Analyzing Data for FXS Association

This assignment focuses on evaluating the relationship between demand scores and a diagnosis of Fragile X Syndrome (FXS). Students analyze data presented in contingency tables and perform a chi-square test in SPSS to determine if there is a statistically significant association between these variables. The analysis considers expected counts and significance levels, ultimately concluding whether or not there is evidence to support an association.

SPSS

TABLE OF CONTENTS
Justification of descriptive and inferential statistics chosen.................................................................
Computation using SPSS tools and techniques....................................................................................
A. Examine the relationship between Aberrant Behaviour Checklist scores and the mean
Vineland score.............................................................................................................................1
B. Compare the QABF attention and demand scores of people with a diagnosis of FXS and
those with any other or no genetic diagnosis. Are people with FXS more likely to have lower
attention than demand scores?.....................................................................................................3
References............................................................................................................................................

JUSTIFICATION OF DESCRIPTIVE AND INFERENTIAL STATISTICS
CHOSEN
In the present study, researcher aims at evaluating the interaction between genetic and
environmental cases of SIB. Herein, the data has been generated through primary method in
which parents of children with fragile X syndrome (FXS) and parents of a heterogeneous sample
of children who displayed challenging behavior about the nature of their child's SIB and the
environments in which SIB is most likely to occur has been carried out to generate valuable data
or information. The current study aims to extend this work by including a between group
comparison and by using a more robust measure of behavioral function. In addition to this,
investigator also focuses on examining parent’s satisfaction with the support they have received
from local services, as well as each child’s adaptive behavior profile. In this regard, different
inferential statistics has been selected on the basis of varied aspects and evaluated by the means
of correlation coefficient and T-Test.
COMPUTATION USING SPSS TOOLS AND TECHNIQUES
A. Examine the relationship between Aberrant Behaviour Checklist scores and the mean
Vineland score
Null Hypothesis: H0: There is a positive relationship between Aberrant Behaviour Checklist
(ABC) and mean Vineland score.
Alternative Hypothesis: H1: There is a negative relationship between Aberrant Behaviour
Checklist (ABC) and mean Vineland score.
Bivariate Correlation Test:
Correlations
ABC Vineland Mean Score
ABC
Pearson Correlation 1 -.415*
Sig. (2-tailed) .028
N 28 28
Vineland Mean Score
Pearson Correlation -.415* 1
Sig. (2-tailed) .028
N 28 28
*. Correlation is significant at the 0.05 level (2-tailed).
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Interpretation:
The correlation coefficient between the two continuous level variables is called Pearson’s
r or Pearson product moment correlation coefficient (Garczynski, 2014). However, a positive r
value expresses a positive relationship between the two variables while on the other hand the
negative r value indicates a negative relationship between the variables (Berg, 2014). Herein,
relationship between aberrant behavior checklist (ABC) and Vineland mean score has been
computed with the help of correlation coefficient. However, with the significance value of 0.05
and the generated value of Pearson correlation of -0.415 clearly indicates that there is negative
relationship between both variables. This shows that, ABC approach of measuring the
challenging behavior of children is not feasible in generating desired result in terms of four
Vineland screener. This is because of the reason that there is negative relationship between the
two variables.
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As per the value of correlation, it can be said that there is a negative and moderate
relationship between Aberrant Behaviour Checklist (ABC) and mean Vineland score. This in
turn indicates that alternative hypothesis proves to be true. The value of -0.415 shows that with
increase in ABC of 100%, there is a corresponding decrease in mean Vineland score of 41.5%. It
can be therefore sad that both variables ABC and Vineland score are interrelated to each other.
However, there is an inverse relationship between them. Further, scatter plot indicates that the
values to be highly scattered in nature. It is due to inverse relationship that the values are
significantly scattered.
B. Compare the QABF attention and demand scores of people with a diagnosis of FXS and those
with any other or no genetic diagnosis. Are people with FXS more likely to have lower attention
than demand scores?
Null Hypothesis: H0: There is no significant association between QABF attentions and a
diagnosis of FXS
Alternative Hypothesis: H1: There is a significant association between QABF attentions and a
diagnosis of FXS.
Cross tabs
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
QABF (Att) *
Diagnosis 28 100.0% 0 0.0% 28 100.0%
QABF (Dem) *
Diagnosis 28 100.0% 0 0.0% 28 100.0%
Crosstab
Count
Diagnosis Total
Angelman ASD FXS No Generic
Diagnosis
PWS Rett
QABF
(Att)
0 0 0 3 1 0 0 4
1 0 0 0 0 1 0 1
3

2 0 0 2 0 0 0 2
3 0 0 2 0 0 1 3
4 0 0 0 1 0 0 1
5 0 0 1 0 0 0 1
6 0 0 0 1 0 0 1
7 0 0 2 0 0 0 2
8 0 0 0 1 0 0 1
9 0 1 1 1 0 0 3
10 0 0 0 1 0 0 1
11 0 1 0 0 0 0 1
12 0 0 0 2 0 0 2
13 1 1 0 2 0 1 5
Total 1 3 11 10 1 2 28
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 70.748a 65 .292
Likelihood Ratio 50.203 65 .912
N of Valid Cases 28
a. 84 cells (100.0%) have expected count less than 5. The minimum expected count is .04.
Interpretation:
On the basis of above chi square test association between two variables has been
evaluated. Herein, researcher focuses on identifying the association between QABF attentions
and a diagnosis of FXS. Considering the significance value of 0.05, when Asymp. Sig. (2-sided)
is high than null hypothesis accepted and vice versa. Therefore, on the basis of above
computation, Asymp. Sig. (2-sided) of 0.292 clearly indicates that null hypothesis has been
selected which indicates that there is no association between QABF attentions and a diagnosis of
FXS.
Null Hypothesis: H0: There is no significant association between demand scores and a diagnosis
of FXS.
Alternative Hypothesis: H1: There is a significant association between demand scores and a
diagnosis of FXS.
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Crosstab
Count
Diagnosis Total
Angelman ASD FXS No Generic
Diagnosis
PWS Rett
QABF
(Dem)
0 0 0 1 1 1 0 3
2 0 0 0 1 0 0 1
3 0 0 0 0 0 1 1
4 0 0 1 1 0 0 2
6 0 1 2 1 0 0 4
8 0 0 0 1 0 0 1
9 0 0 2 1 0 1 4
10 0 0 0 1 0 0 1
11 1 0 2 0 0 0 3
12 0 0 1 1 0 0 2
13 0 0 1 0 0 0 1
14 0 1 0 0 0 0 1
15 0 1 1 2 0 0 4
Total 1 3 11 10 1 2 28
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 53.561a 60 .708
Likelihood Ratio 37.525 60 .990
N of Valid Cases 28
a. 78 cells (100.0%) have expected count less than 5. The minimum expected count is .04.
Interpretation:
On the basis of above chi square test association between two variables has been
evaluated. Herein, researcher focuses on identifying the association between demand scores and
a diagnosis of FXS. Considering the significance value of 0.05, when Asymp. Sig. (2-sided) is
high than null hypothesis accepted and vice versa. Therefore, on the basis of above computation,
Asymp. Sig. (2-sided) of 0.292 clearly indicates that null hypothesis has been selected which
indicates that there is no association between demand scores and a diagnosis of FXS.
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REFERENCES
Berg, D. V. G. R., 2014. SPSS Independent Samples T Test. [Online]. Available through:
<http://www.spss-tutorials.com/spss-independent-samples-t-test/>. [Accessed on 4th
March 2016].
Comparing Means in SPSS (t-Tests), 2013. [Pdf]. Available through:
<http://www.radford.edu/~jaspelme/201/Fall%202006/SPSS-14_t-test_guide.pdf/>.
[Accessed on 4th March 2016].
Garczynski, J., 2014. T-Test in SPSS. [Pdf]. Available through:
<http://pages.towson.edu/jgarczyn/ttest.pdf/>. [Accessed on 4th March 2016].
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