### I. Introduction

*p*< 0.05). This study also reported that nicotine dependence was related to only short-term smoking cessation, not long-term maintenance. Song et al. [13] analyzed nicotine dependence of registrants for the smoking cessation programs conducted by community health centers and reported: as nicotine dependence is lower, the success rate of smoking cessation for six months is higher for both new registrants and re-enrollment registrants.

### II. Methods

### 1. Data Collection

### 2. Study Variables

### 3. Statistical Analysis

^{2}tests and the goodness-of-fit indices were considered. Among the various fit indices, incremental fit indices such as Comparative Fit Index (CFI), Normed Fit Index (NFI), Tucker-Lewis Index (TLI), and absolute indices such as Goodness Fir Index (GFI), Root Mean Square Error of Approximation (RMSEA) were used for the study [24-26].

^{2}tests which include many observed variables. The value of RMSEA should be less than 0.05 for a model that fits well. Values of 0.06 to 0.08 indicate mediocre fit, and values greater than 0.1 indicate poor fit [25-28].

### 4. Study Design

### III. Results

### 1. Descriptive Statistics and Correlation

### 2. ARCL Modeling between Nicotine Dependence and Average Smoking

Model 1: No constraint to the base model

Model 2: Homogeneity constraint model to the autoregressive coefficient (A) of nicotine dependence

Model 3: Homogeneity constraint model to the autoregressive coefficient (B) of average smoking

Model 4: Homogeneity constraint model to the cross-regression coefficient (C) of nicotine dependence

Model 5: Homogeneity constraint model to the cross-regression coefficient (D) of average smoking

Model 6: Homogeneity constraint model to the error coefficient (E) of nicotine dependence and average smoking

^{2}tests were viewed as appropriate for the analyses. However, to increase the reliability of the statistical results, other goodness-of-fit indices such as CFI, GFI, NFI, TLI, RMSEA were applied because χ

^{2}tests has been recognized as problematic when related to sample size sensitivity [21,30]. Thus, as applied herein, if the fit indices were not more adverse when any constraint was applied, it was concluded homogeneity was established.

### 3. Trajectory Changes of Nicotine Dependence and Average Smoking

^{2}. In case of average smoking, all except the values of χ

^{2}and RMSEA were suitable.

### 4. Predictors of Changes in Nicotine Dependence

^{2}(55.410,

*df*= 3) showed suitable fit (NFI = 0.952, CFI = 0.957, TLI = 0.909, RMSEA = 0.090) in the model.

*p*< 0.05). It showed a negative (-) effect on the slope (β = -0.183,

*p*< 0.01) as well. In case of the person who was successful in smoking cessation (1st), the intercept of nicotine dependence was low, and nicotine dependence was decreased over time. Success in smoking cessation (2nd) had a negative (-) effect on the slope of nicotine dependence (β = -0.194,

*p*< 0.05). The person who was successful in smoking cessation (2nd) also showed that nicotine dependence was decreased over time. The average smoking (1st) had a positive (+) effect on the intercept of nicotine dependence (β = -0.684,

*p*< 0.01) and a negative (-) effect on the slope (β = -0.425,

*p*< 0.01). Even though a person who had large amount of average smoking showed high nicotine dependence at the initial point measured, and nicotine dependence was decreased over time.