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The aim of this study is to show complementary usage of logistic and correspondence analysis in a research subject to self-healing methodologies. Firstly, the number of the variables is reduced by logistic regression according to relationship between dependent and independent variables and then research carries on searching variables. The relationship among the behaviours of individuals and their demographic characteristics is modelled by logistic regression and shown graphically by correspondence analysis. In application, first of all, the effect of age, sex, marital status, education level, occupation and income level and present health condition, on appreciating self-health, is explained by a model. As a result of that model, it can be said that the effect of age, occupation and present health condition is reasonable. After analysing that model, the relationship between categorical variables (age, sex, occupation, preferred precautions, and worth of personal health) is shown graphically by multiple correspondence analysis.

One of the oldest instincts which the human being has is protection, especially against diseases. Protection is one of the basic parts of self healing process. Self healing is an extensive process which comprises many methods from non medical mixtures or applications to homemade medicines. Self healing process can be defined as curing oneself own medical problems without any professional support [

As in the many fields, especially medical science, it generally came across categorical variables. There are lots of techniques developed for multiple variable techniques for categorical data. Up to analysis type, those techniques may vary. It is sometimes difficult to selection variable for explaining relationship in self healing process. The multiple variable techniques are used as complementary in self healing survey to solve this problem.

The contribution of this study, complementary usage of multi-variable techniques may be at issue in the same research. For example, in some research, variables can be modeled first and then may have visual presentation. In some cases, there may be too many independent variables that explain dependent variable. In such researches firstly, the number of the variables is reduced and research carried on searching variables.

Logistic regression which is a technique for analysis of categorical variables allows us to classify variables as dependent and independent and models the relationship among the variables. Correspondence analysis is also another technique which we may use to handle categorical data and by which it is possible to analyze two or more categorical variable in a single step and to display the relationship graphically.

In this study, representation of complementary usage of which are widely used multivariate techniques, logistic regression and correspondence analysis in a research subject to self healing methodologies is aimed. Again in the study, the number of categorical variables which are considered as related with each other, is reduced and modeled using logistic regression, and then relationship between the related variables is presented with correspondence analysis graphically.

Logistic regression is summarized and correspondence analysis is summarized in followings. Factors about self healing subject in the application are first analyzed by logistic regression and correspondence analysis in the last section.

The use of logistic regression modeling has exploded during the past decade. Although it is firmly established within epidemiology research, the method is now commonly employed in many fields including but not nearly limited to biomedical research, business and finance, criminology, ecology, engineering, health policy, linguistic and wildlife biology [

Logistic regression is used to explain the relationship between the dependent variable and the independent variables, when the dependent variable is observed into two or more categories. The effects of independent variables the dependent variable is defined as probabilities.

Logistic regression’s purpose is to estimate parameters by creating logistic models. It is also possible to add common variables to the models and so corrected

There are three main methods in logistic regression analysis: Binary Logistic Regression Analysis: It is used for dependent variables that have binary values. Ordinal Logistic Regression Analysis: It is used for dependent variable which is ordinal. The observed values must be at least in three categories. Nominal Logistic Regression Analysis: This method is suitable when dependent variable is nominal. The observed values must be at least in three categories [

Correspondence analysis can be defined as the combination of mathematical and graphical techniques used for explanation of a contingency table [

For two-way contingency tables, simple correspondence analysis is used. For more than two-way contingency tables, multiple correspondence analysis applied [

Row and column marginal vectors can be written

For two dimensional contingency tables matrices of row and column profiles is shown by

[

[

where the columns of

The vectors_{,} are called the principals axes of the rows of

The total inertia can therefore be written as

which is the sum of the squares of singular values [

Multiple correspondence analysis tackles the more general problem of associations among a set of more than two categorical variables [

The study about self healing which is figured out in

First of all, frequencies about the individuals’ demographic characteristics are shown in

Demografic Variables | Categories | Individual Number | % (Percentage) |
---|---|---|---|

Sex | Male | 373 | 49.7 |

Female | 377 | 50.3 | |

Age | 15 - 25 | 250 | 33.3 |

26 - 35 | 170 | 22.7 | |

36 - 45 | 146 | 19.5 | |

46 - 55 | 97 | 12.9 | |

56+ | 87 | 11.6 | |

Marital Status | Single | 378 | 50.4 |

Married | 328 | 43.7 | |

Divorced | 9 | 1.2 | |

Widow | 35 | 4.7 | |

Education Level | Literate Without a Diploma | 13 | 1.7 |

Primary School | 79 | 10.5 | |

Junior-High School | 421 | 56.1 | |

University and master, doctorate | 237 | 31.6 | |

Occupation | Student | 100 | 13.3 |

Housewife-Non Employed | 79 | 10.5 | |

Officer | 150 | 20.0 | |

Employee | 155 | 20.7 | |

Tradesmen | 77 | 10.3 | |

Retired | 80 | 10.7 | |

Self-Employed Person | 89 | 11.9 | |

Other | 20 | 2.7 |

of the individuals agglomerated in the first two categories of the marital status as 50.4% single and 43.7% married. In the education level, this agglomeration showed itself as 56.1% Junior-High School and 31.6% university and master, doctorate. In occupation, this agglomeration differed from age, marital status and education level. Most of the agglomeration in occupation is in student, officer and employee categories as 20.7% employee, 20.0% officer and 13.3% student respectively.

Categories | Individual Number | % (Percentage) | |
---|---|---|---|

Attaching importance to own health | Much care | 241 | 32.1 |

Care | 338 | 45.1 | |

Less care | 130 | 17.3 | |

Do not care | 30 | 4.0 | |

Never care | 11 | 1.5 | |

Caring Physical Health | Yes | 596 | 79.5 |

No | 154 | 20.5 |

In this study, it has been used logistic regression and a complementary study, correspondence analysis, to show the relationship graphically. The relationship among the behaviors of individuals and their demographic characteristics was modeled by using logistic regression and correspondence analysis is applied as a complementary study.

In application, first of all, the effect of age, sex, marital status, education level, occupation and income level and present health condition, on appreciating self health, is explained by a model. After analyzing that model, the relationship between categorical variables (age, gender, marital status, education level, occupation, taking precaution, preferred precautions, and worth of personal health) is shown graphically by multiple correspondence analysis in the section followings.

The relationship among the variables ,which explain whether one takes precautions, gender, age, marital status, educational status, profession, income level and degree of caring physical health was examined by binary logistic regression since the question which supply the data is a yes - no question. One can find the results of the analysis in

As a result of this analysis, age, marital status and the attaching importance to own health are obtained as significant variables. Then we go ahead by multiple correspondence analysis which enables us to display the graphical representation of the variables.

According to

The relationship among the precaution types taken by 596 individuals to for keeping physical health, demographical variables and degree of caring physical health by logistic regression technique. Multi nominal logistic regression is applicable in this situation since the dependent variable is the type of the precaution (use medicine, well nutrition, high value nutrition, abstaining from detrimental materials, abstaining from bothering cases, other). The result of the analysis is seen in

Age, educational status and profession are enough to explain the variation of precaution type. If multiple correspondence analysis with the chosen variables is applied, the graphic in

Taking Precaution | % |
---|---|

Well Nutrition | 31.4 |

Using Protective Medicine | 25.2 |

To Abstain From Detrimental Materials | 21.3 |

Having High Potent Nutrient | 13.9 |

To Get Out Of Detrimental Conditions | 6.7 |

Other | 1.5 |

First Applied Method | % |
---|---|

Resting and Sleeping | 24.4 |

Having A Kind Of Medicine Present At Home | 23.5 |

Applying To A Doctor or Dentist | 22.9 |

Having Home-made Natural Medicine | 12.3 |

No Precaution | 8.5 |

Applying To A Pharmacist | 2.5 |

Having A Suggested Medicine | 1.5 |

Variable | B | Standard Error | Wald | df | Sig. | Exp (B) |
---|---|---|---|---|---|---|

Constant | −3.212 | 0.325 | 97.560 | 1 | 0.000 | 0.040 |

Age | −0.326 | 0.111 | 8.599 | 1 | 0.003 | 0.722 |

Marital | 0.456 | 0.165 | 7.600 | 1 | 0.006 | 1.578 |

Attaching Importance to Own Health | 0.840 | 0.107 | 61.576 | 1 | 0.000 | 2.316 |

Effect | −2 Log Likelihood of Reduced Model | Chi-Square | df | Sig. |
---|---|---|---|---|

Intercept | 1214.520 | 0.000 | 0 | |

Sex | 1222.999 | 8.478 | 5 | 0.132 |

Age | 1246.045 | 31.525 | 20 | 0.049 |

Marital | 1235.744 | 21.223 | 15 | 0.130 |

Education | 1243.165 | 28.644 | 15 | 0.018 |

Occupation | 1269.746 | 55.225 | 35 | 0.016 |

Income | 1250.036 | 35.516 | 30 | 0.224 |

Attaching Importance to Own Health | 1240.500 | 25.980 | 20 | 0.166 |

section. Workers of 36 - 45 age group are keeping away from and high nourished but it is not a matter for college students. People in 46 - 55, are having protective medicine, being well nourished and taking other precautions.

The relationship among the variables, which explain the methods preferred by people in case of illness, gender, age, marital status, educational status, profession, income level and degree of caring physical health, was examined by logistic regression. Multi nominal logistic regression is applicable in this situation since the dependent variable is the type of preferred method (No Precaution - Having Home-Made Natural Medicine - Having a Kind of Medicine Present at Home - Having a Suggested Medicine - Exercising - Resting and Sleeping - Applying to a Pharmacist - Applying to a Doctor or Dentist).

According to

If multiple correspondence analysis is applied, the graph in

Effect | −2 Log Likelihood of Reduced Model | Chi-Square | df | Sig. |
---|---|---|---|---|

Intercept | 1748.516 | 0.000 | 0 | |

Age | 1783.669 | 35.153 | 28 | 0.165 |

Gender | 1765.399 | 16.883 | 7 | 0.018 |

Marital | 1780.007 | 31.491 | 21 | 0.066 |

Education | 1765.540 | 17.023 | 21 | 0.710 |

Occupation | 1823.466 | 74.950 | 49 | 0.010 |

Income | 1780.691 | 32.175 | 35 | 0.605 |

Attaching Importance to Own Health | 1831.510 | 82.994 | 28 | 0.000 |

health up to graph. People who are working in the other job groups are not caring physical health and do nothing in case of an illness. Tradesmen are having a suggested medicine or applying to a pharmacist while students are resting or having a medicine which is already at home in case of an illness.

In many field, techniques for categorical variables are applied as a complementary study. In the study, correspondence analysis and logistic regression analysis are applied at the same research. As a first step, the variables which are explaining the variation of dependent variable are determined by logistic regression and then correspondence analysis applied to show the relation of variables in the model, graphically. These techniques are used in this way because while there are too many explanatory variables explaining dependent variable, it causes complexity. In this situation, by using logistic regression best explanatory variables that describe dependent variable are selected and are modeled. Finally, by using correspondence analysis, this relationship is graphically shown. The relationship is analyzed both correspondence analysis and logistic regression which is a technique that allow us to categorize the variables as independent and dependent variables. By this method, using both methods simultaneously, we got more detailed results for relationship. Thus, in this study we try to show to use correspondence analysis and logistic regression together in variable selection. A survey aimed at usage of self-healing methodologies is utilized to show how to use these techniques together.

The data of an inquiry, which was applied to 750 people health healing process, is analyzed in the study. First, whether taking precaution, type of precaution and type of method applied in case of an illness are determined as dependent variables. It is searched whether they are explained by demographic variables and degree of caring health. After that, determined variables and the other variables are analyzed by multiple correspondence analysis. It is seen that age is significant for dependent variables. It can be said that gender, marital status, education level and occupation are also significant. In addition, degree of caring physical health is considerable on dependent variables while income level is not significant.