In-depth SPSS Data Analysis: Knowledge Economy, Growth, and Research

This report presents a comprehensive data analysis using SPSS to investigate the relationship between the knowledge economy, growth, and research and development (R&D). The analysis includes descriptive statistics for demographic variables such as gender, nationality, age, marital status, education level, organization type, employment status, field of work, job level, and working experience. Reliability tests using Cronbach's alpha are performed to assess the internal consistency of the data. Factor analysis is conducted to determine the construct validity of the variables, with a focus on research performance and transfer to research universities, mediated by investment in R&D. The report includes tables, histograms, and correlation analyses to provide a thorough understanding of the relationships between the variables. The KMO value indicates adequate sampling, and the total variance explained matrix highlights significant factors. The scree plot aids in identifying the point of inflection for factor extraction, and the rotated component matrix shows the component loadings for each variable.

Data analysis using SPSS 1
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Data analysis using SPSS 2
DATA ANALYSIS
1. DESCRIPTIVE STATISTICS (DEMOGRAPHICS)
Count Column N %
GENDER FEMALE 106 70.2%
MALE 45 29.8%
Total 151 100.0%
Table 1
Table 1 above shows the demographics of the respondents on the basis of gender. As can be
observed, there were more female respondents than male. They were 106 out of 151 therefore
constituting to 70.2% of the total. Their male counterparts were 45 out of 151 accounting for
29.8%. However, the research could not attribute anything to this disparity.
Count Column N %
NATIONALIT
Y
AUSTRALIA 4 2.6%
CANADA 3 2.0%
CHINA 2 1.3%
EGYPT 5 3.3%
FRANCE 2 1.3%
INDIA 6 4.0%
IRAQ 4 2.6%
IRELAND 4 2.6%
JAPAN 1 0.7%
LEBANON 4 2.6%
MOROCCO 4 2.6%
OMAN 1 0.7%
PALESTINIA
N
6 4.0%
SWEDEN 5 3.3%
SYRIAN 5 3.3%
TUNISIA 4 2.6%
UAE 80 53.0%

Data analysis using SPSS 3
UK 5 3.3%
USA 6 4.0%
Total 151 100.0%
Table 2
Table 2 shows the distribution of respondents by country of origin. As can be observed, majority
of the respondents were United Arab Emirates nationals. They were 80 out of 151 accounting for
53% of the total. The study could only attribute the high percentage to the proximity since it was
conducted in U.A.E. The second nationals were from United States of America who were 6 and
so accounted for only 4% of the total. The least number of respondents came from Oman and
Japan. They each constituted to 0.7% of the total.
Count Column N
%
AGE 20-29 32 21.2%
30-39 52 34.4%
40-49 32 21.2%
MORE THAN
50
35 23.2%
Total 151 100.0%
Table 3
Table 4 shows the distribution of respondents by age. It can be concluded that the number was
generally distributed across the age groups. However, majority were from between the ages of 30
to 39 years. They were 52 out of 151 and constituted 34.4% of the total. This is followed by
those who are more than 50 years old who made 23.2% of the total. The ages between 20 and 29
years and 40 to 49 years were each 21.2%.

Data analysis using SPSS 4
Count Column N %
MARITAL_STATUS MARRIED 79 52.3%
SINGLE 67 44.4%
DIVORCED 5 3.3%
Total 151 100.0%
Table 4
The distribution of respondents by marital status showed that 79 out of 151 respondents are
married. They made up 52.3% of the total. They were followed by the singles that were 67 out of
151 and made up 44.4% of the total. The rest were the divorced respondents who were only 5 in
number and only constituted to 3.3% of the total.
Count Column N %
EDUCATIONAL_LEVE
L
BACHELORS 56 37.1%
DOCTORATE 61 40.4%
HIGH DIPLOMA 6 4.0%
HIGH SCHOOL 6 4.0%
MASTERS
DEGREE
22 14.6%
Total 151 100.0%
Table 5
The table above shows the distribution of respondents by their educational levels. The highest
number as can be observed was the doctorates who were 61 out of 151 constituting to 40.4%.
They were followed by holders of bachelor’s degree who were 56 and making 37.1% of the total.
Respondents having master’s degree were 22 constituting to 14.6%. The least number were
respondents who were holders of high diploma and high school education. They were 6 each in
number constituting to 4% each.

Data analysis using SPSS 5
Count Column N %
ORGANIZATION_TYPE PUBLIC/GOVERNMENT 132 87.4%
SEMI-GOVERNMENT 14 9.3%
PRIVATE 2 1.3%
OTHER 3 2.0%
Total 151 100.0%
Table 6
From the table above, it can be observed that majority of the respondent were from government
or the public sector. They were 132 out of 151 in numbers thereby constituting to 87.4% of the
total. They were followed by those who work in the semi-government sector who were 14 out of
151 making 9.3% of the total. Those from the private sector were only 2 and the others were 3.
Count Column N %
CURRENTLY_YOU_ARE EMPLOYED 131 86.8%
STUDENT 15 9.9%
UNEMPLOYED 5 3.3%
Total 151 100.0%
Table 7
As can be observed from table 8 above, 131 respondents are currently employed. 15 of them are
students while 5 of them are unemployed.

Data analysis using SPSS 6
Count Column N
%
FIELD_OF_WORK BUSINESS 45 29.8%
EDUCATION 26 17.2%
ENGINEERING 14 9.3%
INFORMATION
TECHNOLOGY
14 9.3%
MEDICINE & HEALTH
SCIENCES
13 8.6%
AGRICULTURE 4 2.6%
COMMUNICATION/
PUBLIC RELATIONS
8 5.3%
LEGAL 2 1.3%
PILOT/AIRFORCE 1 0.7%
OTHER 24 15.9%
Total 151 100.0%
Table 8
From the table above, it can be observed that majority of the respondent work in the field of
business. They were 45 out of 151 in numbers thereby constituting to 29.8% of the total. They
were followed by those who work in other sectors other than the ones that were listed who were
24 out of 151 making 15.9% of the total. The least number was from pilot/air force. There was
only one person in this category.
Count Column N %

Data analysis using SPSS 7
JOB_LEVEL JUNIOR EMPLOYEE 13 8.6%
LOWER
MANAGEMENT
3 2.0%
MIDDLE
MANAGEMENT
30 19.9%
SENIOR EMPLOYEE 30 19.9%
TOP MANAGEMENT 59 39.1%
UNEMPLOYED 10 6.6%
OTHER 6 4.0%
Total 151 100.0%
Table 9
From table 11 above, it can be observed that 59 respondents worked as the top management
level. They were the majority constituting to 39.1% of the total. This is followed by those who
work middle management and senior employee levels who were 30 each. The least number of
respondents (3) worked in the lower management level.
Count Column N %
WORKING_EXPERIENCE 1 YEAR OR LESS 5 3.3%
2 TO 6 YEARS 25 16.6%
7 TO 11 YEARS 36 23.8%
12 TO 16 YEARS 46 30.5%
MORE THAN 17
YEARS
27 17.9%
UNEMPLOYED 12 7.9%
Total 151 100.0%
Table 10
It can be observed from the table above that most of the respondents (46 out of 151) had worked
for between 12 to 16 years. They were followed by those who had worked for more than 17
years. Those who had worked for between 2 to 6 years were 25 in number.
Summary statistics for work experience
Table of summary statistics

Data analysis using SPSS 8
Statistics
WORKING_EXPERIENCE
N Valid 151
Missing 0
Mean 3.6689
Median 4.0000
Mode 4.00
Std. Deviation 1.26344
Table 11
As can be seen from the table above, the mean work experience is 3.6 while the modal work
experience is 4. According to data coding, this indicates that most of the workers had worked for
between 3 to 12 years of their lives.

Data analysis using SPSS 9
Histogram showing distribution of work experience
Figure 1
As can be observed from the shape of the histogram above, the number of years worked by the
respondents was normally distributed. This is attested by the fact that the normal curve was quite
symmetric.
Correlation
Table of correlation between gender and field of work

Data analysis using SPSS 10
Correlations
GENDER FIELD_OF_WO
RK
GENDER
Pearson Correlation 1 .003
Sig. (2-tailed) .969
N 151 151
FIELD_OF_WORK
Pearson Correlation .003 1
Sig. (2-tailed) .969
N 151 151
Table 12
The table above shows correlation results between gender and field of work. As can be observed,
the Pearson correlation coefficient is 0.003. This is an indication that there is a very weak
positive correlation between gender and field of work (Porter, 2008).
2. RELIABILITY TEST (Cronbach test)
Table of reliability test results
Reliability Statistics
Cronbach's
Alpha
N of Items
.841 32
Table 13
The reliability test results above show a Cronbach’s alpha of 0.84. Since this value is above 0.7,
it can be concluded that there is high internal consistency (Richler, 2012).
3. FACTOR ANALYSIS
KMO Value results table

Data analysis using SPSS 11
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .733
Bartlett's Test of Sphericity Approx. Chi-Square 1538.19
2
df 496
Sig. .000
Table 14
As can be observed from table 14 above, the KMO value was 0.733 for all the 32 questions. This
is usually a measure to determine whether the sample used in the study is adequate (Pallant,
2009). The bench mark for adequacy is normally a KMO value of 0.7 and above. Since our value
is 0.733, it can be concluded that the sample in this research study was adequate (Morey, 2015).
In order to extract significant factors, a eigenvalues > 1 was employed to determine which
factors to isolate from the 32 questions. The total variance explained matrix was also analyzed so
as to get those factors that had large values of variance. The results were as in table 15 below.
Total variance explained table
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
1 6.238 19.494 19.494 6.238 19.494 19.494
2 2.856 8.924 28.418 2.856 8.924 28.418
3 1.849 5.777 34.195 1.849 5.777 34.195
4 1.649 5.153 39.348 1.649 5.153 39.348
5 1.566 4.895 44.243 1.566 4.895 44.243
6 1.439 4.496 48.738 1.439 4.496 48.738
7 1.342 4.194 52.932 1.342 4.194 52.932
8 1.266 3.957 56.890 1.266 3.957 56.890
9 1.103 3.448 60.338 1.103 3.448 60.338
10 1.090 3.406 63.744 1.090 3.406 63.744
11 .976 3.050 66.794

Data analysis using SPSS 12
12 .940 2.936 69.730
13 .856 2.675 72.406
14 .814 2.543 74.949
15 .770 2.407 77.355
16 .733 2.290 79.646
17 .725 2.267 81.913
18 .646 2.018 83.931
19 .586 1.833 85.764
20 .559 1.746 87.510
21 .495 1.548 89.057
22 .453 1.416 90.473
23 .431 1.348 91.820
24 .414 1.292 93.113
25 .355 1.108 94.221
26 .345 1.077 95.298
27 .311 .972 96.271
28 .288 .901 97.171
29 .268 .836 98.007
30 .255 .797 98.804
31 .210 .657 99.461
32 .173 .539 100.000
Extraction Method: Principal Component Analysis.
Table 15
As can be observed the table indicated 10 factors which cumulatively accounted for the 63.74%
of the total variation of the data. This is an indication that among the 32 questions only the 10
factors can be extracted for the model due to their high variance.
Scree plot