Home| Journals | Statistical Calculator | About Us | Contact Us
    About this Journal  | Table of Contents
Untitled Document

[Abstract] [PDF] [HTML] [Linked References]

 

Statistical Analysis for Assessing Knowledge and Attitude on HIV/AIDS

A.V. Wadagale1, P.R. Gangwal2, A.R. Aradwad3, V.A. Jadhav4

1Assistant Professor, MIMSR Medical College, Latur (MS), INDIA

2Lecturer, G.H. Raisoni College of Arts, Commerce and Science, Wagholi, Pune (MS), INDIA

3Assistant Professor, MIMSR Medical College, Latur (MS), INDIA

4Reader, Science College, Nanded (MS), INDIA

Corresponding Addresses

Email : [email protected] , [email protected]

Research Article

Abstract:

 In this research article, we describe different statistical techniques for accessing knowledge and attitude towards the disease called HIV/AIDS. We carry out questionnaires on 200 students of computer science, stream of G.H. Raisoni College of arts, commerce and science. The statistical techniques like Pearson’s correlation coefficient, one way ANOVA, Tukey HSD, Levene’s statistics are used. Results are produced on statistical software SPSS 17.0 and MS-Excel. Result shows the relationship between Knowledge and Attitude on HIV/AIDS. At last, we give some recommendation, challenges and Major issues focus on HIV/AIDS.

 

Keywords: Pearson’s correlation coefficient, post hoc test, Tukey HSD, Levene’s statistics, HIV/AIDS

 

Introduction:

 

India is second most populated in the world where more than 1 billion people are living . HIV infected population is nearly 7% among these 1 billion that if we transfer it in actual number is reaches approximately 3.9 million+.  Out of these actual figure > 1% people are found in 6 states i.e. Maharashtra, Tamilnadu, Karnataka, Manipur, Nagaland and rest of country shares less than one percent infected population from HIV/AIDS. Based on annual surveillance data report which is collected from different sources shows that, men are victims in more than 75% cases while transmission route is through sex i.e. more than 85% cases. It also shows that there are significant variations among and within states. There are different mode of transmission of HIV/AIDS like sexual contact (84.53%), Blood and Blood products (3.37%), IDUs (3.36%), Perinatal transmission (2.14%) and other sources (6.70%).

There is need of knowledge of awareness of HIV/AIDS. For this there is need of continuous surveillance, awareness program, increased health care allocations, identification of high risk groups, access to treatment for all, removal of stigma and discrimination, developing appropriate guidelines, etc.

In this research article we collect the information from 200 undergraduate students from computer science branch.  The question arises here that why we select the students from this stream, there is thinking in society that most of the students from computer science may not have detail knowledge of awareness of HIV/AIDS. Considering the changing scenario of development in India, computer branch play a vital role in many of the leading management. Here, we check their inadequate knowledge and negative attitudes in management of HIV/AIDS patient may prevent. The application of scientific methods of computer science students resulting into fragmented care of the infected people with potential negative impact.

 

Material and Method:

 

As the number of computer science students are nearly 200, so we include all of the students in our study. To know the required information from the student we prepare the questionnaire which contains questions on Demographic information, factual questions to know their knowledge, opinion questions to assess their attitude towards HIV/AIDS. The question arises here that why we include the students from branch, because the main issue behind this is,  these students have the technical knowledge of software’s and information system but are the know anything about today’s hazardous condition of HIV/AIDS. From this point we can conclude the approximate knowledge and attitude of society towards HIV/AIDS.  To stratify the student’s knowledge we frame 27 questions out of which 26 questions have two outcomes i.e. yes or no, while to know their attitude on HIV/AIDS, we fixed 13 questions with three outcomes i.e. Agree, Undecided, Disagree. One question is place as open format.

Statistical techniques like ANOVA post doc test, Pearson’s correlation coefficient, homogeneity of variance is checked by Levene’s statistics and for comparison between first, second and third year students we used Tukey HSD which is useful for multiple comparison. All the result are carried out by using MS excel and SPSS 17.0 software.

 

Technical Analysis:

 

Pearson’s correlation coefficient:

 

A correlation is a number between -1 and +1 that measures the degree of association between two variables (call them X and Y). A positive value for the correlation implies a positive association (large values of X tend to be associated with large values of Y and small values of X tend to be associated with small values of Y). A negative value for the correlation implies a negative or inverse association (large values of X tend to be associated with small values of Y and vice versa). Correlation is symbolically represented by rxy

 

Description: 
r_{xy}=\frac{\sum\limits_{i=1}^n (x_i-\bar{x})(y_i-\bar{y})}{(n-1) s_x s_y}
      =\frac{\sum\limits_{i=1}^n (x_i-\bar{x})(y_i-\bar{y})}
            {\sqrt{\sum\limits_{i=1}^n (x_i-\bar{x})^2 \sum\limits_{i=1}^n (y_i-\bar{y})^2}},

 

Score

Levene Statistic

df1

df2

Sig.

5.814

2

197

.004

Table1: Test of Homogeneity of Variances

 

ANOVA:

 

When we want to compare means of more than two groups or levels of an independent variable, we use one way ANOVA. It is used to find the significant relations by assuming equal variance. The procedure of ANOVA involves the derivation of two different estimates of population variance. Then statistics is calculated from the ratio of these two estimates where one is between group variance estimate which is measure of effect of independent variable and other estimate within group variance which is error variance itself. The F ratio is ratio of between the groups and within the groups variance. When hypothesis is rejected i.e. when significant different is lies, post hoc analysis and other test needs to be performed to get the results.

 

Score

 

Sum of Squares

Df

Mean Square

F

Sig.

Between Groups

19.885

2

9.942

1.260

.286

Within Groups

1553.870

197

7.888

 

 

Total

1573.755

199

 

 

 

Table 2: One way Analysis of Variance (ANOVA)

 

 

 

Levene’s statistics for homogeneity of variance:

 

Test for Homogeneity of Variances Levene's test is used to test if k samples have equal variances which is first invented by Great Scientist Levene in 1960. The term homogeneity of variance is used when there are equal variance across samples. Levene’s test is less sensitive that we can used it for small number of samples also. It is alternative test for Bartlett test. As in ANOVA we assumed that variance are equal overall, we check this by using Levene’s Statistics.

The test statistics is W, which is defined as follow:

Description: W = \frac{(N-k)}{(k-1)} \frac{\sum_{i=1}^k N_i (Z_{i\cdot}-Z_{\cdot\cdot})^2} {\sum_{i=1}^k \sum_{j=1}^{N_i} (Z_{ij}-Z_{i\cdot})^2},

 

Where,

 

  • W is the result of the test,

  • k is the number of different groups to which the samples belong,

  • N is the total number of samples,

  • Ni is the number of samples in the ith group,

 

Yij is the value of the jth sample from the ith group

 

 

Description: Z_{ij} = \left\{\begin{matrix} 
|Y_{ij} - \bar{Y}_{i\cdot}|, & \bar{Y}_{i\cdot} \mbox{ is a mean of i-th group } \\ 
|Y_{ij} - \tilde{Y}_{i\cdot}|, & \tilde{Y}_{i\cdot} \mbox{ is a median of i-th group } \end{matrix}\right.

 

Description: Z_{\cdot\cdot} = \frac{1}{N} \sum_{i=1}^{k} \sum_{j=1}^{N_i} Z_{ij}

 

is the mean of all Zij

and

 

Description: Z_{i\cdot} = \frac{1}{N_i} \sum_{j=1}^{N_i} Z_{ij}

 

is the mean of Zij for ith group

We check the significance of W is tested against F(α,k − 1,Nk) where F is a quantile of the F test distribution, with k − 1 and Nk its degrees of freedom, and α is the chosen level of significance (usually 0.05 or 0.01).

Figure 1 : Boxplot for class Vs. score

 

Result and Discussion:

 

We carried out results in MS-Excel and SPSS 17.0 for Pearson’s correlation coefficient, post hoc test, Levene’s statistics, Tukey HSD. Here, as mention, we took place questionnaires on 200 students from computer science stream. As it estimated, the students from this stream have less knowledge of the disease HIV/AIDS, the results shows the same. We conclude stepwise results as follows:

1.      Karl Pearson’s Correlation coefficient shows that there is weak relationship between Knowledge and attitude (r=0.009) in computer science students. This means there is need to aware about the HIV/AIDS in these students. Furthermore, we check their covariance which is also not significant because value of correlation coefficient is very weak (r=0.004)

2.      Homogeneity of Variances:  1) The variances of the group are similar.  2) The group should be independent. Since Homogeneity of Variances should not be there for conducting ANOVA tests, which is one of the assumptions of ANOVA, we see that Levene’s test [Table 1] shows that homogeneity of variance is significant (p<0.05). As such, WE can be confident that population variances for each group are approximately not equal.

3.      [Table 2] shows that the F test values along with degrees of freedom (2,197) are not significance of 0.286. Given that p>0.05, we accept the null hypothesis and reject the alternative hypothesis that there is no significance difference in scores from different classes. F(2,197)0.05=1.260, P>0.05.

4.      Post Hoc analysis involves hunting through data for some significance. This testing carries risks of type I errors. These test are designed to protect type I errors, given that all the possible comparisons are going to be made. Post hoc tests are stricter than planned comparisons and it is difficult to obtain significance. We used Tukey test/honestly significant difference (HSD) test. Using Tukey HSD [Table 3], we can conclude that there is no significant difference of First, Second and Third year and in their scores.

5.      [Figure 1] gives the information about the class of first year, second year and third year with their individual scores.

 

 

 

 

 

 

 

 

Score

Tukey HSD

(I) Class

(J) Class

Mean Difference (I-J)

Std. Error

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

1

2

-.270

.456

.824

-1.35

.81

3

.627

.527

.461

-.62

1.87

2

1

.270

.456

.824

-.81

1.35

3

.897

.570

.259

-.45

2.24

3

1

-.627

.527

.461

-1.87

.62

2

-.897

.570

.259

-2.24

.45

Table 3 : Tukey HSD for multiple comparisons

From the results we recommend some major issues and challenges on awareness of HIV/AIDS like there should be continuous surveillance, Awareness of program, we must increase health care allocations, identification of high risk groups, access to treatment for all, developing appropriate guidelines for HIV/AIDS and removal of stigma and discrimination of this disease. There are some challenges like training workshops for different stakeholders, dissemination of national and international guidelines, behavioral studies for risk reduction, promotion of access to drugs as all ARVs are available in the market but not in the national program on HIV/AIDS control.

The major issues discussed are illiteracy, gender discrimination, imbalanced globalization, international collaboration with the local interest and promotion of human subjects in HIV/AIDS research, etc.

Acknowledgement:

We acknowledge to Principal, Staff members, student and specially NSS department of Raisoni arts, commerce and science college, wagholi, Pune(MS), INDIA.

References :

[1] Amira Sidig MD, Alsadig Mahgoub DTPH and Abbashar Hussein MD, “A study of knowledge, attitude, practice towards HIV/AIDS and prevalence of HIV/AIDS among tea sellers women in Khartoum State (April 2004 - May 2005)”, SudaneseJ. Of Public Health, Vol 4,No.1: 214-224,2009.

 

[2 ]Anthony J. Onwuegbuzie, Nancy L. Leech, “Post Hoc Power: A Concept Whose Time Has Come”, UNDERSTANDING STATISTICS, 3(4): 201–230, 2004

 

[3] Arvind Pandey, Dandu C S Reddy et al , “Improved estimates of India’s burden in 2006” Indian J. Med. Res129., 50-58, Jan-2009

 

[4] Becker N G,“A method of non-parametric back-projection and its application to AIDS Data”, STATISTICS IN MEDICINE, VOL. 10, 1527-1542, 1991.

 

[5] Chase, L. J., & Tucker, R. K.,“Statistical power: Derivation, development, and data-analytic implications.” The Psychological Record, 26, 473–486,1976

 

[6] Chase, L. J.,&Chase, R. B., “A statistical power analysis of applied psychological research”, Journal of Applied Psychology, 61:234–237, 1976.

 

[7] Chase, L. J.,&Tucker, R. K.,”A power-analytical examination of contemporary communication Research.”,  Speech Monographs, 42(1): 29–41,1975

 

[8] Cohen, J.  “The statistical power of abnormal social psychological research: A review.” Journal of Abnormal and Social Psychology, 65, 145–153, 1962

 

[9] Lal S.S., R.S. Vasan, Sankara Sarma P., Thankapran K.R., “ Knowledge and attitude of college students in kerala towards HIV/AIDS, sexually transmitted diseases & sexuality”, The national Medical Journal of India, 2000

 

[10] Longini I M , Byers R H, Hessol N A and Tan  W Y, “ Estimating the stage specific numbers of HIV infection using a Markov model and Backcalculation ”, Statistics in Medicine, Vol.11,1992

 

[11] Overview of making estimates of HIV/AIDS and its impact in countries with low- level or concentrated epidemics: The Workbook Method, UNAIDS/WHO, June 2003

 

[12] Jean-Philippe Laurenceau, Adele M. Hayes, and Greg C. Feldman,” Some Methodological and Statistical Issues in the Study of Change Processes in Psychotherapy.”, Clin Psychol Rev., 27(6): 682–695,July 2007 

 

 
 
 
 
 
  Copyrights statperson consultancy www

Copyrights © statperson consultancy www.statperson.com  2013. All Rights Reserved.

Developer Details