Factors influencing the choice of the people to lead a healthy lifestyle

Concept and health indicators, the impact on his bad habits. A study of factors that influence the choice of people to stick to a certain type of lifestyle on the example of people aged 14 to 40 years old. The stages of the analysis and the results.

Рубрика Социология и обществознание
Вид курсовая работа
Язык английский
Дата добавления 28.08.2016
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6. Factor and Cluster Analyses

To perform the cluster analysis, we should take individuals answers to the questions concerning «Health» category: l5_0_(Number of visits to the doctor), l5_(Did you have any health problems during the last 30 days?), l20_(Were you in a hospital during the last 3 months?), m71_(Do you smoke now?), alclast30 (Did you consume alcohol during the last 30 days?), m11311a_(Did you go to fitness in the last 30 days?), m113_1a_(Did you run in the last 30 days?), m113_2a_(Did you swim in the last 30 days?), m113_3a_(Did you dance in the last 30 days?), m113_4a_(Did you play basketball or football in the last 30 days?), m114_(Which variants of the descriptions does fit your behavior better?), l31_(Did you miss your work during the last 30 days due to illness?), m1_(Weight), m2_1_(How have your weight been changed over the last year?). For conducting the cluster analysis 14 explanatory variables are taken. However, it is no reasonable to make the cluster analysis on these 14 variables since many variables such as l5_0_(Number of visits to the doctor) and l5_ (Did you have any health problems during the last 30 days?) correlate with each other. As in our analysis we should include variables that do not correlate with each other.

Table 7. Correlation Matrix

The way to solve this type of problem is to use PCA Factor Analysis. The factor analysis is a way to reduce the dimensions and quantity of variables. It also solves a problem of finding an allocation of only significant variables that make influence on dependent variables;

It is assumed that the observed variables are just a linear combination of some unobservable factors. It facilitates the solution of the multicollinearity problem in the model in the number of ways. Firstly, the factor analysis reduces the quantity of variables (creating a Factor or a Principal Component, in other words a «pool» of similar variables) Secondly, it also provides possibility of rotation of Factors or Principal Components so that we can use uncorrelated factors in the model.

Next step is to perform the factor analysis. To determine the number of factors that are needed to be selected, we should carefully examine the «Cumulative column». As it was mentioned in the methodology part on the research, in order to define the number of factors that will be used for modeling, we should select all the values that do not exceed 0.8.

Table 8. Factors

According to the criteria described above we should select 9 factors. However, we assume that these factors are dependent. Therefore, we perform the orthogonalization (rotation) of factors in order to obtain factors that are independent between each other. As a result of the orthogonalization we obtain four independent factors. The following task is to interpret their meaning. In the table below we can observe factor loading matrix containing the correlation between factors and initial variables responsible for determination of lifestyle type.

Table 9. Factor loading matrix

Variable

Factor1

Factor2

Factor3

Factor4

How often do you visit a doctor during the year?

-0.6172

0.0392

0.2168

0.1529

Did you have any health problems during the last 30 days?

0.7195

-0.0585

0.1438

0.0786

Were you in a hospital during the last 3 months?

0.4875

0.0917

-0.2717

0.0479

How would you rate your health?

0.5703

0.1818

0.1590

-0.0631

Do you smoke now?

0.0135

0.0889

0.6564

0.2880

Did you consume alcohol during the last 30 days?

0.0223

0.0601

0.7776

-0.1460

Did you run in the last 30 days?

-0.0461

0.6388

0.1411

0.0098

Did you go to fitness in the last 30 days?

0.0394

0.5649

-0.1956

0.0916

Did you swim in the last 30 days?

0.0108

0.5059

-0.1087

0.1160

Did you dance in the last 30 days?

-0.0082

0.0667

-0.0024

0.8575

Did you play basketball or football in the last 30 days?

0.0016

-0.5814

-0.1689

0.3131

How often do you do physical excercises?

-0.0687

0.6540

0.1474

0.2993

Did you miss your work during the last 30 days due to illness?

0.6392

-0.1307

-0.0067

-0.0005

Health

Sport

Habbits

Rest

We can observe from the Table 9 that the first factor correlates mostly with questions about sports (running, swimming, fitness, football/basketball) and with the answer to the question how often the respondent does sports. The second factor is correlated with the answers concerning questions of visiting the hospital, if he stayed at the hospital, absenteeism of work due to illnesses. The third correlates with smoking and drinking some alcohol. The fourth is correlated with dancing and body weight.

On the basis of this information, we can interpret the obtained factors in the following way. The first factor is responsible for sports, the second one - for health, the third one - for bad habits. Factor four is harder to interpret, however it can be considered as responsible for leisure time.

Based on the factor analysis, the cluster analysis is conducted. Since respondents do not answer all the questions, there is a large number of missing values, therefore it is not possible to create cluster analysis based on answers of all 1132 of respondents as a panel data analysis. The way to solve this problem is to perform the cluster analysis on results of the factor analysis for the entire sample from 2005 to 2012.

We should make a selection number of clusters according to the method of single linkage, using Calinski statistics. (Appendix 8)

We can see that there is a break for 3 and 4 clusters. The highest VRC ratio is reached when the number of clusters is equal to 4. We have tried to conduct the research using 4 different clusters, but this attempt has not been successful. In case the sample was divided into 4 clusters it was quite hard to interpret them as they were close to each other in terms of characteristics of health condition. The variation between clusters was insufficient which is unacceptable for the purpose of our research. So, we decided to divide our sample into 3 clusters to obtain more pronounced differences between them.

Besides, it should be stated that the sample used in the research is scarce and include only 1 919 respondents' answers. This is another reason to use 3 clusters instead of 4. In case of 3 clusters we are able to provide proper interpretation of all the clusters that are used in the paper and obtain proper amount of observations in each cluster.

The cross table 10 given above provides insight into respondents' answers about their self-assessment of their health status according to the formed cluster. We use Сhi2 statistics to make sure that interconnection between the cluster number and level of health can be interpreted. However, at this point we are not able to make definite conclusion about the meaning of the clusters. Probably, this question cannot be considered as a solid indicator of person's health condition due to the fact that self-evaluation is not always a correct estimation of actual health. In other words, the person might believe that he is healthy despite the fact that he or she suffers from a numerous diseases. That is why we need to examine the distribution of the answers according to other questions in order to get an adequate description of the clusters.

From the table 11, based on Chi2 statistics, we may find out that the interrelationship between the number of the cluster and health status exists. We can observe that in the first cluster we have 718 respondents (who represent 91% of the cluster) that provided the answer that they did not have health problems in the last 30 days, whereas in the third cluster 283 people (79% of the cluster) said that they had problems with their health. This implies that the first cluster represents people with good health status, the third cluster has people with poor level of health and the second cluster is neutral. However, neutral cluster is closer to the first one, characterized by better state of health condition, as the number of respondents that say that they had no problems is 618 (81%), which is not significantly lower than 91% in the first cluster. At this point it could be assumed that we have formed the following clusters:

1) Cluster 1 - people with good health condition;

2) Cluster 2 - people with neutral health condition;

3) Cluster 3 - people with poor health condition.

Firstly I would like to examine interconnections between clusters and qualitative variables. In order to perform this relationship, we build a cross tabulation matrix that shows interdependence between the number of a cluster and a qualitative variable. The results are presented in Appendices 2, 3 and 4. Based on the information provided in the Appendices, we found out that variables such as: marital status, level of education and gender are significant. We have discussed qualitative analysis and now we would like to give descriptive statistics of quantitative variables. From Appendices 5,6 and 7 we observe that in cluster 1 the average weight of respondents is 63 kilograms and the average age is 25 years old. In cluster 2 we find out that average age of participants is 29 years old, while average weight of a respondent od 78. What concerns cluster 3, the average weight is equal to 64 kilograms ant average age of a respondent is 26.

It should be also noted that we conducted a Chi2 test to define the relationship of attributes to different variables. Chi2 test evaluates the hypothesis that there is no relationship between the variables. If p-value is less than 0.05, then we reject the null hypothesis and claim that the relationship between the variables could be interpreted. In our case p-value is equal to 0.000, so we may make assumptions about the number of a cluster and answers of respondents concerning diseases in the last 30 days.

Next we are going to examine relationship between clusters and day that people skip at work due to illnesses.

According to information provided in the table 14, 99% of observations in cluster 1 did not miss the work due to illnesses in the last 30 days. Simultaneously cluster 3 represents observations (more than 35%) that have connection to the recent health issues. Based on Chi2 statistics we may conclude that interrelationship between the number of the cluster and missed days at work exists. As a result we can make an assumption that the cluster structure that has been detected earlier is adequate.

The information in the table 13 provides characteristic whether the person has spent time in the hospital in last 3 month or not. According to Chi2 statistics we discover that interrelationship between these variables exist. We can clearly observe that the number of positive answers in cluster 3 is almost 6 times higher than in cluster 1. Additionally the share of observation that corresponds with negative answer to the question about spending time in the hospital is much higher in cluster 1 than in cluster 3. As a result, we can assume that it is another point that proves the cluster structure that has been stated earlier in the text.

However, the only cluster analysis is not enough to identify trends. It is interesting to find out how the individual's match to a particular cluster would change if, for example, the marital status transformed, a person got higher level of education or gained weight and so on. We run a multinomial logistic regression in order to find interdependencies between the cluster number and gender, age, martial status, level of education and weight.

The number of a cluster is taken as a dependent variable (1 cluster - good, 2 cluster - neutral, 3 cluster - poor). Age, gender, weight, martial status and level of education are taken as independent variables to test the afforested hypotheses. So, we run a multinomial logit regression and assume that the second (neutral) cluster is the base one.

The next section contains the description of results concerning the impact of various characteristics of respondents on their lifestyle.

7. Estimation Results

We use cluster 2 with neutral level of health as a base cluster and compare the obtained results for cluster 1 and cluster 3 with it. From the estimation of the results in table 15 we observe that the variable weight is not significant neither for cluster 1 nor for cluster 3. We are not able to interpret weight in this particular model, therefore we have evidence that provides us with the possibility of rejecting the hypothesis 2.

Next step is to run the regression with all the same variables except weight.

Now we receive results where all variables are significant either in one of the cluster or in both. If we observe change in marital status, the multinomial log-odds for cluster with good level of health (cluster 1) relative to cluster with neutral level of health (cluster2) would be expected to increase by 0,268 while holding all the variables in the model constant. If a person has increased one point in the level of education, the multinomial log-odds for cluster 1 relative to cluster 2 would be expected to decrease by 0,1 unit, while holding all other variables in the model constant. If a person becomes older, the multinomial log-odds for cluster number 1 relative to cluster number 2 would be expected to increase by 0,039 while holding all the variables in the model constant. What concerns gender, the multinomial logit for females relative males is 1.1 unit lower for being in cluster 1 than in cluster 2 given all the prediction variables constant.

From the estimation results for cluster 3, it is seen that all the variables except marital status are significant.

So, if a person has increased one point level of education, the multinomial log-odds for cluster 3 relative to cluster 2 would be expected to increase by 0,673 unit, while holding all other variables in the model constant. If a person becomes older the multinomial log-odds for cluster number 3 relative to cluster number 2 decreases by 0,312 while keeping other variables constant. Also, the multinomial logit model estimates that females relative to males is 0,468 higher for being in cluster 3 relative to cluster 2.

At this point it is essential to provide interpretation of the results according to relative risk rate of being in other cluster than the base (in our case being in cluster 1 or 3 instead of cluster 2).

Firstly, that change in marital status will result in relative risk for cluster 1 relative to cluster increase by a factor of 1,3 keeping all other variables equal. In order to explain such a phenomenon, we can assume that married people tend to pay more attention to their health. Married people often have kids, so they have to be responsible and stick to healthy lifestyle in order to be a good example for children. As a result, hypothesis 5 cannot be rejected, as married people tend to have healthier lifestyle patterns.

Secondly, I would like to discuss level of education. If this parameters changes 1 point, the relative risk of being in cluster 1 relative to cluster 2 would be expected to increase by a factor of 0,9 if all other variables in the model held constant. At the same time relative risk for cluster 3 relative to cluster 2 has a positive factor of 1,16 holding all other variables constant. This result corresponds with the idea that people who get more education suffer from health issues. Good level of education could be interpreted as a sign that the person has a job that requires less physical work. As a rule, people with better education tend to work in offices at least 8 hours a day and spend Fridays night at bars or clubs. All of the mentioned above does not lead to healthy lifestyle. At the same time people who were not lucky enough to get good education has occupations that require physical work that keeps them fit. Thus, these results allow us to reject the hypothesis 4.

What concerns unit increase in age, the relative risk for cluster 1 relative to cluster 2 is expected to increase by a factor of 1,03 holding all other variables constant, while in cluster 3 relative to cluster 2 we expect an increase by a factor of 0,96 keeping all other variables constant. The possible explanation of such results is connected to data that has been used in the research. We have excluded people over 40 years from the sample in order to eliminate the effect of illnesses that occur with age. Thus, we can assume that people in their middle age tend to care more about their health status. They probably understand that they are not the youth anymore and have to keep the diet and do additional exercises to keep themselves in a healthy shape. Thus, we have received evidences that allow us to reject hypothesis 1 that health status deteriorates with age.

In conclusion, let us discuss gender differences. It should be mentioned that according to the coding of the data the unit increase in this variable implies switch from male to female. Therefore, for females relative to males, the relative risk for cluster that is described as «good health» relative to «neutral» cluster would be expected to increase by a factor of 0,25 given all the variables held constant. What concerns cluster that is called «poor health» in the current research, the relative risk for it relative to «neutral» cluster is expected to increase by a factor of 1,6 keeping all the variables constant. So it could be concluded that there exist more relative risk that female compared to male get in the third cluster compared to the second cluster than to the first cluster compared to the second cluster. We may assume that women tend to follow different types of diets that on the one hand help them to reduce weight but on the other hand reduce their overall conditions and health status. Thus, females might feel sick or unhealthy more often that result in the fact that relative risk rate of being in cluster 1 relative to cluster 2 is smaller than in cluster 3 relative to cluster 2 keeping all other variables constant. These results allow us to reject the hypothesis 3.

Conclusion

The aim of our research was to find out what variables affects the probability of getting into a particular health status cluster. While conducting the research, we detected four significant variables that play an important role in defining which type of lifestyle to choose. Also, we detected the following hypotheses. The first one states whether health status deteriorates with age, the second one tries to find out how weight affect health status, the third one deducted that women tend to be healthier than men, the forth one tries to explain how the education level makes influence on health, while the last one tries to determine whether the marital status is significant in leading a healthy lifestyle.

In our research we took the data which contains people aged from 14 to 40. The reason we specify the selection in such a way is due to the fact that after 40 people start having health problems that arise due to the age and might have nothing in common with the lifestyle patterns that the person has. The data grants evidence that people aged 26-30 provide the majority of answers to the questionnaire. We believe that people of that age are actively interested in their health and willing to find out ways staying healthy as they get older.

In our analysis, we firstly conducted factors that helped us to perform the clusters. We had four factors that were responsible for sports, bad habits, leisure time and health. After that we build three main clusters that reflect the type of a healthy lifestyle. Cluster number one stays for good health status, the second one was neutral and the third one was for the poor state of health. So then we build a multinomial regression model and get the following results.

We came to the conclusion that higher level of education does not helps people to stick to a healthy lifestyle that can be described as people that are more educated tend to do have jobs that does not require physical activities that negatively affects their lifestyle patterns. Also we have discovered that an increase in age does not play a negative role in human's health due to the fact that people tend to pay more attention to their health as they get older. What is more, it was concluded that men tend to lead healthier lifestyle than women as we assume that they are not influenced by different diet tendencies. These days women care mostly about numbers on weight rather than overall health status. Moreover, we deducted that an increase in weight does not provide significant effect on the health status of the person.

In addition, based on the results we received in the research we may say that hypothesis concerning marital status has not been rejected. We assumed that married people tend to lead a healthier lifestyle. However, while running the regression we discovered that this variable is not significant for cluster that describes poor state of health, whereas significant for the cluster that describes a good state of health.

Nowadays it seems essential that healthy lifestyle is a key to long and prosper life. Unfortunately, a large number of people tend to understand this idea but do not put it in practice. It is believed that a person has to spend a vast majority of his or her time doing sport and miss out on tasty nutrition. However, it is not always the case. If the person is willing to maintain healthy state of body, he or she does not have to be a diet expert or professional football player. One just needs to pay attention to what he or she eats and avoids sedentary lifestyle.

References

[1] Roshina Y.M.» Lifestyle in relation to health: how important is social inequality?», Research Article, 2015

[2] Kolosnycina M.G., Berdnikova A.N. «Overweight - how much and what to do with it?», Research Article, 2009

[3] Chan C.W., and Leung S.F. «Lifestyle Health Behaviors of Hong Kong Chinese: Results of a Cluster Analysis», Journal of Public Health 2015, Vol.27 (3), p. 293-302

[4] Chan M.F., Mok E., Wong T.K.S., Lee R.L.T., Fok M.S.M. «Investigating the health profiles of Hong Kong Chinese: results of a cluster analysis, Journal of Clinical Nursing 2006, p. 911-920

[5] Kim S., Symons M. and Popkin Barry M. «Contrasting Socioeconomic Profiles Related to Healthier Lifestyles in China and the United States», American Journal of Epidemiology, Vol.159 (2), p. 184-191

[6] Qi V., Phillips Susan P. and Hopman Wilma M. «Determinants of a healty lifestyle and use of preventive screening in Canada», Research Article

[7] Grossman M., «The Human Capital Model», Handbook of Health Economics, 2000, Vol(1).

[8] Cockerham W.C. «Health lifestyle theory and the convergence of agency and structure», The Journal of Health and Social Behavior, 2005, Vol.46 (1), p. 51-67.

[12] Lattin James M., Carroll J. Douglas, Green Paul E., «Analyzing Multivariate Data»

[13] Harman, H.H. Modern Factor Analysis, 3rd ed. Chicago: University of Chicago Press, 1976

[14] Mulaik, S.A. Foundations of Factor Analysis. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC, 2010

[15] Multivariative Course Page, Phil Ender

[16] Makles, A. Stata tip 110: How to get optimal k - means cluster solution. Stata Journal 12: p. 347-351, 2012

[17] Calinski, T., and J. Harabasz «A dendrite method for cluster analysis» Communication is Statistics, 1975, Vol 3 (1), p. 1-27

[18] Greene, W.H. Econometrician Analysis, 7th Ed. Upper Saddle River, NJ: Prentice Hall, 2012

[19] Christensen V.T., Capriano R.M. (2014) Social Class differences in BMI among Danish women^ Applying Cockerham's health lifestyles approach and Bourdieu's theory of lifestyle. // Social Science & Medicine. 2014, №112, p. 12-21

[20] Glorioso V., Pisati M. (2014) Socioeconomic inequality in health-related behavior: a lifestyle approach. // Quality & Quantity: International Journal of Methodology, 2014, V. 48 №5, p. 2859-2879

[21] Hsu H-Y & Gallinagh R (2001) The relationships between health beliefs and utilization of free health examinations in older people living in a community setting in Taiwan. Journal of Advanced Nursing 35, 864-873

[22] Shin Y (1999) The effects of a walking exercise program on physical function and emotional state of elderly Korean women. Public Health Nursing 16, 146-154.

[23] Tung WC & Gillett PA (2005) Stages of change for physical activity among family caregivers. Journal of Advanced Nursing49, 513-521.

[24] Poortinga Wouter,» The prevalence and clustering of four major lifestyle risk factors in an English adult population», Research Article, 2006

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