Project analysis -22 dec 2014-am (1)
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Haruna Emmanuel P73270
Suresh Mani P77104
Ashok Sivaji P77800
Dwi Budiningsari P75375
Hamzah Wali P74918
Nadzirah Hanis Zainordin P75182
Marwan Jalambo P75376
Ooi Theng Choon P75129
Presenters
NNPD6014 – Group 1
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Introduction Research Question Objective Research Hypothesis Methodology
◦ Research Design◦ Study Population◦ Study Population and Sampling Frame◦ Sampling Size◦ Sampling Methodology◦ Inclusion and Exclusion◦ Tools & Instrument◦ Reliability and Validity
Analysis Two-way ANOVA between group
Binary Logistic Regression
Conclusion References
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Nutrition is an important factor in the performance and
health of human.
80% of adults do not know recommended calorie level(US
National surveys in US (2011)
20% of students are aware of recommended calorie and
40% overweight (Yu-Chieh et al 2012).
Diseases vary by races and gender (Rajakumar , 2012) )
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In addition to the population breakdown,
another reason for this is because
1. Malays reported highest percentage of
hypertension 17% followed by Chinese 11%
(Rajakumar , 2012)
2. Chinese reported lowest health awareness
(Rajakumar , 2012)
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Adults, 18 years and above
◦ 33.3% (5.4 million) are pre-obese
◦ 27.2% (4.4 million) are obese
Children below 18 years (based on weight for
age status)
◦ 3.9% (0.3 million) are obese
Source: NATIONAL HEALTH AND MORBIDITY SURVEY 2011
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Transition from age related disease to Lifestyle related disease
Fifteen percent of Malaysians above the age of 18 years are diabetics.
The prevalence among those above 40 years old was 17.6%,
Hypertension: 31.6% once they reach age 55 years old
Hypertension 35.8% in above 40 years
Source: NATIONAL HEALTH AND MORBIDITY SURVEY 2011
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Can gender, age, race, BMI, education level and family history predict knowledge scores on calorie and BMI among students and staff of Faculty of Health Sciences, UKM ?
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To determine interaction between race and school
on the knowledge scores on calorie and BMI among
FSH student and staff
To determine the influence of gender, age ,race,
BMI, education level and family history in predicting
knowledge scores on calorie and BMI among FSH
student and staff
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Gender, age, race, BMI, education level and family history influence knowledge scores on calorie and BMI among students and staff of Faculty of Health Sciences, UKM.
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Race
Age
BMI
Gender
Education
Family History
Knowledge Scores on Calorie and BMI
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Cross sectional study using a web-based survey questionnaire
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Period:◦ October – December 2014
Location:◦ Faculty of Health Science, UKM, Jalan Raja Muda
Abdul Aziz, Kuala Lumpur, Malaysia
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Study Population◦ Students and staff of FHS
Sampling Frame◦ List of students and staff currently enrolled and
employed in FHS respectively
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For a known population of N=1500, ( Krejcie R V & Morgan D W. , 1970)
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For a known population of N=1500, ( Krejcie R V & Morgan D W. , 1970)
n = 3.841 (1500) (0.5) (1-0.5)0.052 (1500-1) + 3.841 (0.5) (1-0.5)
= 1440.375
3.75 + 0.96025
= 1440.375
3.81
n = 378
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Random Sampling
Target of minimum 31 samples (n > 30) from each schools◦ Diagnostic & Applied Health Sciences (DAHS)◦ Healthcare Sciences (HS)◦ Rehabilitation Sciences (RS)
Corder, G. W., & Foreman, D. I. (2009, p.2) The minimum sample size for using a parametric statistical test varies among texts. For example, Pett (1997) and Salkind (2004) noted that most researchers suggest n>30. Warner (2008) encouraged considering n>20 as a minimum and n> 10 per group as an absolute minimum."
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Survey was sent to 150 samples randomly from each schools◦ 278 responses received out of 450 (62 %)◦ Complete questions response was obtained from
179 /450 ( 40%)
n = 179
Comparison with calculated sample size◦ 179/378, (47%)
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33%
45%
22%
Breakdown of Respondents by Schools
within FHS
Diagnostics &
Applied Health
ScienceHealthcare
Science
Rehabilitation
Science
33% => 59 responses
45% => 81 responses
22% => 39 responses
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Inclusion ◦ Both full & part time students enrolled
◦ Staff employed by FHS
◦ Malaysian citizen
◦ Responses received from 25th Nov 2014
Exclusion◦ Incomplete survey items
◦ Student / staff on study leave
◦ Response received after 5th Dec 2014, 6.30pm
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Tool◦ Use Mi-UXLab version 1.0◦ http://usability.mimos.my/miuranus/survey/login.
php?key=rE6xWTOx34pfn8k82Fk4FT41tOwoh5◦ 38 Items
Part A Knowledge on Calorie ( 23 questions)
Part B BMI (5 questions)
Part C Demographic (10 questions)
◦ Correct answer is given one mark, wrong answer is given no marks
◦ Part A & B : Each question has 5 options (MCQ)◦ Score > 70% rated as High Score
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Mean = 53%IQR = 17High Score = Mean + IQR = 70%
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Reliability◦ Reliability was done using Cronbach/Coefficient alpha
◦ For both Test 1 and Retest 2, Cronbach Alpha,
◦ α > 0.7 = > Highly reliable questionnaire items
◦ Paired-Sample t-test t(23) = -0.591, p=0.560
◦ Since p > 0.05, there is no significant difference between Test 1 and Retest2
Cronbach Alpha N of items
Test 1 0.771 28
Retest 2 0.714 28
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Validity◦ Face validity
Readability Statistics Score / Grade
Flesch Reading Ease 55.0FairlyDifficult
65.3 (Standard)
Flesch Grade Level 8.7 6.7
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Validity◦ Face validity
Readability Statistics Score / Grade
Flesch Reading Ease 55.0FairlyDifficult
65.3(Standard)
Flesch Grade Level 8.7 6.7
Expected due to technical and localized terms used in survey such as ‘ikan kembong’, saturated, unsaturated, monosaturated, polysaturated, trans-fatty acid, ‘otak-otak’, calorie, BMI
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Two Way ANOVA Between Groups
Binary Logistics Regression
Use SPSS version 22.0
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School Mean SE
Malay 50.73% 1.40%
Non-Malay 55.21% 1.45%
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School Mean SE
Diagnostic 47.67% 1.39%
Healthcare 60.13% 1.48%
Rehabilitation 44.41% 1.76%
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• There is no interaction effect for School * Race , F ( 2,173)=0.093 (p=0.911)
• We look at main effect, both school and race are significant p < 0.05, post hoc analysis required
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According to Cohen (1998), for both school and race, effect size is small r < 0.29 ,
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• The mean score from Diagnostic and Healthcare schools significantly differ ( p < 0.05)
• The mean score from Rehabilitation and Healthcare schools significantly differ ( p < 0.05)
• Sample size for schools are widely different, use post-test Hochberg’s GT2
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In order to determine the interaction between race and school on the knowledge
scores on calorie and BMI among FSH student and staff, a Two way between groupsANOVA was performed.
Prior to interpreting the results of Two-way between group ANOVA, severalassumptions were evaluated. First, the Cook’s distance obtained from the residualstatistics ( < 1) shows that each independent variable is normally distributed. Second,the Levene’s test is not significant (p > 0.05) implying the population variances forschool and race is approximately equal, hence homogeneity of variance is assumed.Descriptive statistics , means of independent variables are shown in the tables.
Dependent Variable Independent Variable Mean Std Dev.
% Score by
Respondents
Diagnostics School 47.68% 1.39%
Healthcare School 60.13% 1.47%
Rehabilitation School 44.41% 1.76%
Dependent
Variable
Independe
nt Variable
Mean Std Dev.
% Score by
Respondents
Non-Malay 55.21% 1.45%
Malay 50.73% 1.40%
The test of between subject effects, shows that there is no interaction effect forSchool * Race , F ( 2, 173)=0.093 (p=0.911). However, for the main effects, bothschool and race are significant p < 0.05, post hoc analysis required. Since the samplesize for schools are widely different, Hochberg’s GT2 posttest was used. From this, it wasfound that the mean score from Diagnostic and Healthcare schools significantly differ;and the mean score from Rehabilitation and Healthcare schools significantly differ.
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-2 Log Likelihood Cox & Snell R Square Nagelkerke R Square
88.513 0.167 0.339
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Chi-square df Sig.
6.101 8 0.636
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Odds Ratio (estimated)
Lower Upper BLR (Predictor Ratio)
Race(NonMalay / Malay)
1.091 0.416 2.861 1.490
Gender(Female/Male)
1.231 0.337 4.496 1.664
Education(Graduate / Undergraduate)
18.167 4.044 81.615 15.310
Family Disease (Family History / No Family History)
1.966 0.675 5.723 1.412
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Two – way ANOVA
Binary Logistic Regression
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There are no interaction between race and school
on knowledge score on calorie and BMI among
FSH student and staff. However main effects,
school and race have significant effect on
knowledge score
Education made a significant contribution in
predicting knowledge scores on calorie and BMI
among FSH student and staff
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There is no interaction effect (p<.05) between School * Race F ( 2,173)=0.093 (p>.05)
We can see from the Test of Between-Subjects Effects table that ◦ for our factor “School”, we have found a
significant effect, F (2, 173) = 30.698, p < 0.05.◦ for our factor “Race”, whether Malay or non-
Malay we have found a significant effect, F (1,173) =7.926, p < 0.05
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A logistic regression analysis was conducted to predict the knowledge level on calorie and BMI. This was coded as (High =0 and Low = 1) while the independent variables were Education, Gender, Race, Family history of disease, BMI and Age.
A test of the full model against a constant only model was statistically significant, indicating how well that the predictors influence the prediction. (chi square = 32.627, p < .001 with df = 6).
Nagelkerke’s R2 of .339 indicated a moderately weak relationship between prediction and grouping. Prediction success overall was 88.8%.
The Wald criterion demonstrated that only education made a significant contribution to prediction (p = .002). Other variable were not significant predictors.
EXP(B) value indicates that when education level is raised by one unit the odds ratio is 15 times as large and therefore graduate are 15 more times likely to be more knowledgeable on calorie and BMI than undergraduate.
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