The brief data of the relation of living as commuters and quality of life

The dataset presented here is useful for identifying the relation of living as commuters and the quality of life. This dataset separates commuters into several groups according to their gender, marital status, educational background, and occupation to enrich the demography data and to give more insight. Data was collected from commuters who work in Medan, North Sumatra, Indonesia. Questionnaire-based survey with proportional random sampling has collected from 384 respondents by accidental sampling. We use statistic descriptive, median-test, Kruskal-Wallis, and spearman-rank correlation to analyze the data. The data shows there is any different response regarding the quality of life between gender, marital status, educational background, and occupation among commuters. The researchers found at least three dominant factors which make the differences, that is the time limitation for social interaction in the neighborhood, time limitation regarding quality interaction as parents, and time limitation for themselves to have quality time. However, the commuters have several reasons why they still survive in the situation. From ten factors proposed, there are only three factors which have significant relation with three dominant factors of commuters life problem which suggested previously. That is 1) Time Compliance with Income; 2) Income per Month, and 3) Fairness Travel Time to Work Location.


Data
The data proposed here resulted from surveying commuters who were working in Medan City while they live in another city around Medan. The previous research has shown that commuters life has several impacts on quality of life [1e4], and demography of the commuters also contribute to resulting the differences of commuters' perspective regarding their quality of life [1,3,5,6]. Therefore, we identify the perception of commuters regarding their quality of life and why they choose those circumstances. Then we separated the data refers to their valuable characteristics, such as gender, marital status, educational background, and occupation for better display of data. Table 1 presents the figure of quality of life regarding the demography of respondents. The author uses the median test and Kruskal-Wallis to identify the difference of responses of each group. It contains the signification number of differences in commuter perception regarding their quality of life according to gender, marital status, educational background, and occupation. The detail of the respondent's demography and its figure of responses are distributed in Table 2, which probable the readers to analyze what factors in demographics which may affect the quality of life among commuters. Furthermore, for advanced analysis, the author used rank-spearman correlation to identified the relationship between quality of life and the antecedents (Table 3). From Table 3, we can indicate there are four aspects which influencing sample to justify their quality of life, that is: work time compliance with income, income per month, ease of transportation mode, fairness travel time to work location. Specifications Table   Subject Economics and Econometrics/Geography, Planning and Development.

Specific subject area
Demography of Commuters, Commuters Life, Quality of Life.

Value of the Data
The data in this article displays a brief exploration regarding commuters preferences toward their quality of life. The dataset describes the gap of preferences between a sample which grouped in gender, marital status, educational background, and occupation. The data provide the fruitful result because the data figured heterogeneous demography background of the sample. The data is valuable for further research regarding quality of life, index of happiness, productivity, and welfare in the region or even as proxies of another area which have similar characteristic. For researchers who interested in public sector development, we present a dataset that is valuable for predicting how commuters define their quality of life and the reason for its definitions. The dataset can be further analyzed in the future using an advanced method of data analysis or even new data format. The figure of data is valuable for the reason of decision-making regarding public transportation investment, traffic regulation, and income regulation to control the index of happiness based on the quality of life.

Experimental design, materials and methods
The commuters in this data are someone who has work location in Medan City while they live in another city around Medan. Medan is the capital city of North Sumatera and the 4th largest city in Indonesia, which has growing industrial clusters. People who work in Medan, mostly, come from another region around Medan because of the high price of the property in Medan. Therefore, to do their job, they need to take daily commuting at least 60e90 minutes to arrive at their workplace. This data distributed the commuters into several groups based on their demography, such as gender, marital status, educational background, and occupation. Researchers try to provide valuable data refer to the explorable value that already exists on the characteristic of the sample. Moreover, we analyze the data from each factor of variables to gain the detail variation of the relationship between variables.
The data was collected using 5-Likert-scale questionnaire. The questionnaire was distributed to the enumerator who spread into 30-entrance way to Medan City from each region around. Enumerator uses proportional random sampling to collect the data and accidental sampling to selecting the sample [7]. From those methods, researchers have obtained 384, which ready to be analyzed. After the dataset was collected, it was tabulated by the primary statistic method, mean and standard deviation, to analyze the descriptive statistics. From the figure of the statistic descriptive, we can predict the pattern and tendencies of data based on the demography of the sample. The descriptive data is observable in Table 2.
Furthermore, researchers use a median test to analyze commuters' quality of life according to gender and Kruskal Wallis to analyze commuters' quality of life regarding marital status, educational background, and occupation [8]. In this stage, we exploring the differences of responses among commuters refers to their characteristics by observing z-score and chi-square value to justify the significance of differences (Table 1). In the final stage, we use Spearman's Rank correlation to produce a   correlation matrix between commuters' quality of life with its determinant factors, which can be observed in Table 3. Researchers use the methods to identify the relation between variable from each factor from both variables. The correlation data will show a detailed figure of the relationship, which is fruitful for interpretation. To support the accuracy of data analysis and practicality, we use IBM SPSS 22 to examine the dataset [9].