Noise and Cognitive Performance

This assignment investigates the influence of different types of noise (no noise, white noise, crowd noise) on cognitive performance. Participants complete a series of 15 test trials, with their average reaction times and percentage of correct responses being recorded. Correlations are then calculated to determine if a relationship exists between the noise conditions and these measures of cognitive performance.

Running head: DATA ANALYSIS ON PSYCHOLOGY
Data Analysis on Psychology
Name of the Student:
Name of the University:
Author’s note:

1DATA ANALYSIS ON PSYCHOLOGY
Abstract
The report incorporates a completion of a Lab Report supplemented by lecture material and additional material covered in the first semester of
class in week 2. Lab Report is in Microsoft office (Excel) format. The Lab Report refers the discipline of psychology to the student for
systematically preparing a report in accordance with the applied scientific method. We ensure that all central variables are adequately defined.
There are one main hypothesis common to all students to investigate, which is related to the differences between the three different ‘Noise
conditions’ (No noise/White Noise/Crowd Noise) on the ‘Response speeds’. We also tested one more hypothesis based on the additional data
that was collected different “Noise conditions” and “Percentage of correct responses”. Descriptive statistics, Correlation coefficient and
necessary graphs & plots were calculated and presented in the Lab Report of psychology class. The analysis is incorporated with the help of
SPSS 20.

2DATA ANALYSIS ON PSYCHOLOGY
Contents
Introduction:-................................................................................................................................................................................................................3
Methods:-......................................................................................................................................................................................................................3
Aim...........................................................................................................................................................................................................................3
Data Description.......................................................................................................................................................................................................3
Hypotheses...............................................................................................................................................................................................................3
Statistical Tools and Packages.................................................................................................................................................................................4
Results:-........................................................................................................................................................................................................................4
Introduction to the Variables....................................................................................................................................................................................4
1. Education Status...............................................................................................................................................................................................4
2. Age of the Participants:-...................................................................................................................................................................................5
3. Noise Conditions..............................................................................................................................................................................................7
4. Percentage Correct Responses..........................................................................................................................................................................8
5. Average Reaction Time....................................................................................................................................................................................9
Hypothesis Testing: -..............................................................................................................................................................................................10
Hypothesis1:.......................................................................................................................................................................................................10
Hypothesis2:.......................................................................................................................................................................................................16
Conclusion..................................................................................................................................................................................................................22
References..................................................................................................................................................................................................................23
Appendix: -.................................................................................................................................................................................................................24

3DATA ANALYSIS ON PSYCHOLOGY
Introduction:-
In this study, participants were asked to select particular letters from an array of the letters. This case study welcomed participants
into the Psychology Lab Room and the Lab Technician set the participant up with the experiment. Participants were given two practice trials and
feedback was given on how to correctly respond on the task if the participant needed. Each participant was given 15 test trials and the length of
time between the presentation of the task in each trial and the response on the keyboard was recorded. Participants also informed their age and
education level. Participants were thanked for their involvement in the study and were instructed to return to their class. Later in the class,
participants were debriefed and invited to ask questions about the experiment.
The report concerns about the concerns regarding auditory distraction and the method how it affects performance in a cognitive attention
task. The independent variable of the study was assigned to the auditory distraction group. Participants of study were assigned in three auditory
groups that are- 1) “White noise” group was given headphones and static white noise was played. 2) “Crowd noise” group was delivered
headphones that played crowds talking similar to a busy cafe. 3) “No noise” group was assigned as controlled group where they were given
headphones but nothing was played.
Methods:-
Aim
The aim and objective of the study was to determine whether certain types of noise distraction would affect on the cognitive tasks like
Percentage correct responses and average response times or not.
Data Description
The report describes about the collected data of classroom students. Total 77 students responded according to their background. The five
variables are Education status, Age, Noise condition, Percentage correct responses (out of 15 test trials) and Average reaction times (out of 15
test trials). Age, Percentage correct responses (out of 15 test trials) and Average reaction times (out of 15 test trials) are numeric in nature.
Education status and Noise condition are categorical (nominal) in nature (Reynolds, 1984). We label “Full Time” as 1 and “Part Time” as 2.
Next, we label “No Noise” as 1, “White Noise” as 2 and “Crowd Noise” as 3. We calculated the descriptive statistics of all the factors. We
calculated cross function and linear regression relationship of Noise condition and Average reaction time out of 15 test trials. Then we calculated
crosstab function and simple linear regression relationship of Noise condition and Percentage correct responses out of 15 test trials. Lastly, we
calculated correlation of ‘bivariate’ data between selected variables.
Hypotheses
We are eager to check mainly two hypotheses regarding the issue:
1) A.
Null hypothesis (H0) = There is a cross tab relationship with ‘Noise condition’ and ‘Average reaction times’.
Alternative hypothesis (HA) = There is no cross tab relationship with ‘Noise condition’ and ‘Average reaction times’.
B.
Null hypothesis (H0) = There is insignificant linear relationship with ‘Noise condition’ and ‘Average reaction times’.
Alternative hypothesis (HA) = There is significant linear relationship with ‘Noise condition’ and ‘Average reaction times’.
2) A.

4DATA ANALYSIS ON PSYCHOLOGY
Null hypothesis (H0) = There is a cross tab relationship with ‘Noise condition’ and ‘Percentage correct responses’.
Alternative hypothesis (HA) = There is no cross tab relationship with ‘Noise condition’ and ‘Percentage correct responses’.
B.
Null hypothesis (H0) = There is insignificant linear relationship with ‘Noise condition’ and ‘Percentage correct responses’.
Alternative hypothesis (HA) = There is significant linear relationship with ‘Noise condition’ and ‘Percentage correct responses’.
Statistical Tools and Packages
We applied “SPSS 20” package to analyse the psychological data. The instructions regarding Cross-tab, linear regression, select cases,
descriptive statistics and graphs were used to the analysis of data (Gaur and Gaur, 2006).
Results:-
Introduction to the Variables
1. Education Status
1.1. Frequency Distribution of Education Status:
Statistics
Education status: Full time/Part
time
N Valid 77
Missing 0
Education status: Full time/Part time
Frequency Percent Valid Percent Cumulative
Percent
Valid
Full Time 42 54.5 54.5 54.5
Part Time 35 45.5 45.5 100.0
Total 77 100.0 100.0
The data of educational status received from students of the class indicates that among 77 students of the class majority is having full
time course with frequency 42 and minority is having part time course with frequency 35. Frequency is actually the total number of occurrences
of an event. The percentages of full time and part time students are 54.5% and 45.5% respectively (Leech, Barrett and Morgan, 2005).

5DATA ANALYSIS ON PSYCHOLOGY
The Bar plot indicates the Full time and Part time Students’ frequency distribution in the class.
1.2. Descriptive Statistics of Education Status:
Descriptive Statistics
N Range Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Education status: Full
time/Part time 77 1 1 2 1.45 .501 .251 .186 .274 -2.018 .541
Valid N (listwise) 77
The descriptive statistics table of Education status indicates that mean, standard deviation and variance of the factorized categorical
variable are 1.45, 0.501 and 0.251.
We know that skewness is less than (-1) or greater than 1, is highly skewed. If the skewness is between (-1) and (-0.5) or between (0.5) to
1, indicates a moderately skewed. If the skewness is between (-0.5) and (0.5), the distribution is approximately symmetric (Oja, 1983). The value
of skewness is 0.186. Therefore, we can conclude that the distribution is symmetric in nature.
The value of Kurtosis greater than 3 indicates that the distribution is Leptokurtic (Peaked) (Judd, McClelland and Culhane, 1995). The
value of Kurtosis equal to 3 interprets that the distribution is perfectly normal. Lastly, the value of Kurtosis less than 3 indicates that the
distribution is Platykurtic (Flat). Here, value of Kurtosis is (-2.018). Hence, the data is platykurtic.
2. Age of the Participants:-
2.1. Frequency Distribution of Age:
Statistics
Age of participant
N Valid 77
Missing 0
Age of participant
Frequency Percent Valid Percent Cumulative
Percent
Valid 19 8 10.4 10.4 10.4
20 13 16.9 16.9 27.3
21 8 10.4 10.4 37.7
22 3 3.9 3.9 41.6
23 3 3.9 3.9 45.5

6DATA ANALYSIS ON PSYCHOLOGY
24 2 2.6 2.6 48.1
25 3 3.9 3.9 51.9
26 3 3.9 3.9 55.8
27 3 3.9 3.9 59.7
29 5 6.5 6.5 66.2
30 2 2.6 2.6 68.8
32 3 3.9 3.9 72.7
35 2 2.6 2.6 75.3
36 3 3.9 3.9 79.2
37 3 3.9 3.9 83.1
38 1 1.3 1.3 84.4
39 2 2.6 2.6 87.0
40 2 2.6 2.6 89.6
41 1 1.3 1.3 90.9
43 1 1.3 1.3 92.2
45 2 2.6 2.6 94.8
46 1 1.3 1.3 96.1
50 1 1.3 1.3 97.4
51 2 2.6 2.6 100.0
Total 77 100.0 100.0
The data of Age of the Students received from students of the class indicates that among 77 students of the class maximum number (13)
of respondents is 20 years old. Frequency is actually the total number of occurrences of an event. The frequency is followed by 19 and 21 years
old respondents each having occurrences of eight times. The students of 19, 20 and 21 years old are totally 29 in frequency and 37.7% in
frequency.
The bar plot indicates the frequency distribution of age of participants.

7DATA ANALYSIS ON PSYCHOLOGY
The Histogram plot shows the frequency distribution of age of participants.
2.2. Descriptive Statistics of Age:
Descriptive Statistics
N Range Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Age of participant 77 32 19 51 27.99 9.026 81.460 .950 .274 -.123 .541
Valid N (listwise) 77
The descriptive statistics table of Age of the participants indicates that mean, standard deviation and variance of the numerical variable
are 27.99, 9.026 and 81.460.
The value of skewness is 0.950. Therefore, we can conclude that the distribution is moderately skewed in nature. The distribution is
positively skewed. Here, value of Kurtosis is (-0.123). Hence, the data is platykurtic.
3. Noise Conditions
3.1. Frequency Distribution of Noise Conditions:
Statistics
Noise condition (No noise/White
Noise/Crowd Noise)
N Valid 77
Missing 0
Noise condition (No noise/White Noise/Crowd Noise)
Frequency Percent Valid Percent Cumulative
Percent
Valid
No Noise 27 35.1 35.1 35.1
White Noise 17 22.1 22.1 57.1
Crowd Noise 33 42.9 42.9 100.0
Total 77 100.0 100.0

8DATA ANALYSIS ON PSYCHOLOGY
The frequency table of 77 noise conditions indicates that Crowd Noise (33) is maximum in occurrence whereas White Noise (17) is
minimum in occurrences. The percentages of these noise conditions are 42.9% and 35.1% respectively.
The pie chart shows the frequency distribution of three types of noise conditions.
3.2. Descriptive Statistics of Noise Condition:
Descriptive Statistics
N Range Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Noise condition (No
noise/White Noise/Crowd
Noise)
77 2 1 3 2.08 .885 .783 -.155 .274 -1.723 .541
Valid N (listwise) 77
The descriptive statistics table of Noise Condition indicates that mean, standard deviation and variance of the factorized categorical
variable are 2.08, 0.885 and 0.783.
The value of skewness is (-0.155). Therefore, we can conclude that the distribution is approximately symmetric in nature (Trochim,
2006). The distribution is positively skewed. Here, value of Kurtosis is (-1.723). Hence, the data is platykurtic.
4. Percentage Correct Responses
4.1. Frequency Distribution of Percentage Correct responses (out of 15 test trials):
Statistics
Percentage correct responses
(out of 15 test trials)
N Valid 77
Missing 0
Percentage correct responses (out of 15 test trials)
Frequency Percent Valid Percent Cumulative
Percent
Valid 13 1 1.3 1.3 1.3

9DATA ANALYSIS ON PSYCHOLOGY
20 1 1.3 1.3 2.6
73 2 2.6 2.6 5.2
80 2 2.6 2.6 7.8
87 3 3.9 3.9 11.7
87 2 2.6 2.6 14.3
93 2 2.6 2.6 16.9
93 9 11.7 11.7 28.6
100 55 71.4 71.4 100.0
Total 77 100.0 100.0
The Percentage of correct response table interprets that maximum percentage of response is 100% (frequency=55, percent=71.4%). The
minimum percentage of frequency is 13% and 20%. Their frequency and percent are equal that is frequency=1 and percent=1.3%. It is important
to note that, the frequency and percent are significantly large for cent percent correct responses.
The bar plot indicates the frequency distribution of percentage of correct response out of 15 test trials.
4.2. Descriptive Statistics of Percentage Correct responses (out of 15 test trials):
Descriptive Statistics
N Range Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Percentage correct
responses (out of 15 test
trials)
77 87 13 100 94.80 14.251 203.086 -4.578 .274 23.101 .541
Valid N (listwise) 77
The descriptive statistics table of Percentage Correct responses indicates that mean, standard deviation and variance of the numerical
variable are 94.8, 14.251 and 203.086.
The value of skewness is (-4.578). Therefore, we can conclude that the distribution is highly skewed in nature. The distribution is
negatively skewed. Here, value of Kurtosis is (23.101). Hence, the data is leptokurtic.
5. Average Reaction Time
5.1. Frequency Distribution of Average reaction times (out of 15 test trials):

10DATA ANALYSIS ON PSYCHOLOGY
We only apply here Histogram plot of average reaction time to give response.
5.2. Descriptive Statistics of Average reaction times (out of 15 test trials):
Descriptive Statistics
N Range Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Average reaction times (out
of 15 test trials) 77 2978.07 1880.73 4858.80 2994.0996 723.94538 524096.913 .932 .274 .417 .541
Valid N (listwise) 77
The descriptive statistics table of Average reaction times indicates that mean, standard deviation and variance of the numerical variable
are 2994.0996, 723.94538 and 524096913.
The value of skewness is (0.923). Therefore, we can conclude that the distribution is moderately skewed in nature. The distribution is
positively skewed. Here, value of Kurtosis is (0.417). Hence, the data is platykurtic.
Hypothesis Testing: -
Hypothesis1:
Questions:
a) Is any cross function relation present between Noise condition and Average reaction time?
b) Is any significant linear relationship present between Noise condition and Average reaction time?
Crosstab: -
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent

11DATA ANALYSIS ON PSYCHOLOGY
Noise condition (No
noise/White Noise/Crowd
Noise) * Average reaction
times (out of 15 test trials)
77 100.0% 0 0.0% 77 100.0%
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 154.000a 152 .439
Likelihood Ratio 163.872 152 .241
Linear-by-Linear Association .275 1 .600
N of Valid Cases 77
a. 231 cells (100.0%) have expected count less than 5. The minimum
expected count is .22.
Symmetric Measures
Value Asymp. Std.
Errora
Approx. Tb Approx. Sig.
Nominal by Nominal
Phi 1.414 .439
Cramer's V 1.000 .439
Contingency Coefficient .816 .439
Interval by Interval Pearson's R .060 .107 .522 .603c
Ordinal by Ordinal Spearman Correlation .053 .113 .458 .648c
N of Valid Cases 77
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
The cross summary incorporated with crosstabs function between Noise conditions and Average reaction times interprets that they are
very weakly related to each other with the value of correlation coefficient = 0.60. However, they are not linearly related with the value of

12DATA ANALYSIS ON PSYCHOLOGY
significant F-value = 0.439. The value greater than 0.05 (<0.439) infers that we can accept the null hypothesis of insignificant cross-function
association between the variables Noise conditions (nominal) and Average reaction times (numeric).
The crosstab relation indicates that Crowd noise has significant relation with Average reaction times.
The Group wise box plot of Mean of Average reaction time according to the Noise condition:-
The bar plot indicates that mean of average reaction time out of 15 test trials in case of all the three noise conditions are almost equal.
However, the Crowd Noise has highest average reaction time and No Noise has lowest average reaction time (Rosenthal and Rosnow, 1991).
The medians of the average reaction time out of 15 test trials are indicated by grouped box plot of ‘Noise condition’. The median is
highest for “Crowd Noise” and lowest for “White Noise”.

13DATA ANALYSIS ON PSYCHOLOGY
Pearson Correlation Coefficient: -
The Pearson Correlation coefficient between Noise condition and Average reaction times out of test trials is (0.06). It indicates that two
factors are weakly and positively correlated.
Simple Linear Regression: -
The regression analysis was employed in order to empirically identify whether the Noise conditions was a statistically important to
average reaction times or not. The equation is, Y1=β0 +β1* X1 + μ, where Y1 refers to Noise conditions, β0 refers to the constant or the intercept,
X1 refers to the Average reaction times, β1 refers to the change of coefficient for the Average reaction times, while μ refers to the error term. The
regression result shows the goodness of fit for the regression between the Predictors and response.
Linear regression model is a commonly used generalized form of regression model where the response factor linearly relates with the
parameters of explanatory variables. In linear regression model, the response variable should be continuous and dependent with explanatory
variables (Faraway 2016). The high value (near to 1) gives the signal of strong linear relationship, the lowest value (near to -1) shows strong
negative linear relationship and the value near to zero gives the signal to weakest linear relationship with response and predictors. Multiple
regression equation also can calculate the regression value if all the parameters of simple linear regression taken together in case of dichotomous
(continuous or discrete) response parameter (Darlington and Hayes 2016).
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1
Average
reaction times
(out of 15 test
trials)b
. Enter
a. Dependent Variable: Noise condition (No noise/White
Noise/Crowd Noise)
b. All requested variables entered.
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .060a .004 -.010 .889
a. Predictors: (Constant), Average reaction times (out of 15 test trials)
b. Dependent Variable: Noise condition (No noise/White Noise/Crowd
Noise)
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .215 1 .215 .272 .603b
Residual 59.317 75 .791
Total 59.532 76
a. Dependent Variable: Noise condition (No noise/White Noise/Crowd Noise)
b. Predictors: (Constant), Average reaction times (out of 15 test trials)
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 1.858 .434 4.281 .000
Average reaction times (out
of 15 test trials) 7.355E-005 .000 .060 .522 .603
a. Dependent Variable: Noise condition (No noise/White Noise/Crowd Noise)

14DATA ANALYSIS ON PSYCHOLOGY
As the value of multiple R2 is 0.06, we can tell that there is no significant linear association between Noise conditions and Average
reaction time. It also interprets 6% of the variations in the Noise conditions could be explained by the variations of Average reaction times
(Rozeboom, 1960). The Value of adjusted R2 (-0.010) indicates a very bad (0 to 0.3 or 0 to -0.3) fitting as per the rules of goodness of fit.
The significant p-value of product line (0.603) has p-value more than 0.05. Therefore, we can accept the null hypothesis of absence of
linear relationship of these two factors at 95% confidence limit. Therefore, these two factors are not linearly related (Edwards, 1954).
The fitted normal probability plot shows that the residual plot does not fit well.
Comparison of “Average Reaction time” across “Noise conditions”: -
1. Noise condition (No noise/White Noise/Crowd Noise) = No Noise
Descriptive Statistics
N Range Minimum Maximum Sum Mean Std.
Deviation
Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Statistic Statistic Std.
Error
Statistic Std.
Error
Average reaction times
(out of 15 test trials) 27 2757.53 1992.07 4749.60 79290.67 2936.6916 127.89550 664.56453 441646.016 .977 .448 1.111 .872
Valid N (listwise) 27
a. Noise condition (No noise/White Noise/Crowd Noise) = No Noise

15DATA ANALYSIS ON PSYCHOLOGY
2. Noise condition (No noise/White Noise/Crowd Noise) = White Noise
Descriptive Statisticsa
N Range Minimum Maximum Sum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Average reaction times
(out of 15 test trials) 17 2878.27 1953.60 4831.87 51070.07 3004.1220 860.05847 739700.570 1.237 .550 .727 1.063
Valid N (listwise) 17
a. Noise condition (No noise/White Noise/Crowd Noise) = White Noise
3. Noise condition (No noise/White Noise/Crowd Noise) = Crowd Noise
Descriptive Statisticsa
N Range Minimum Maximum Sum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error

16DATA ANALYSIS ON PSYCHOLOGY
Average reaction times
(out of 15 test trials) 33 2978.07 1880.73 4858.80 100184.92 3035.9067 715.12653 511405.954 .704 .409 .054 .798
Valid N (listwise) 33
a. Noise condition (No noise/White Noise/Crowd Noise) = Crowd Noise
Hypothesis2:
Questions:
a) Is any association of cross tab function lies between Noise condition and Percentage of correct responses?
b) Is any linear relationship present between Noise condition and Percentage of correct responses?
Crosstab: -
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Noise condition (No
noise/White Noise/Crowd
Noise) * Percentage correct
responses (out of 15 test
trials)
77 100.0% 0 0.0% 77 100.0%
Noise condition (No noise/White Noise/Crowd Noise) * Percentage correct responses (out of 15 test trials) Crosstabulation
Count
Percentage correct responses (out of 15 test trials) Total
13 20 73 80 87 87 93 93 100
Noise condition (No
noise/White Noise/Crowd
Noise)
No Noise 0 1 0 0 1 2 0 2 21 27
White Noise 0 0 2 1 0 0 0 3 11 17
Crowd Noise 1 0 0 1 2 0 2 4 23 33
Total 1 1 2 2 3 2 2 9 55 77
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 20.326a 16 .206
Likelihood Ratio 22.235 16 .136

17DATA ANALYSIS ON PSYCHOLOGY
Linear-by-Linear Association .007 1 .935
N of Valid Cases 77
a. 24 cells (88.9%) have expected count less than 5. The minimum
expected count is .22.
Symmetric Measures
Value Asymp. Std.
Errora
Approx. Tb Approx. Sig.
Nominal by Nominal
Phi .514 .206
Cramer's V .363 .206
Contingency Coefficient .457 .206
Interval by Interval Pearson's R -.009 .123 -.081 .936c
Ordinal by Ordinal Spearman Correlation -.065 .110 -.568 .572c
N of Valid Cases 77
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
The cross summary incorporated with crosstabs function between Noise conditions and Percentage correct responses interprets that they
are very weakly related to each other with the value of correlation coefficient = (-0.009). However, they are not linearly related with the value of
significant F-value = 0.206. The value less than 0.05 (> -0.009) infers that we should reject the null hypothesis of insignificant cross-function
association between the variables Noise conditions (nominal) and Percentage correct response (numeric).
White noise has least frequency of 100% accuracy. Crowded noise (13%) and No noise (20%) has least percent of correct responses.

18DATA ANALYSIS ON PSYCHOLOGY
The Group wise box plot of Mean of Percentage correct responses according to the Noise condition:-
The mean of Average Percentage correct responses out of 15 test trials are almost equal for all the three types of ‘Noise condition’.
However, comparatively ‘No Noise’ has highest and ‘White Noise’ has lowest mean of Average percentage of correct responses (Norugis,
1988).
The grouped box plot of percentage correct responses out of 15 test trials with respect to different types of noise condition indicate that
though ‘No Noise’ has maximum outliers but it has less spread and median is 100%. ‘White Noise’ has highest spread and the median is least
among three categories. Besides, this group has few outliers. ‘Crowd Noise’ has spread and median more than ‘No Noise’ but less than ‘White
Noise’. This group also have some considerable outliers.

19DATA ANALYSIS ON PSYCHOLOGY
Pearson Correlation Coefficient: -
The Pearson Correlation coefficient between Percentage correct responses out of 15 test trials and Noise condition is (-0.075). It indicates
that two factors are weakly and negatively correlated.
Simple Linear Regression:-
The regression analysis was employed in order to empirically identify whether Noise condition is a statistically important to Percentage
correct responses or not. The equation is, Y1=β0 +β1* X1 + μ, where Y1 refers to Noise condition, β0 refers to the constant or the intercept, X1
refers to the Percentage correct responses, β1 refers to the change of coefficient for the Percentage correct responses, while μ refers to the error
term. The regression result shows the goodness of fit for the regression between the Predictors and response.
The linear regression method is applied between Noise condition and Percentage of correct responses.
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1
Percentage
correct
responses (out
of 15 test trials)b
. Enter
a. Dependent Variable: Noise condition (No noise/White
Noise/Crowd Noise)
b. All requested variables entered.
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .009a .000 -.013 .891
a. Predictors: (Constant), Percentage correct responses (out of 15 test
trials)
b. Dependent Variable: Noise condition (No noise/White Noise/Crowd
Noise)
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .005 1 .005 .006 .936b
Residual 59.527 75 .794
Total 59.532 76
a. Dependent Variable: Noise condition (No noise/White Noise/Crowd Noise)
b. Predictors: (Constant), Percentage correct responses (out of 15 test trials)
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 2.133 .687 3.103 .003
Percentage correct
responses (out of 15 test
trials)
-.001 .007 -.009 -.081 .936
a. Dependent Variable: Noise condition (No noise/White Noise/Crowd Noise)
As the value of multiple R2 is 0.0, we can tell that there is no significant linear association between Percentage correct responses and
Noise condition (Hayes and Matthes, 2009). It also interprets 0.0% of the variations in the Noise condition could be explained by the variations
of Percentage correct responses. The Value of adjusted R2 (-0.013) indicates a very bad (0 to 0.3 or 0 to -0.3) fitting as per the rules of goodness
of fit (Ho, 2006).

20DATA ANALYSIS ON PSYCHOLOGY
The significant p-value of product line (0.936) has p-value more than 0.05 (Bakan, 1966). Therefore, we can accept the null hypothesis
of absence of linear relationship of these two factors at 95% confidence limit. Therefore, these two factors are not linearly related at all.
The fitted normal probability plot shows that the residual plot does not fit well.
Comparison of “Average Reaction time” across “Noise conditions”: -
1. Noise condition (No noise/White Noise/Crowd Noise) = No Noise
Descriptive Statistics
N Range Minimum Maximum Sum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Percentage correct
responses (out of 15 test
trials)
27 80 20 100 2567 95.06 15.645 244.762 -4.580 .448 22.321 .872
Valid N (listwise) 27
a. Noise condition (No noise/White Noise/Crowd Noise) = No Noise

21DATA ANALYSIS ON PSYCHOLOGY
2. Noise condition (No noise/White Noise/Crowd Noise) = White Noise
Descriptive Statistics
N Range Minimum Maximum Sum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Percentage correct
responses (out of 15 test
trials)
17 27 73 100 1607 94.51 9.498 90.215 -1.673 .550 1.453 1.063
Valid N (listwise) 17
a. Noise condition (No noise/White Noise/Crowd Noise) = White Noise

22DATA ANALYSIS ON PSYCHOLOGY
3. Noise condition (No noise/White Noise/Crowd Noise) = Crowd Noise
Descriptive Statistics
N Range Minimum Maximum Sum Mean Std. Deviation Variance Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
Percentage correct
responses (out of 15 test
trials)
33 87 13 100 3127 94.74 15.435 238.246 -4.890 .409 25.928 .798
Valid N (listwise) 33
a. Noise condition (No noise/White Noise/Crowd Noise) = Crowd Noise
Conclusion
The data analysis on psychological data interprets that among the three categories of Noise condition, “No noise” gives better
performance in case of Average reaction times and Percentage correct responses. “White noise” provides worst performance with the reference
of these two fields (Hamburg, 1970). Overall, the ‘Average reaction times’ and ‘Noise condition’ are not linearly associated. We could conclude
the similar result in case of linear regression between ‘Noise condition’ and ‘Percentage correct responses’. Relationship related to cross function
summary is significant in case of “Noise condition’ and ‘Average reaction times’. However, association related to cross function summary is
insignificant in case of ‘Noise condition’ and ‘Percentage correct responses’. More to say that, correlation coefficients between ‘Noise condition’
and ‘Average reaction times’ is very weak. The result reflects in case of “Noise condition’ and ‘Percentage correct responses’ too.

23DATA ANALYSIS ON PSYCHOLOGY
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24DATA ANALYSIS ON PSYCHOLOGY
Appendix: -
Correlation Coefficient ( r ) Comment
1 Perfect Positive Correlation
0.7 to 1 Strong Positive Correlation
0.3 to 0.7 Moderate Positive Correlation
0 to 0.3 Weak Positive Correlation
0 Absolutely No Correlation (Uncorrelated)
0 to (-0.3) Weak Negative Correlation
(-0.3) to (-0.7) Moderate Negative Correlation
(-0.7) to (-1) Strong Negative Correlation
(-1) Perfect Negative Correlation
(Zou, Tuncali and Silverman, 2003)
Correlations
Noise condition (No
noise/White Noise/Crowd
Noise)
Average reaction times (out
of 15 test trials)
Noise condition (No noise/White Noise/Crowd
Noise)
Pearson Correlation 1 .060
Sig. (2-tailed) .603
N 77 77
Average reaction times (out of 15 test trials)
Pearson Correlation .060 1
Sig. (2-tailed) .603
N 77 77
Correlations
Percentage correct responses
(out of 15 test trials)
Noise condition (No noise/White
Noise/Crowd Noise)
Percentage correct responses (out of 15 test trials)
Pearson Correlation 1 -.009
Sig. (2-tailed) .936
N 77 77
Noise condition (No noise/White Noise/Crowd Noise)
Pearson Correlation -.009 1
Sig. (2-tailed) .936
N 77 77