My thesis integrates perspectives from text comprehension and multimedia learning theories. Results provide evidence for a linear contiguity effect and a text cohesion effect as new multimedia design principles. Publications are forthcoming.
SAS Results for Problem 2Factor (IV) in ANOVA 2 levels (1 or.docxtodd331
SAS Results for Problem 2:
Factor (IV) in ANOVA:
2 levels (1 or 2)
The ANOVA Procedure
Class Level Information
Class Levels Values
PERLEVEL 2 1 2
Number of Observations Read 80
Number of Observations Used 80
1st Dependent Variable (DV) analyzed
The ANOVA Procedure
ANOVA F-test for testing differences in mean DV1 for two IV levels
Dependent Variable: EFFORT
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 1 22.8703938 22.8703938 15.74 0.0002
Error 78 113.3171062 1.4527834
Corrected Total 79 136.1875000
R-Square Coeff Var Root MSE EFFORT Mean
0.167933 28.78363 1.205315 4.187500
Source DF Anova SS Mean Square F Value Pr > F
PERLEVEL 1 22.87039385 22.87039385 15.74 0.0002
2nd DV analyzed
The ANOVA Procedure
ANOVA F-test for testing differences in mean DV2 for two IV levels
Dependent Variable: UND
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 1 9.3002113 9.3002113 3.92 0.0513
Error 78 185.1872887 2.3741960
Corrected Total 79 194.4875000
R-Square Coeff Var Root MSE UND Mean
0.047819 36.36207 1.540843 4.237500
Source DF Anova SS Mean Square F Value Pr > F
PERLEVEL 1 9.30021129 9.30021129 3.92 0.0513
Note: Good strategy is conduct univariate ANOVA F-tests at a small α (e.g., α = .01 or .02)
The ANOVA Procedure
ANOVA F-test for testing differences in mean DV3 for two IV levels
3rd DV analyzed
Dependent Variable: QUALITY
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 1 7.6597701 7.6597701 5.45 0.0221
Error 78 109.5402299 1.4043619
Corrected Total 79 117.2000000
R-Square Coeff Var Root MSE QUALITY Mean
0.065356 28.90385 1.185058 4.100000
Source .
Educational Psychology 565 Practice Quiz(use α = .05 unl.docxtoltonkendal
Educational Psychology 565 Practice Quiz
(use α = .05 unless otherwise stated).
1. A small school district wants to know what type of teaching/learning is most effective at helping students learn to read. Three methods are proposed (top-down, bottom-up, and interactive). It is believed that the gender of the teacher may also be important in student learning, so the study also aims to determine if gender of the teacher is important. There are 12 schools in the district, and each school has 1 second grade class (each class has 10 students). Two female teachers and two male teachers’ classrooms are randomly assigned to each of the three methods (all 12 teachers have just been hired in the district). At the end of the year, the students all took a 100 item standardized multiple-choice reading test called the “EZreading” test (note: the analysis was performed at the student level).
Coding:
teachgender = gender of teacher: 1= men, 2 = women
Teachmeth = teaching method (1=top-down, 2=bottom-up, 3=interactive)
EZread = scores on the Ezread reading test
Use SPSS output “SPSS printout for question 1”to help answer the parts below.
a. What is/are the independent variable(s) in this experiment (Be specific)? What level of measurement is/are the IV(s)? Explain why?
b. What is/are the dependent variable(s) in this experiment (Be specific)? What level of measurement is/are the DV(s)? Explain why?
c. State the null hypotheses and alternative hypotheses for the factors and the interaction in symbols and words.
d. Do you think the assumption of homogeneity of variance has been met? Support your answer.
e. Do you think the assumption of independence has been met? Support your answer.
f. Calculate Cohen’s d for the difference between the top-down and interactive methods. Explain what Cohen’s d means for this comparison.
g. Is the interaction of the two factors statistically significant? Explain your answer.
h. Report the results of the study along with an interpretation for the results. You do not need to write up the results like a results section; you can just report the findings with statements about each factor and the interaction of the two factors. Be sure to cite evidence from your analysis.
i. Based on the results of the study what would you recommend about teaching method and gender of teachers?
2. Answer the following questions.
Source
SS
df
MS
F
Between
100
20
Within
2
50
Total
200
7
a. Complete the ANOVA source table (fill in all blank spaces)
b. How many people are in this study. (hint: use degrees of freedom)
c. What is the critical F at α = .01? Would you reject the null hypothesis? Explain your answer.
d. What are the critical F at α = .05? Would you reject the null hypothesis? Explain your answer.
e. Why do the conclusions from items c and d differ? Explain your answer in terms of Type I and II errors.
3. A researcher wants to kn.
My thesis integrates perspectives from text comprehension and multimedia learning theories. Results provide evidence for a linear contiguity effect and a text cohesion effect as new multimedia design principles. Publications are forthcoming.
SAS Results for Problem 2Factor (IV) in ANOVA 2 levels (1 or.docxtodd331
SAS Results for Problem 2:
Factor (IV) in ANOVA:
2 levels (1 or 2)
The ANOVA Procedure
Class Level Information
Class Levels Values
PERLEVEL 2 1 2
Number of Observations Read 80
Number of Observations Used 80
1st Dependent Variable (DV) analyzed
The ANOVA Procedure
ANOVA F-test for testing differences in mean DV1 for two IV levels
Dependent Variable: EFFORT
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 1 22.8703938 22.8703938 15.74 0.0002
Error 78 113.3171062 1.4527834
Corrected Total 79 136.1875000
R-Square Coeff Var Root MSE EFFORT Mean
0.167933 28.78363 1.205315 4.187500
Source DF Anova SS Mean Square F Value Pr > F
PERLEVEL 1 22.87039385 22.87039385 15.74 0.0002
2nd DV analyzed
The ANOVA Procedure
ANOVA F-test for testing differences in mean DV2 for two IV levels
Dependent Variable: UND
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 1 9.3002113 9.3002113 3.92 0.0513
Error 78 185.1872887 2.3741960
Corrected Total 79 194.4875000
R-Square Coeff Var Root MSE UND Mean
0.047819 36.36207 1.540843 4.237500
Source DF Anova SS Mean Square F Value Pr > F
PERLEVEL 1 9.30021129 9.30021129 3.92 0.0513
Note: Good strategy is conduct univariate ANOVA F-tests at a small α (e.g., α = .01 or .02)
The ANOVA Procedure
ANOVA F-test for testing differences in mean DV3 for two IV levels
3rd DV analyzed
Dependent Variable: QUALITY
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 1 7.6597701 7.6597701 5.45 0.0221
Error 78 109.5402299 1.4043619
Corrected Total 79 117.2000000
R-Square Coeff Var Root MSE QUALITY Mean
0.065356 28.90385 1.185058 4.100000
Source .
Educational Psychology 565 Practice Quiz(use α = .05 unl.docxtoltonkendal
Educational Psychology 565 Practice Quiz
(use α = .05 unless otherwise stated).
1. A small school district wants to know what type of teaching/learning is most effective at helping students learn to read. Three methods are proposed (top-down, bottom-up, and interactive). It is believed that the gender of the teacher may also be important in student learning, so the study also aims to determine if gender of the teacher is important. There are 12 schools in the district, and each school has 1 second grade class (each class has 10 students). Two female teachers and two male teachers’ classrooms are randomly assigned to each of the three methods (all 12 teachers have just been hired in the district). At the end of the year, the students all took a 100 item standardized multiple-choice reading test called the “EZreading” test (note: the analysis was performed at the student level).
Coding:
teachgender = gender of teacher: 1= men, 2 = women
Teachmeth = teaching method (1=top-down, 2=bottom-up, 3=interactive)
EZread = scores on the Ezread reading test
Use SPSS output “SPSS printout for question 1”to help answer the parts below.
a. What is/are the independent variable(s) in this experiment (Be specific)? What level of measurement is/are the IV(s)? Explain why?
b. What is/are the dependent variable(s) in this experiment (Be specific)? What level of measurement is/are the DV(s)? Explain why?
c. State the null hypotheses and alternative hypotheses for the factors and the interaction in symbols and words.
d. Do you think the assumption of homogeneity of variance has been met? Support your answer.
e. Do you think the assumption of independence has been met? Support your answer.
f. Calculate Cohen’s d for the difference between the top-down and interactive methods. Explain what Cohen’s d means for this comparison.
g. Is the interaction of the two factors statistically significant? Explain your answer.
h. Report the results of the study along with an interpretation for the results. You do not need to write up the results like a results section; you can just report the findings with statements about each factor and the interaction of the two factors. Be sure to cite evidence from your analysis.
i. Based on the results of the study what would you recommend about teaching method and gender of teachers?
2. Answer the following questions.
Source
SS
df
MS
F
Between
100
20
Within
2
50
Total
200
7
a. Complete the ANOVA source table (fill in all blank spaces)
b. How many people are in this study. (hint: use degrees of freedom)
c. What is the critical F at α = .01? Would you reject the null hypothesis? Explain your answer.
d. What are the critical F at α = .05? Would you reject the null hypothesis? Explain your answer.
e. Why do the conclusions from items c and d differ? Explain your answer in terms of Type I and II errors.
3. A researcher wants to kn.
A sample of adults with hearing aids were randomly assigned to one o.pdfkostikjaylonshaewe47
A sample of adults with hearing aids were randomly assigned to one of three conditions that
focused on lists of practice words that are typically challenging for individuals with hearing loss
to accurately perceive. Following 12 weeks of structured practice with a speech-language
pathologist, they were assessed using a testing of speech perception to yield a “hearing score”.
State the null and research hypotheses, assess the assumptions of ANOVA, and interpret the
results (F-ratio, post hoc results when applicable), and describe the outcome of the study with
respect to null hypothesis.
Between-Subjects Factors
N
ListID
List1
24
List2
24
List3
24
List4
24
Descriptive Statistics
Dependent Variable:HearingScore
ListID
Mean
Std. Deviation
N
List1
32.75
7.409
24
List2
29.67
8.058
24
List3
25.25
8.316
24
List4
25.58
7.779
24
Total
28.31
8.372
96
Tests of Between-Subjects Effects
Dependent Variable:HearingScore
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model
920.458a
3
306.819
4.919
.003
Intercept
76953.375
1
76953.375
1233.793
.000
ListID
920.458
3
306.819
4.919
.003
Error
5738.167
92
62.371
Total
83612.000
96
Corrected Total
6658.625
95
a. R Squared = .138 (Adjusted R Squared = .110)
b. Computed using alpha = .05
1. Grand Mean
Dependent Variable:HearingScore
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
28.313
.806
26.712
29.913
2. ListID
Dependent Variable:HearingScore
ListID
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
List1
32.750
1.612
29.548
35.952
List2
29.667
1.612
26.465
32.868
List3
25.250
1.612
22.048
28.452
List4
25.583
1.612
22.382
28.785
Multiple Comparisons
HearingScore
Bonferroni
(I) ListID
(J) ListID
Mean Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
List1
List2
3.08
2.280
1.000
-3.06
9.23
List3
7.50*
2.280
.009
1.35
13.65
List4
7.17*
2.280
.013
1.02
13.31
List2
List1
-3.08
2.280
1.000
-9.23
3.06
List3
4.42
2.280
.335
-1.73
10.56
List4
4.08
2.280
.459
-2.06
10.23
List3
List1
-7.50*
2.280
.009
-13.65
-1.35
List2
-4.42
2.280
.335
-10.56
1.73
List4
-.33
2.280
1.000
-6.48
5.81
List4
List1
-7.17*
2.280
.013
-13.31
-1.02
List2
-4.08
2.280
.459
-10.23
2.06
List3
.33
2.280
1.000
-5.81
6.48
Based on observed means.
The error term is Mean Square(Error) = 62.371.
*. The mean difference is significant at the .05 level.
Between-Subjects Factors
N
ListID
List1
24
List2
24
List3
24
List4
24
Solution
Null hypothesis: The means of all testing conditions focused are equal
Alternative hypothesis: At least one of the means are not equal
Assuptions of Anova:
1.Each sample is drawn from normal population.
.2.The populaitons have common variance.
3.The samples are independent to each other.
4.The factor effects are additive in nature.
The F-value is 4.919, and P-value is 0.003
Since the P-value is less than 0.05 we reject the null hypothesis . hence conclude that at least two
of the means are significant.
Post hoc results:
at 0.05,
the list 1 and list 3 are signi.
19 9742 the application paper id 0016(edit ty)IAESIJEECS
Severely occluded face images are the main problem in low performance of face recognition algorithms. In this paper, we apply a new algorithm, a modified version of the least trimmed squares (LTS) with a genetic algorithms introduce by [1]. We focused on the application of modified LTS with genetic algorithm method for face image recognition. This algorithm uses genetic algorithms to construct a basic subset rather than selecting the basic subset randomly. The modification in this method lessens the number of trials to obtain the minimum of the LTS objective function. This method was then applied to two benchmark datasets with clean and occluded query images. The performance of this method was measured by recognition rates. The AT&T dataset and Yale Dataset with different image pixel sizes were used to assess the method in performing face recognition. The query images were contaminated with salt and pepper noise. The modified LTS with GAs method is applied in face recognition framework by using the contaminated images as query image in the context of linear regression. By the end of this study, we can determine this either this method can perform well in dealing with occluded images or vice versa.
Quantitative Analysis for Emperical ResearchAmit Kamble
Overview for Approach Methods for quantitative analysis; which includes
1) Planning of Experiments
2) Data Generation
3) presentation of report
some numerical approach methods; data modeling; hypothesis methods
A sample of adults with hearing aids were randomly assigned to one o.pdfkostikjaylonshaewe47
A sample of adults with hearing aids were randomly assigned to one of three conditions that
focused on lists of practice words that are typically challenging for individuals with hearing loss
to accurately perceive. Following 12 weeks of structured practice with a speech-language
pathologist, they were assessed using a testing of speech perception to yield a “hearing score”.
State the null and research hypotheses, assess the assumptions of ANOVA, and interpret the
results (F-ratio, post hoc results when applicable), and describe the outcome of the study with
respect to null hypothesis.
Between-Subjects Factors
N
ListID
List1
24
List2
24
List3
24
List4
24
Descriptive Statistics
Dependent Variable:HearingScore
ListID
Mean
Std. Deviation
N
List1
32.75
7.409
24
List2
29.67
8.058
24
List3
25.25
8.316
24
List4
25.58
7.779
24
Total
28.31
8.372
96
Tests of Between-Subjects Effects
Dependent Variable:HearingScore
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model
920.458a
3
306.819
4.919
.003
Intercept
76953.375
1
76953.375
1233.793
.000
ListID
920.458
3
306.819
4.919
.003
Error
5738.167
92
62.371
Total
83612.000
96
Corrected Total
6658.625
95
a. R Squared = .138 (Adjusted R Squared = .110)
b. Computed using alpha = .05
1. Grand Mean
Dependent Variable:HearingScore
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
28.313
.806
26.712
29.913
2. ListID
Dependent Variable:HearingScore
ListID
Mean
Std. Error
95% Confidence Interval
Lower Bound
Upper Bound
List1
32.750
1.612
29.548
35.952
List2
29.667
1.612
26.465
32.868
List3
25.250
1.612
22.048
28.452
List4
25.583
1.612
22.382
28.785
Multiple Comparisons
HearingScore
Bonferroni
(I) ListID
(J) ListID
Mean Difference (I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
List1
List2
3.08
2.280
1.000
-3.06
9.23
List3
7.50*
2.280
.009
1.35
13.65
List4
7.17*
2.280
.013
1.02
13.31
List2
List1
-3.08
2.280
1.000
-9.23
3.06
List3
4.42
2.280
.335
-1.73
10.56
List4
4.08
2.280
.459
-2.06
10.23
List3
List1
-7.50*
2.280
.009
-13.65
-1.35
List2
-4.42
2.280
.335
-10.56
1.73
List4
-.33
2.280
1.000
-6.48
5.81
List4
List1
-7.17*
2.280
.013
-13.31
-1.02
List2
-4.08
2.280
.459
-10.23
2.06
List3
.33
2.280
1.000
-5.81
6.48
Based on observed means.
The error term is Mean Square(Error) = 62.371.
*. The mean difference is significant at the .05 level.
Between-Subjects Factors
N
ListID
List1
24
List2
24
List3
24
List4
24
Solution
Null hypothesis: The means of all testing conditions focused are equal
Alternative hypothesis: At least one of the means are not equal
Assuptions of Anova:
1.Each sample is drawn from normal population.
.2.The populaitons have common variance.
3.The samples are independent to each other.
4.The factor effects are additive in nature.
The F-value is 4.919, and P-value is 0.003
Since the P-value is less than 0.05 we reject the null hypothesis . hence conclude that at least two
of the means are significant.
Post hoc results:
at 0.05,
the list 1 and list 3 are signi.
19 9742 the application paper id 0016(edit ty)IAESIJEECS
Severely occluded face images are the main problem in low performance of face recognition algorithms. In this paper, we apply a new algorithm, a modified version of the least trimmed squares (LTS) with a genetic algorithms introduce by [1]. We focused on the application of modified LTS with genetic algorithm method for face image recognition. This algorithm uses genetic algorithms to construct a basic subset rather than selecting the basic subset randomly. The modification in this method lessens the number of trials to obtain the minimum of the LTS objective function. This method was then applied to two benchmark datasets with clean and occluded query images. The performance of this method was measured by recognition rates. The AT&T dataset and Yale Dataset with different image pixel sizes were used to assess the method in performing face recognition. The query images were contaminated with salt and pepper noise. The modified LTS with GAs method is applied in face recognition framework by using the contaminated images as query image in the context of linear regression. By the end of this study, we can determine this either this method can perform well in dealing with occluded images or vice versa.
Quantitative Analysis for Emperical ResearchAmit Kamble
Overview for Approach Methods for quantitative analysis; which includes
1) Planning of Experiments
2) Data Generation
3) presentation of report
some numerical approach methods; data modeling; hypothesis methods
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.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
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.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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!
1. SORT CASES BY Practice.
SPLIT FILE SEPARATE BY Practice.
EXAMINE VARIABLES=Comprehension BY Noise
/PLOT NPPLOT
/STATISTICS DESCRIPTIVES
/CINTERVAL 95
/MISSING LISTWISE
/NOTOTAL.
Explore
Notes
Output Created 19-DEC-2021 16:22:13
Comments
Input Active Dataset DataSet0
Filter <none>
Weight <none>
Split File Practice
N of Rows in Working
Data File
30
Missing Value
Handling
Definition of Missing User-defined missing
values for dependent
variables are treated as
missing.
Cases Used Statistics are based on
cases with no missing
values for any
dependent variable or
factor used.
Syntax EXAMINE
VARIABLES=Comprehe
nsion BY Noise
/PLOT NPPLOT
/STATISTICS
DESCRIPTIVES
/CINTERVAL 95
/MISSING LISTWISE
/NOTOTAL.
Resources Processor Time 00:00:04.69
Elapsed Time 00:00:05.78
[DataSet0]
2. Practice = No practice
Level of background
Case Processing Summarya
Level of
background
Cases
Valid Missing Total
N Percent N Percent N Percent
Reading
comprehension
No noice 5 100.0% 0 0.0% 5 100.0%
Low noice 5 100.0% 0 0.0% 5 100.0%
High noice 5 100.0% 0 0.0% 5 100.0%
a. Practice = No practice
Descriptivesa
Level of background Statistic Std. Error
Reading
comprehension
No noice Mean 13.80 .663
95% Confidence
Interval for Mean
Lower Bound 11.96
Upper Bound 15.64
5% Trimmed Mean 13.78
Median 14.00
Variance 2.200
Std. Deviation 1.483
Minimum 12
Maximum 16
Range 4
Interquartile Range 3
Skewness .552 .913
Kurtosis .868 2.000
Low noice Mean 12.60 .600
95% Confidence
Interval for Mean
Lower Bound 10.93
Upper Bound 14.27
5% Trimmed Mean 12.61
Median 12.00
Variance 1.800
Std. Deviation 1.342
Minimum 11
Maximum 14
Range 3
Interquartile Range 3
Skewness .166 .913
Kurtosis -2.407 2.000
High noice Mean 11.40 .872
3. 95% Confidence
Interval for Mean
Lower Bound 8.98
Upper Bound 13.82
5% Trimmed Mean 11.39
Median 12.00
Variance 3.800
Std. Deviation 1.949
Minimum 9
Maximum 14
Range 5
Interquartile Range 4
Skewness .081 .913
Kurtosis -.817 2.000
a. Practice = No practice
Tests of Normalitya
Level of
background
Kolmogorov-Smirnovb Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Reading
comprehension
No noice .246 5 .200* .956 5 .777
Low noice .273 5 .200* .852 5 .201
High noice .221 5 .200* .953 5 .758
*. This is a lower bound of the true significance.
a. Practice = No practice
b. Lilliefors Significance Correction
7. Level of background
Case Processing Summarya
Level of
background
Cases
Valid Missing Total
N Percent N Percent N Percent
Reading
comprehension
No noice 5 100.0% 0 0.0% 5 100.0%
Low noice 5 100.0% 0 0.0% 5 100.0%
High noice 5 100.0% 0 0.0% 5 100.0%
a. Practice = Practice
Descriptivesa
Level of background Statistic Std. Error
Reading
comprehension
No noice Mean 18.00 .548
95% Confidence
Interval for Mean
Lower Bound 16.48
Upper Bound 19.52
5% Trimmed Mean 17.94
Median 18.00
Variance 1.500
Std. Deviation 1.225
Minimum 17
Maximum 20
Range 3
Interquartile Range 2
Skewness 1.361 .913
Kurtosis 2.000 2.000
Low noice Mean 16.80 .583
95% Confidence
Interval for Mean
Lower Bound 15.18
Upper Bound 18.42
5% Trimmed Mean 16.83
Median 17.00
Variance 1.700
Std. Deviation 1.304
Minimum 15
Maximum 18
Range 3
Interquartile Range 3
Skewness -.541 .913
Kurtosis -1.488 2.000
High noice Mean 12.20 .917
95% Confidence
Interval for Mean
Lower Bound 9.66
Upper Bound 14.74
5% Trimmed Mean 12.22
Median 13.00
Variance 4.200
8. Std. Deviation 2.049
Minimum 10
Maximum 14
Range 4
Interquartile Range 4
Skewness -.441 .913
Kurtosis -3.163 2.000
a. Practice = Practice
Tests of Normalitya
Level of
background
Kolmogorov-Smirnovb Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Reading
comprehension
No noice .300 5 .161 .833 5 .146
Low noice .221 5 .200* .902 5 .421
High noice .258 5 .200* .782 5 .057
*. This is a lower bound of the true significance.
a. Practice = Practice
b. Lilliefors Significance Correction
Reading comprehension
Normal Q-Q Plots
12. Univariate Analysis of Variance
Notes
Output Created 19-DEC-2021 16:51:43
Comments
Input Active Dataset DataSet0
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File
30
Missing Value
Handling
Definition of Missing User-defined missing
values are treated as
missing.
Cases Used Statistics are based on
all cases with valid data
for all variables in the
model.
13. Syntax UNIANOVA
Comprehension BY
Practice Noise
/METHOD=SSTYPE(3)
/INTERCEPT=INCLUDE
/PLOT=PROFILE(Practi
ce*Noise) TYPE=LINE
ERRORBAR=NO
MEANREFERENCE=N
O YAXIS=AUTO
/EMMEANS=TABLES(Pr
actice) COMPARE
ADJ(LSD)
/EMMEANS=TABLES(N
oise) COMPARE
ADJ(LSD)
/EMMEANS=TABLES(Pr
actice*Noise)
COMPARE(Practice)/EM
MEANS=TABLES(Practi
ce*Noise)
COMPARE(Noise)
/EMMEANS=TABLES(Pr
actice*Noise)
/PRINT ETASQ
DESCRIPTIVE
HOMOGENEITY
/CRITERIA=ALPHA(.05)
/DESIGN=Practice
Noise Practice*Noise.
Resources Processor Time 00:00:00.61
Elapsed Time 00:00:00.70
Between-Subjects Factors
Value Label N
Practice 0 No practice 15
1 Practice 15
Level of
background
0 No noice 10
1 Low noice 10
2 High noice 10
14. Descriptive Statistics
Dependent Variable: Reading comprehension
Practice
Level of
background Mean
Std.
Deviation N
No practice No noice 13.80 1.483 5
Low noice 12.60 1.342 5
High noice 11.40 1.949 5
Total 12.60 1.805 15
Practice No noice 18.00 1.225 5
Low noice 16.80 1.304 5
High noice 12.20 2.049 5
Total 15.67 2.968 15
Total No noice 15.90 2.558 10
Low noice 14.70 2.541 10
High noice 11.80 1.932 10
Total 14.13 2.874 30
Levene's Test of Equality of Error Variancesa,b
Levene
Statistic df1 df2 Sig.
Reading
comprehension
Based on Mean 1.127 5 24 .373
Based on Median .400 5 24 .844
Based on Median and
with adjusted df
.400 5 20.061 .843
Based on trimmed
mean
1.107 5 24 .383
Tests the null hypothesis that the error variance of the dependent variable is equal across
groups.
a. Dependent variable: Reading comprehension
b. Design: Intercept + Practice + Noise + Practice * Noise
Tests of Between-Subjects Effects
Dependent Variable: Reading comprehension
Source
Type III Sum
of Squares df
Mean
Square F Sig.
Partial Eta
Squared
Corrected
Model
178.667a 5 35.733 14.105 .000 .746
Intercept 5992.533 1 5992.533 2365.474 .000 .990
Practice 70.533 1 70.533 27.842 .000 .537
Noise 88.867 2 44.433 17.539 .000 .594
Practice * Noise 19.267 2 9.633 3.803 .037 .241
Error 60.800 24 2.533
Total 6232.000 30
Corrected Total 239.467 29
a. R Squared = .746 (Adjusted R Squared = .693)
15. Estimated Marginal Means
1. Practice
Estimates
Dependent Variable: Reading comprehension
Practice Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
No practice 12.600 .411 11.752 13.448
Practice 15.667 .411 14.818 16.515
Pairwise Comparisons
Dependent Variable: Reading comprehension
(I) Practice (J) Practice
Mean
Difference (I-
J) Std. Error Sig.b
95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
No practice Practice -3.067* .581 .000 -4.266 -1.867
Practice No practice 3.067* .581 .000 1.867 4.266
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no
adjustments).
Univariate Tests
Dependent Variable: Reading comprehension
Sum of
Squares df
Mean
Square F Sig.
Partial Eta
Squared
Contrast 70.533 1 70.533 27.842 .000 .537
Error 60.800 24 2.533
The F tests the effect of Practice. This test is based on the linearly independent
pairwise comparisons among the estimated marginal means.
16. 2. Level of background
Estimates
Dependent Variable: Reading comprehension
Level of
background Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
No noice 15.900 .503 14.861 16.939
Low noice 14.700 .503 13.661 15.739
High noice 11.800 .503 10.761 12.839
Pairwise Comparisons
Dependent Variable: Reading comprehension
(I) Level of
background
(J) Level of
background
Mean
Difference (I-
J) Std. Error Sig.b
95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
No noice Low noice 1.200 .712 .105 -.269 2.669
High noice 4.100* .712 .000 2.631 5.569
Low noice No noice -1.200 .712 .105 -2.669 .269
High noice 2.900* .712 .000 1.431 4.369
High noice No noice -4.100* .712 .000 -5.569 -2.631
Low noice -2.900* .712 .000 -4.369 -1.431
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
Univariate Tests
Dependent Variable: Reading comprehension
Sum of
Squares df
Mean
Square F Sig.
Partial Eta
Squared
Contrast 88.867 2 44.433 17.539 .000 .594
Error 60.800 24 2.533
The F tests the effect of Level of background. This test is based on the linearly
independent pairwise comparisons among the estimated marginal means.
17. 3. Practice * Level of background
Estimates
Dependent Variable: Reading comprehension
Practice
Level of
background Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
No practice No noice 13.800 .712 12.331 15.269
Low noice 12.600 .712 11.131 14.069
High noice 11.400 .712 9.931 12.869
Practice No noice 18.000 .712 16.531 19.469
Low noice 16.800 .712 15.331 18.269
High noice 12.200 .712 10.731 13.669
Pairwise Comparisons
Dependent Variable: Reading comprehension
Level of
background (I) Practice (J) Practice
Mean
Difference (I-
J) Std. Error Sig.b
95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
No noice No practice Practice -4.200* 1.007 .000 -6.278 -2.122
Practice No practice 4.200* 1.007 .000 2.122 6.278
Low noice No practice Practice -4.200* 1.007 .000 -6.278 -2.122
Practice No practice 4.200* 1.007 .000 2.122 6.278
High noice No practice Practice -.800 1.007 .435 -2.878 1.278
Practice No practice .800 1.007 .435 -1.278 2.878
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
Univariate Tests
Dependent Variable: Reading comprehension
Level of background
Sum of
Squares df
Mean
Square F Sig.
Partial Eta
Squared
No noice Contrast 44.100 1 44.100 17.408 .000 .420
Error 60.800 24 2.533
Low noice Contrast 44.100 1 44.100 17.408 .000 .420
Error 60.800 24 2.533
High noice Contrast 1.600 1 1.600 .632 .435 .026
Error 60.800 24 2.533
Each F tests the simple effects of Practice within each level combination of the other effects shown.
These tests are based on the linearly independent pairwise comparisons among the estimated
marginal means.
18. 4. Practice * Level of background
Estimates
Dependent Variable: Reading comprehension
Practice
Level of
background Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
No practice No noice 13.800 .712 12.331 15.269
Low noice 12.600 .712 11.131 14.069
High noice 11.400 .712 9.931 12.869
Practice No noice 18.000 .712 16.531 19.469
Low noice 16.800 .712 15.331 18.269
High noice 12.200 .712 10.731 13.669
Pairwise Comparisons
Dependent Variable: Reading comprehension
Practice
(I) Level of
background
(J) Level of
background
Mean
Difference (I-
J) Std. Error Sig.b
95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
No practice No noice Low noice 1.200 1.007 .245 -.878 3.278
High noice 2.400* 1.007 .025 .322 4.478
Low noice No noice -1.200 1.007 .245 -3.278 .878
High noice 1.200 1.007 .245 -.878 3.278
High noice No noice -2.400* 1.007 .025 -4.478 -.322
Low noice -1.200 1.007 .245 -3.278 .878
Practice No noice Low noice 1.200 1.007 .245 -.878 3.278
High noice 5.800* 1.007 .000 3.722 7.878
Low noice No noice -1.200 1.007 .245 -3.278 .878
High noice 4.600* 1.007 .000 2.522 6.678
High noice No noice -5.800* 1.007 .000 -7.878 -3.722
Low noice -4.600* 1.007 .000 -6.678 -2.522
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
Univariate Tests
Dependent Variable: Reading comprehension
Practice
Sum of
Squares df
Mean
Square F Sig.
Partial Eta
Squared
No practice Contrast 14.400 2 7.200 2.842 .078 .191
Error 60.800 24 2.533
Practice Contrast 93.733 2 46.867 18.500 .000 .607
Error 60.800 24 2.533
19. Each F tests the simple effects of Level of background within each level combination of the other
effects shown. These tests are based on the linearly independent pairwise comparisons among the
estimated marginal means.
5. Practice * Level of background
Dependent Variable: Reading comprehension
Practice
Level of
background Mean Std. Error
95% Confidence Interval
Lower Bound Upper Bound
No practice No noice 13.800 .712 12.331 15.269
Low noice 12.600 .712 11.131 14.069
High noice 11.400 .712 9.931 12.869
Practice No noice 18.000 .712 16.531 19.469
Low noice 16.800 .712 15.331 18.269
High noice 12.200 .712 10.731 13.669
Profile Plots