) _________ in (IV level / condition 1) ________and
(IV level / condition 2)________ conditions.”
7. • Reporting the Study using APA
• You can report data from your own experiments by
using the template below.
• “A paired-samples t-test was conducted to compare (your DV
measure) _________ in (IV level / condition 1) ________and
(IV level / condition 2)________ conditions.”
• “A paired-samples t-test was conducted to compare number
of pizza slices eaten in one sitting by football players before
the football season and after the football season”.
9. • Reporting Results using APA
• You want to tell your reader whether or not there
was a significant difference between condition
means. You can report data from your own
experiments by using the template below.
10. • Reporting Results using APA
• You want to tell your reader whether or not there
was a significant difference between condition
means. You can report data from your own
experiments by using the template below.
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
11. • Just fill in the blanks by using the SPSS output
12. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
13. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
14. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Before_Season
5.1739 23 1.40299 .29254
After_Season
6.7391 23 1.62976 .33983
15. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Before_Season
5.1739 23 1.40299 .29254
After_Season
6.7391 23 1.62976 .33983
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
16. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Before_Season
5.1739 23 1.40299 .29254
After_Season
6.7391 23 1.62976 .33983
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
17. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Before_Season
5.1739 23 1.40299 .29254
After_Season
6.7391 23 1.62976 .33983
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
18. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Before_Season
5.1739 23 1.40299 .29254
After_Season
6.7391 23 1.62976 .33983
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
19. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Before_Season
5.1739 23 1.40299 .29254
After_Season
6.7391 23 1.62976 .33983
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=___) conditions; t(__)=___, p = ____”
20. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Before_Season
5.1739 23 1.40299 .29254
After_Season
6.7391 23 1.62976 .33983
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=___) conditions; t(__)=___, p = ____”
21. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Before_Season
5.1739 23 1.40299 .29254
After_Season
6.7391 23 1.62976 .33983
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=1.63) conditions; t(__)=___, p = ____”
22. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Before_Season
5.1739 23 1.40299 .29254
After_Season
6.7391 23 1.62976 .33983
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=1.63) conditions; t(__)=___, p = ____”
23. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Test
Paired Differences
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Mean Lower Upper
t df Sig. (2-tailed)
Pair 1 Before_Season
- After_Season
-1.56522 1.61881 .33755 -2.26524 -.86519 -4.637 22 .000
24. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Test
Paired Differences
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Mean Lower Upper
t df Sig. (2-tailed)
Pair 1 Before_Season
- After_Season
-1.56522 1.61881 .33755 -2.26524 -.86519 -4.637 22 .000
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=1.63) conditions; t(22)=___, p = ____”
25. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Test
Paired Differences
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Mean Lower Upper
t df Sig. (2-tailed)
Pair 1 Before_Season
- After_Season
-1.56522 1.61881 .33755 -2.26524 -.86519 -4.637 22 .000
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=1.63) conditions; t(22)=___, p = ____”
26. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Test
Paired Differences
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Mean Lower Upper
t df Sig. (2-tailed)
Pair 1 Before_Season
- After_Season
-1.56522 1.61881 .33755 -2.26524 -.86519 -4.637 22 .000
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=1.63) conditions; t(22)=___, p = ____”
Degrees of
Freedom
27. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Test
Paired Differences
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Mean Lower Upper
t df Sig. (2-tailed)
Pair 1 Before_Season
- After_Season
-1.56522 1.61881 .33755 -2.26524 -.86519 -4.637 22 .000
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=1.63) conditions; t(22)= -4.64, p = ____”
28. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Test
Paired Differences
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Mean Lower Upper
t df Sig. (2-tailed)
Pair 1 Before_Season
- After_Season
-1.56522 1.61881 .33755 -2.26524 -.86519 -4.637 22 .000
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=1.63) conditions; t(22)= -4.64, p = ____”
29. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Test
Paired Differences
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Mean Lower Upper
t df Sig. (2-tailed)
Pair 1 Before_Season
- After_Season
-1.56522 1.61881 .33755 -2.26524 -.86519 -4.637 22 .000
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=1.63) conditions; t(22)= -4.64, p = .000”
30. • Just fill in the blanks by using the SPSS output
• Let’s start by filling in the Mean and Standard
Deviation for each condition
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=___, SD=___) and IV level 2 (M=___,
SD=___) conditions; t(__)=___, p = ____”
Paired Samples Test
Paired Differences
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Mean Lower Upper
t df Sig. (2-tailed)
Pair 1 Before_Season
- After_Season
-1.56522 1.61881 .33755 -2.26524 -.86519 -4.637 22 .000
• “There was a significant (not a significant) difference in the
scores for IV level 1 (M=5.17, SD=1.40) and IV level 2 (M=6.73
SD=1.63) conditions; t(22)= -4.64, p = .000”
32. “There was a significant increase in the number of
pizza slices eaten by football players after the season
(M=5.17, SD=1.40) than before the season (M=6.73
SD=1.63); t(22)= 4.64, p = .000”