A Novel Application of Multiple Regression Analysis Models to Internet Addiction

2016 ◽  
Author(s):  
Special Issues Editor
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Internet Addiction ◽  
Analysis Models

Adiksi Internet pada Remaja

2019 ◽  
Vol 7 (1) ◽  
pp. 72-78
Author(s):  
Ismalia Prambayu ◽  
Mulia Sari Dewi
Keyword(s):  
Regression Analysis ◽  
Social Skills ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Parenting Style ◽  
Internet Addiction ◽  
Psychological Factors ◽  
Social Skill ◽  
Analysis Method ◽  
Loneliness Scale

AbstractInternet addiction has become a worrying phenomenon for Indonesian teenagers. This research was conducted to determine whether the psychological factors will influence internet addiction in adolescents. This research uses quantitative with multiple regression analysis method. The winning sample is 200 adolescents. The instrument collects data using a scale internet addiction scale that compiled by Griffiths (2005) and developed by Lemmens (2009), Parenting Authority Questionnaire (PAQ) developed by Buri (1991), Social Skill Inventory (SSI) developed by Riggio (1986), and A Rasch-Type Loneliness Scale compiled by De Jong Gierveld (2006).  The results showed that there were significant differences in the parenting style, social skills, and loneliness on the tendency of internet addiction in adolescents.AbstrakAdiksi Internet menjadi salah satu fenomena yang mengkhawatirkan untuk remaja Indonesia. Penelitian ini dilakukan untuk mengetahui faktor psikologis apakah yang memberikan pengaruh terhadap kecenderungan adiksi internet pada remaja. Sampel pada penelitian ini berjumlah 200 remaja dengan menggunakan metode analisis kuantitatif. Penelitian ini menggunakan alat ukur sebagai berikut, alat ukur adiksi internet yang dikembangkan oleh Lemmens (2009), Parenting Authority Questionnaire (PAQ) yang dikembangkan oleh Buri (1991), Social Skill Inventory (SSI) yang dikembangkan oleh Riggio (1986), dan A Rasch-Type Loneliness Scale yang disusun oleh De Jong Gierveld (2006). Berdasarkan hasil pengujian ditemukan pengaruh signifikan gaya pengasuhan, keterampilan sosial, dan kesepian terhadap kecenderungan adiksi internet pada remaja.

A Study of Internet Addiction and Its Effects on the Mental Health of Medical Students: A Cross-Sectional Study in Western Iran

2019 ◽  
Author(s):  
Javad Yoosefi Lebni ◽  
Razie Toghroli ◽  
Manoj Sharma ◽  
Behzad Mahaki ◽  
Hadi Darvishi Gilan ◽  
...  
Keyword(s):  
Mental Health ◽  
Regression Analysis ◽  
Academic Success ◽  
University Students ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Internet Addiction ◽  
Pearson Correlation ◽  
The Internet ◽  
Statistical Population

Abstract Background: The internet itself is a harmless technology. However, its overuse and misuse will expose its users to internet addiction. On the other hand, diagnosis of students’mental dysfunction is of prime importance, and their progress and academic success in the future can be prevented by this technology if not properly handled. Methods: In this descriptive and correlational study, the statistical population consisted of all students at Kermanshah University of Medical Sciences, of which 447 students studying in the first and second semesters of the academic year 2017-18 were selected through Cochran's Sample Size Formula and Stratified Random Sampling. As for data collection, the Young's Internet addiction Test (IAT) and Goldberg General Health Questionnaire (GHQ-28) were utilized. Moreover, for data analysis, the descriptive statistics (percentage, mean, standard deviation) and inferential statistics (T-test, Pearson correlation coefficient, and multiple regression analysis) were employed in the environment of SPSS Statistical Software Version 23.0. Results: The results of the present study demonstrated that the total mean score of students’ internet addiction and mental health measured 3.81±0.88 and 2.56±0.33, respectively. The results also indicated that internet addiction correlared positively with depression whereas negatively with mental health (P<0.001). Besides, the results of multiple regression analysis revealed that the five major predictors of vulnerability to internet addictionin university students were as follows: the key reason for using the internet, faculty, depression, the main place for using the internet, and somatic symptoms. Conclusions: According to the findings of the present study, it can be concluded that students’ excessive use of the internet leads to depression, anxiety, and reduced mental health, thereby affecting their academic performance. Hence, it is suggested that further monitoring and control be exercised on how the internet is used by university students, and they should be informed of the detrimental effects of this technology in the case of misuse or overuse.

Enhancing project performance by developing multiple regression analysis and risk analysis models for interface

2014 ◽  
Vol 41 (11) ◽  
pp. 929-944 ◽  
Author(s):  
Nesreen Weshah ◽  
Wael El-Ghandour ◽  
Lynne Cowe Falls ◽  
George Jergeas
Keyword(s):  
Regression Analysis ◽  
Risk Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Performance Indicators ◽  
Project Performance ◽  
Interface Problems ◽  
Interface Problem ◽  
Second Phase ◽  
Analysis Models

Interface management (IM) is a main factor in the success of construction projects. The failure to correctly manage interfaces impacts a project’s performance measurements, such as scope control and schedule. Using Alberta’s data, collected using a web questionnaire from a large group of experienced industry experts, three phases are conducted in this research. The first identifies the top ten interface problems that affect IM. The second phase includes enhancing project performance by developing and applying multiple regression analysis models between the underlying interface problem factors and the project performance indicators. The last phase includes measuring the severity of the impact of each IM problem to develop an IM risk analysis model. The results of the multiple regression models indicate that the interface problems caused by the “technical engineering and site issues factor”, the “bidding and contracting factor”, and the “information factor” were the strongest influences on the schedule and cost project performance indicators. The results will assist engineers, architects, and others in analyzing and predicting the project performance. This will in turn serve to minimize project delay and cost and reduce conflict among project participants.

Pengaruh Citra Merek, Kualitas Produk Dan Harga Terhadap Kepuasan Konsumen Pada Bisnis Franchise Minuman

2017 ◽  
Vol 24 (2) ◽  
pp. 144-153
Author(s):  
Yunita Fitri Wahyuningtyas
Keyword(s):  
Regression Analysis ◽  
Marketing Strategy ◽  
Product Quality ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Sampling Method ◽  
Sampling Technique ◽  
Sampling Process ◽  
And Function ◽  
Simultaneous Influence

This research is conducted upon the emergence of many companies producing the same product of the same kind and function. It leads to the urgency of proper and well planned marketing strategy. This research aims to investigate how far the influence of branding, product quality, and price toward consumer’s satisfaction in beverage franchise business. This research utilizes 5 likert scale questionnaire which is tested by using multiple regression analysis to reveal whether or not there is partial and simultaneous influence of branding, product quality, and price toward consumer’s satisfaction in beverage franchise business. Sampling method is accidental sampling technique, in which sample of particular population is taken based on the accessibility and availability of the sample during the sampling process. Sample used is 100 samples among consumers or customers of Mang Endy Milkshake. The result shows that branding, product quality, and price influence consumer’s satisfaction in beverage franchise business.

Significance of Experience, Farm Size, Quantity of Propagules and Location f or Seaweed Farmers in the Divided Island of Sebatik

2018 ◽  
Vol 9 (9) ◽  
pp. 825-832
Author(s):  
James M. Alin ◽  
◽  
Datu Razali Datu Eranza ◽  
Arsiah Bahron ◽  
◽  
...  
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Production Function ◽  
Weather Conditions ◽  
Education Level ◽  
Farm Size ◽  
Total Production ◽  
Explanatory Factors ◽  
Douglas Production Function

Seaweed-Kappaphycus-Euchema Cottonii and Denticulum species was first cultivated at Sabah side of Sebatik in 2009. By November 2014, sixty one Sabahan seaweed farmers cultivated 122 ha or 3,050 long lines. Thirty Sabahan seaweed farmers in Kampung Pendekar (3.2 m.t dried) and 31 in Burst Point (12.5 m.t dried) produced 16 metric tonnes of dried seaweed contributed 31% to Tawau’s total production (51 m.t). The remaining 69% were from farmers in Cowie Bay that separates Sebatik from municipality of Tawau. Indonesian in Desa Setabu, Sebatik started in 2008. However, the number of Indonesian seaweed farmers, their cultivated areas and production (as well as quality) in Sebatik increased many times higher and faster than the Sabah side of Sebatik. In 2009 more than 1,401 households in Kabupaten Nunukan (including Sebatik) cultivated over 700 ha and have produced 55,098.95 and 116, 73 m.t dried seaweed in 2010 and 2011 respectively. There is a divergence in productions from farming the sea off the same island under similar weather conditions. Which of the eight explanatory factors were affecting production of seaweeds in Sebatik? Using Cobb Douglas production function, Multiple Regression analysis was conducted on 100 samples (50 Sabahan and 50 Indonesian). Results; Variable significant at α = 0.05% are Experience in farming whereas Farm size; Quantity of propagules and Location — Dummy are the variables significant at α 0.01%. Not significant are variables Fuel; Age; Number of family members involved in farming and Education level.

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