Quantitative Analysis Report: SPSS Analysis of Infant Development Data

This report presents a quantitative analysis of data from the National Infant Development Survey, focusing on a cohort of individuals tracked since 1958. The analysis employs SPSS to examine relationships between variables such as Body Mass Index (BMI) at ages 7 and 50, respondent's sex, father's education, and O-level qualifications. The study tests two main hypotheses: the correlation between BMI at different ages and the association of adiposity at age 50 with education and sex. Preliminary statistical analysis includes descriptive statistics and frequency distributions. Correlation analysis reveals a moderate relationship between BMI at ages 7 and 50. Regression analysis is conducted to assess the influence of sex on BMI, with interpretations of the statistical significance. The report concludes that there is no significant relationship between body mass index and sex. The report includes statistical tables, correlation matrices, and regression outputs to support the findings.

Quantitative Data
Analysis Report

Contents
INTRODUCTION.......................................................................................................................................3
I HYPOTHESIS AND METHODOLOGY.................................................................................................3
II. PRELIMINARY STATISTICAL ANALYSIS.......................................................................................4
III. REGRESSION ANALYSIS & INTERPRETATION..........................................................................10
CONCLUSION.........................................................................................................................................24
REFERENCES..........................................................................................................................................26
APPENDIX...............................................................................................................................................27

INTRODUCTION
The report is based on analysis of Data from the National Infant Development Survey, which
tracks a group of people raised in a particular week in 1958, was obtained at 3-5 year intervals
(Wang, Hastie & Owen, 2017). In order to do proper analysis of such aspects a range of SPSS
tests have been applied to assess whether there is suitable relation among variables or there is
any significant outcome that can derived from data set of 50 respondents containing male and
female.
I HYPOTHESIS AND METHODOLOGY
Descriptive data and binary logistic regression will be checked to examine the association
between BMI at 7 and 50 years of age. Models are evaluated independently with or without
response variable. The observations shall be recorded and viewed both verbosely and where
necessary, socially. We would test the following assumptions:
H1: There is significant relation between Body mass index at age of 7 and at age of 50.
H0: There is no significant relation between Body mass index at age of 7 and at age of 50.
H1: The adiposity at age 50 is measured as body mass index (BMI), is associated with own
education, father’s education and sex.
H0: The adiposity at age 50 is not measured as body mass index (BMI), is associated with own
education, father’s education and sex
Measures and variables:
Respondent’s sex- In the report this has been indicated by different aspects as 1 indicates to male
and 2 refers to female respondents.

Father left school at minimum age or not- This has been presented by two variables which are
one and two. Herein, 1 refers to Father left school at minimum age and 2 refers to Father has not
left school at minimum age.
Number of O-levels passed by 1974- In order to do so, two variables are chosen which are one
and two. Herein one equals to O-levels passed by 1974 and 2 equals to O-levels not passed by
1974.
Physical functioning score- Herein, 1 refers to excellent, 2 refers to good, 3 refers to average, 4
relates to poor and 5 relates to risky.
Emotional well-being score- Herein, 1 refers to excellent, 2 refers to good, 3 refers to average, 4
relates to poor and 5 relates to risky.
Social functioning score- Herein, 1 refers to excellent, 2 refers to good, 3 refers to average, 4
relates to poor and 5 relates to risky.
Social functioning score- Herein, 1 refers to excellent, 2 refers to good, 3 refers to average, 4
relates to poor and 5 relates to risky.
II. PRELIMINARY STATISTICAL ANALYSIS
Statistics

Responde
nt sex
Father
left
school
at
minimu
m age
or not
Numb
er of
O-
levels
passed
by
1974
Body
mass
index
at age
7
Body
mass
index
at age
50
Physical
functioni
ng score
Emotion
al well-
being
score
Social
functioni
ng score
Gener
al
health
score
N Valid 49 49 49 49 49 49 49 49 49
Missin
g 0 0 0 0 0 0 0 0 0
Mean 1.45 1.45 1.45 45.55 26.29 2.39 2.41 2.47 2.41
Median 1.00 1.00 1.00 44.00 25.00 2.00 2.00 2.00 2.00
Std.
Deviation .503 .503 .503 9.019 4.306 1.204 1.098 1.260 1.189
Variance .253 .253 .253 81.33
6
18.54
2 1.451 1.205 1.588 1.413
Respondent sex
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Male 27 55.1 55.1 55.1
Female 22 44.9 44.9 100.0
Total 49 100.0 100.0
Father left school at minimum age or not
Frequency Percent
Valid
Percent
Cumulative
Percent

Valid yes 27 55.1 55.1 55.1
No 22 44.9 44.9 100.0
Total 49 100.0 100.0
Number of O-levels passed by 1974
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Yes 27 55.1 55.1 55.1
No 22 44.9 44.9 100.0
Total 49 100.0 100.0
Body mass index at age 7
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 30 1 2.0 2.0 2.0
34 2 4.1 4.1 6.1
35 1 2.0 2.0 8.2
36 2 4.1 4.1 12.2
37 2 4.1 4.1 16.3
38 2 4.1 4.1 20.4
39 2 4.1 4.1 24.5
40 3 6.1 6.1 30.6
41 2 4.1 4.1 34.7
42 4 8.2 8.2 42.9
43 3 6.1 6.1 49.0
44 2 4.1 4.1 53.1
45 5 10.2 10.2 63.3

46 2 4.1 4.1 67.3
47 2 4.1 4.1 71.4
48 2 4.1 4.1 75.5
49 1 2.0 2.0 77.6
51 1 2.0 2.0 79.6
53 1 2.0 2.0 81.6
54 1 2.0 2.0 83.7
57 2 4.1 4.1 87.8
60 1 2.0 2.0 89.8
61 1 2.0 2.0 91.8
63 1 2.0 2.0 93.9
65 1 2.0 2.0 95.9
66 1 2.0 2.0 98.0
69 1 2.0 2.0 100.0
Total 49 100.0 100.0
Body mass index at age 50
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 17 1 2.0 2.0 2.0
19 2 4.1 4.1 6.1
21 3 6.1 6.1 12.2
22 1 2.0 2.0 14.3
23 3 6.1 6.1 20.4
24 9 18.4 18.4 38.8
25 6 12.2 12.2 51.0
26 4 8.2 8.2 59.2
27 4 8.2 8.2 67.3

28 2 4.1 4.1 71.4
29 3 6.1 6.1 77.6
30 1 2.0 2.0 79.6
31 4 8.2 8.2 87.8
32 1 2.0 2.0 89.8
33 2 4.1 4.1 93.9
34 1 2.0 2.0 95.9
35 1 2.0 2.0 98.0
37 1 2.0 2.0 100.0
Total 49 100.0 100.0
Physical functioning score
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Excellent 14 28.6 28.6 28.6
Good 15 30.6 30.6 59.2
Average 9 18.4 18.4 77.6
Poor 9 18.4 18.4 95.9
Risky 2 4.1 4.1 100.0
Total 49 100.0 100.0
Emotional well-being score
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid excellent 11 22.4 22.4 22.4

good 18 36.7 36.7 59.2
avergae 10 20.4 20.4 79.6
poor 9 18.4 18.4 98.0
risky 1 2.0 2.0 100.0
Total 49 100.0 100.0
Social functioning score
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid excellent 13 26.5 26.5 26.5
good 16 32.7 32.7 59.2
avergae 7 14.3 14.3 73.5
poor 10 20.4 20.4 93.9
risky 3 6.1 6.1 100.0
Total 49 100.0 100.0
General health score
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid excellent 12 24.5 24.5 24.5
good 19 38.8 38.8 63.3
avergae 6 12.2 12.2 75.5
poor 10 20.4 20.4 95.9
risky 2 4.1 4.1 100.0
Total 49 100.0 100.0

Analysis- The above done tables shows that the value of mean and standard deviation among
each aspect is not close to each other. As a result, this can be stated that there is no relation
among data set of 50 respondents. Apart from this, the mean value of Body mass index at age 7
has higher mean value as compared to Body mass index at age 50. This shows that at age of 7
people’s health was quite better and there was significant balance between weight and height.
III. REGRESSION ANALYSIS & INTERPRETATION
In order to test above mentioned hypothesis, a range of SPSS tests have applied in an effective
manner which are as follows:
Testing of hypothesis one:
H1: There is significant relation between Body mass index at age of 7 and at age of 50.
H0: There is no significant relation between Body mass index at age of 7 and at age of 50.
In order to such hypothesis, correlation analysis is crucial which is performed below in such
manner:
Correlation- Correlation is used to assess the association between measurable variables or
predictor data. In other terms, it's a test of how objects contribute to each other (Wagenmakers,
Verhagen & Ly, 2016). A research of how factors are associated is called a relationship analysis.
Correlations are helpful and if you can figure out what pattern of social you are that you can
make projections of future actions. Knowing what the future brings is very relevant in social
sciences such as governance and universal healthcare. Business owners also use these figures for
their expenditures and business strategies.
Correlations

Body mass
index at age
7
Body mass
index at age
50
Body mass index at
age 7
Pearson
Correlation 1 .311*
Sig. (2-tailed) .030
N 49 49
Body mass index at
age 50
Pearson
Correlation .311* 1
Sig. (2-tailed) .030
N 49 49
*. Correlation is significant at the 0.05 level (2-tailed).
Interpretation: On the basis of above done correlation analysis, this can be stated that there is
average relation between Body mass index at age 7 and Body mass index at age 50. This is so
because value of correlation is of 0.311 which is more than 0.3 but below 0.6.
Testing of hypothesis two:
In order to test of hypothesis, detailed analyses of hypothesis need to be done in an effective
manner that is as follows:
Regression analysis: Regression analysis is a group of mathematical techniques used to estimate
the correlation matrix and one or even more outcome variable (Shipley, 2016). It may be used to
determine the correlation among factors and to predict the interaction between them during the
potential. Regression analysis involves many variations, like normal, dynamic analysis, and –
anti. The most popular models are basic linear and several model structures. Nonlinear data
mining is widely used for more complex problems wherein selected variables display a causal
relation.

Excluded Variablesa
Model Beta In t Sig.
Partial
Correlation
Collinearity
Statistics
Tolerance
1 Father left school at
minimum age or not .b . . . .000
a. Dependent Variable: Body mass index at age 50
b. Predictors in the Model: (Constant), Respondent sex
Correlations
Body mass
index at age
50
Father left
school at
minimum age
or not
Respondent
sex
Pearson
Correlation
Body mass index at
age 50 1.000 .094 .094
Father left school at
minimum age or not .094 1.000 1.000
Respondent sex .094 1.000 1.000
Sig. (1-tailed) Body mass index at
age 50 . .261 .261
Father left school at
minimum age or not .261 . .000
Respondent sex .261 .000 .
N Body mass index at
age 50 49 49 49
Father left school at
minimum age or not
49 49 49

Respondent sex 49 49 49
Variables Entered/Removeda
Model
Variables
Entered
Variables
Removed Method
1 Respondent
sexb . Enter
a. Dependent Variable: Body mass index at age
50
b. Tolerance = .000 limit reached.
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .094a .009 -.012 4.332
a. Predictors: (Constant), Respondent sex
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 7.785 1 7.785 .415 .523b
Residual 882.215 47 18.771
Total 890.000 48
a. Dependent Variable: Body mass index at age 50
b. Predictors: (Constant), Respondent sex

Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 25.125 1.906 13.180 .000
Respondent sex .801 1.244 .094 .644 .523
a. Dependent Variable: Body mass index at age 50
Excluded Variablesa
Model Beta In t Sig.
Partial
Correlation
Collinearity
Statistics
Tolerance
1 Father left school at
minimum age or not .b . . . .000
a. Dependent Variable: Body mass index at age 50
b. Predictors in the Model: (Constant), Respondent sex
Collinearity Diagnosticsa
Model Dimension Eigenvalue
Condition
Index
Variance Proportions
(Constant)
Respondent
sex
1 1 1.946 1.000 .03 .03
2 .054 5.993 .97 .97
a. Dependent Variable: Body mass index at age 50
Interpretation: On the basis of above done regression analysis, this can be assessed that value of
significant difference is of 0.523 that is more than 0.05. This shows that there is no significant

relation between body mass index and sex of respondents. This is so because of unequal relation
between both variables (Peres‐Neto, Dra & ter Braak, 2017). Apart from this, from Pearson
correlation this can be stated that there is no relation between various kinds of factors including
body mass index at the age of 50 and sex and education of respondents. It has been done in
accordance of value of correlation is below 3.
Regression on body mass index at age of 7 and wellbeing variables:
Descriptive Statistics
Mean
Std.
Deviation N
Body mass index at
age 7 45.55 9.019 49
Physical functioning
score 2.39 1.204 49
Emotional well-being
score 2.41 1.098 49
Social functioning
score 2.47 1.260 49
General health score 2.41 1.189 49
Correlations
Body
mass
index at
age 7
Physical
functioning
score
Emotional
well-being
score
Social
functioning
score
General
health
score

Pearson
Correlation
Body mass
index at age
7
1.000 .099 -.246 -.073 -.031
Physical
functioning
score
.099 1.000 .035 -.191 .135
Emotional
well-being
score
-.246 .035 1.000 -.141 .285
Social
functioning
score
-.073 -.191 -.141 1.000 -.339
General
health score -.031 .135 .285 -.339 1.000
Sig. (1-
tailed)
Body mass
index at age
7
. .250 .044 .310 .416
Physical
functioning
score
.250 . .405 .094 .178
Emotional
well-being
score
.044 .405 . .166 .024
Social
functioning
score
.310 .094 .166 . .009
General
health score .416 .178 .024 .009 .

N Body mass
index at age
7
49 49 49 49 49
Physical
functioning
score
49 49 49 49 49
Emotional
well-being
score
49 49 49 49 49
Social
functioning
score
49 49 49 49 49
General
health score 49 49 49 49 49
Variables Entered/Removeda
Model
Variables
Entered
Variables
Removed Method
1 General
health score,
Physical
functioning
score,
Emotional
well-being
score, Social
functioning
scoreb
. Enter

a. Dependent Variable: Body mass index at age
7
b. All requested variables entered.
Model Summary
Model R
R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .283a .080 -.003 9.033 .080 .961 4 44 .438
a. Predictors: (Constant), General health score, Physical functioning score, Emotional well-
being score, Social functioning score
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 313.612 4 78.403 .961 .438b
Residual 3590.510 44 81.603
Total 3904.122 48
a. Dependent Variable: Body mass index at age 7
b. Predictors: (Constant), General health score, Physical functioning score,
Emotional well-being score, Social functioning score
Coefficientsa

Model
Unstandardize
d Coefficients
Standardize
d
Coefficients
t Sig.
Correlations
Collinearity
Statistics
B
Std.
Error Beta
Zero
-
order
Partia
l Part
Toleranc
e VIF
1 (Constant) 50.764 6.009 8.44
8 .000
Physical
functionin
g score
.677 1.106 .090 .612 .544 .099 .092 .088 .958 1.044
Emotional
well-being
score
-2.157 1.241 -.263
-
1.73
9
.089 -.246 -.254 -.25
1 .917 1.091
Social
functionin
g score
-.663 1.115 -.093 -.594 .555 -.073 -.089 -.08
6 .861 1.161
General
health
score
.001 1.209 .000 .001 1.00
0 -.031 .000 .000 .823 1.215
a. Dependent Variable: Body mass index at age 7
Collinearity Diagnosticsa
Mode
l
Dimensio
n
Eigenvalu
e
Conditio
n Index
Variance Proportions
(Constant
)
Physical
functionin
g score
Emotiona
l well-
being
score
Social
functionin
g score
Genera
l health
score
1 1 4.365 1.000 .00 .01 .01 .01 .01

2 .289 3.887 .00 .03 .02 .41 .12
3 .188 4.813 .00 .70 .18 .00 .08
4 .122 5.990 .00 .04 .65 .04 .56
5 .036 11.089 1.00 .22 .14 .54 .23
a. Dependent Variable: Body mass index at age 7
Interpretation: On the basis of above done analysis, this can be stated that there is significant
difference between because value of P is more than 0.05 that is of 0.438. Apart from this, it can
be stated on the basis of correlation that there is poor relation among variables because value is
less than 0.3 under each element (Green, 2016).
Regression on body mass index at age of 50 and wellbeing variables and respondent sex:
Descriptive Statistics
Mean
Std.
Deviation N
Body mass index at
age 50 26.29 4.306 49
Physical functioning
score 2.39 1.204 49
Emotional well-being
score 2.41 1.098 49
Social functioning
score 2.47 1.260 49
General health score 2.41 1.189 49
Respondent sex 1.45 .503 49

Correlations
Body
mass
index
at age
50
Physical
functioning
score
Emotional
well-being
score
Social
functioning
score
General
health
score
Respondent
sex
Pearson
Correlation
Body mass
index at
age 50
1.000 -.275 -.435 .290 -.040 .094
Physical
functioning
score
-.275 1.000 .035 -.191 .135 -.122
Emotional
well-being
score
-.435 .035 1.000 -.141 .285 .039
Social
functioning
score
.290 -.191 -.141 1.000 -.339 .121
General
health score -.040 .135 .285 -.339 1.000 -.034
Respondent
sex .094 -.122 .039 .121 -.034 1.000
Sig. (1-
tailed)
Body mass
index at
age 50
. .028 .001 .022 .394 .261
Physical
functioning
score
.028 . .405 .094 .178 .203

Emotional
well-being
score
.001 .405 . .166 .024 .396
Social
functioning
score
.022 .094 .166 . .009 .204
General
health score .394 .178 .024 .009 . .408
Respondent
sex .261 .203 .396 .204 .408 .
N Body mass
index at
age 50
49 49 49 49 49 49
Physical
functioning
score
49 49 49 49 49 49
Emotional
well-being
score
49 49 49 49 49 49
Social
functioning
score
49 49 49 49 49 49
General
health score 49 49 49 49 49 49
Respondent
sex 49 49 49 49 49 49
Variables Entered/Removeda

Model
Variables
Entered
Variables
Removed Method
1 Respondent
sex, General
health score,
Physical
functioning
score,
Emotional
well-being
score, Social
functioning
scoreb
. Enter
a. Dependent Variable: Body mass index at age
50
b. All requested variables entered.
Model Summary
Model R
R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .574a .329 .251 3.726 .329 4.221 5 43 .003
a. Predictors: (Constant), Respondent sex, General health score, Physical functioning score,
Emotional well-being score, Social functioning score
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.

1 Regression 292.990 5 58.598 4.221 .003b
Residual 597.010 43 13.884
Total 890.000 48
a. Dependent Variable: Body mass index at age 50
b. Predictors: (Constant), Respondent sex, General health score, Physical
functioning score, Emotional well-being score, Social functioning score
Interpretation: On the basis of above done tables, this can be assessed that there is regression
exists among BMI at the age of 50, respondents’ sex and wellbeing variables. The rationale
behind is that value of R and R square is under expectancy level (Meyer, Van Witteloostuijn, &
Beugelsdijk, 2017). Apart from this, there is significant difference among data set and value is of
0.003 that is less than 0.05.
CONCLUSION
On the basis of above report this can be concluded that there is average relation between
respondents whose BMI at the age of 7 and 50 is distinct. This has been tested in accordance of
SPSS tests and correlation. While from second hypothesis of report this can be stated that
hypothesis is wrong and it has been tested under regression analysis. Apart from this, regression
analysis is also applied on some other variable under which distinct outcome is produced.

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equations and causal inference with R. Cambridge University Press.
Peres‐Neto, P. R., Dray, S., & ter Braak, C. J. (2017). Linking trait variation to the environment:
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524.
Meyer, K. E., Van Witteloostuijn, A., & Beugelsdijk, S. (2017). What’s in a p? Reassessing best
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APPENDIX