Kaiser-Meyer-Olkin Factor Analysis: A Quantitative Approach on Mobile Gaming Addiction using Random Forest Classifier

Costales, Jefferson A.; Catulay, Jeff Jojer Jones E.; Costales, Jeffrey; Bermudez, Noel

doi:10.1145/3546157.3546161

Cited by 11 publications

(10 citation statements)

References 9 publications

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“…This statistic is a measure of the ratio of variance between variables that are likely to share the variation. The lower the ratio, the more suitable the data will be for factor analysis [17].…”

Section: Exploratory Factor Analysismentioning

confidence: 99%

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Design and Validation of an Instrument to Measure E-governance through Factor Analysis

Páez Moreno,

Parra Fonseca,

Rios Incio

2024

Preprint

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(1) E-governance is defined as the application of electronic means in the interaction between government and citizens and government and business, as well as in internal government operations to simplify and improve democratic, governmental, and business aspects of governance. Thus, e-governance is built from a paradigmatic dimension such as e-democracy (relationship between government and citizens) and an operational one such as e-government. The objective was to design and validate an instrument to measure e-governance based on three factors: a) e-administration, b) e-services, and c) e-democracy; (2) Methods: Based on the level of importance given to each factor (sample of 2042 Latin American citizens), as well as the relationships between them, an analysis of the importance of each factor is carried out; (3) Results: After the confirmatory analysis, the definitive instrument with which e-governance can be measured by other researchers and future research is obtained, considering the three selection factors, namely: e-administration, e-services and e-democracy; (4) Conclusions: This research contributes to political science through the design and validation of an instrument consisting of 39 items that can be used to measure e-governance according to the dimensions proposed by the United Nations Educational, Scientific and Cultural Organization.

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Section: Exploratory Factor Analysismentioning

confidence: 99%

“…KMO values close to zero mean that there are large partial correlations compared to the sum of correlations. In other words, there are generalized correlations that pose a major problem for factor analysis [17].…”

Section: Exploratory Factor Analysismentioning

confidence: 99%

Design and Validation of an Instrument to Measure E-governance through Factor Analysis

Páez Moreno,

Parra Fonseca,

Rios Incio

2024

Preprint

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“…The sample is said to be appropriate if the KMO value ranges from 0.5 to 1.0 and vice versa if the KMO value is less than 0.5, then the sample is not representative of the population. The calculation of the KMO assumption test statistic (Kaiser Meyer Olkin) is defined in Equation (2) [17]. 𝑘 : number of clusters 𝑐 𝑖 : the set of all data points assigned to cluster i 𝜇 𝑖 : centroid of cluster i.…”

Section: Representative Samplementioning

confidence: 99%

Non Hierarchical K-Means Analysis to Clustering Priority Distribution of Fuel Subsidies in Indonesia

Astuti,

Guci,

Alim

et al. 2023

BAREKENG: J. Math. & App.

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The growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel. Therefore, there is a need for appropriate action from the government in determining related policies. K-Means multivariate cluster analysis is a non-hierarchical cluster method that is popularly used, one of which is used in Machine Learning algorithms, especially Unsupervised Learning. The purpose of this research is to clustering that are priority distribution of subsidies in Indonesia based on the characteristics formed. The data in this study consist of the percentage of poverty, the percentage of total transportation, the percentage of transportation use, and the percentage of area. Data were analyzed using multivariate cluster analysis with the K-Means method. Based on the research results, information was obtained that the data fulfilled a representative sample with value of KMO >50%. In addition, there are 4 optimal clusters which are the results of the calculation of the Elbow and Silhoutte methods, so 4 provincial clusters are formed with their respective characteristics. Cluster 1 is a province that is highly prioritized to receive fuel subsidies, Cluster 2 is a province that is not highly prioritized for fuel subsidies, Cluster 3 is a province that is prioritized to receive fuel subsidies, and Cluster 4 is a province that is not prioritized to receive fuel subsidies.

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“…In practical terms, the KMO measure assesses the degree to which the variables in a dataset are related to each other and whether they share enough common variance to justify the use of factor analysis. A higher KMO value indicates that the variables are more closely related to each other and that factor analysis is more likely to yield meaningful results (Costales, Catulay, Costales & Bermudez, 2022). 7.…”

Section: Data Analysis Results and Discussionmentioning

confidence: 99%

“…Modelis ir hipotezės patikrintos, tikrinant vidinį nuoseklumą, atliekant elementų analizę ir vertinant modelį naudojant SEM. Patikrintas imties adekvatumas -KMO (Kaiserio-Mejerio-Olkino matas), homoskedastiškumas (Bartleto testas: koreliacijos matrica nėra tapatybės matrica), bendruomeniškumas ir savosios vertės (Costales, Catulay, Costales, & Bermudez, 2022).…”

Section: Veiksnių Darančių įTaką Virtualios Komandos Sprendimų Priėmi...unclassified

Modeling the factors determining a virtual team’s decision-making

Majzoub¹

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Disertacija rengta 2017-2023 metais Vilniaus Gedimino technikos universitete. Vadovas prof. dr. Vida DAVIDAVIČIENĖ (Vilniaus Gedimino technikos universitetas, vadyba -S 003). Vilniaus Gedimino technikos universiteto Vadybos mokslo krypties disertacijos gynimo taryba: Pirmininkas prof. dr. Renata KORSAKIENĖ (Vilniaus Gedimino technikos universitetas, vadyba -S 003). Nariai: dr. Laima JESEVIČIŪTĖ-UFARTIENĖ (Vilniaus Gedimino technikos universitetas, vadyba -S 003), prof. habil. dr. Artūras KAKLAUSKAS (Vilniaus Gedimino technikos universitetas, vadyba -S 003), dr. Rudolph OOSTHUIZEN (Pretorijos universitetas, Pietų Afrikos Respublika, vadyba -S 003), prof. dr. Rimvydas SKYRIUS (Vilniaus universitetas, vadyba -S 003). Disertacija bus ginama viešame Vadybos mokslo krypties disertacijos gynimo tarybos posėdyje 2023 m. gegužės 17 d. 10 val. Vilniaus Gedimino technikos universiteto senato posėdžių salėje.

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Kaiser-Meyer-Olkin Factor Analysis: A Quantitative Approach on Mobile Gaming Addiction using Random Forest Classifier

Cited by 11 publications

References 9 publications

Design and Validation of an Instrument to Measure E-governance through Factor Analysis

Design and Validation of an Instrument to Measure E-governance through Factor Analysis

Non Hierarchical K-Means Analysis to Clustering Priority Distribution of Fuel Subsidies in Indonesia

Modeling the factors determining a virtual team’s decision-making

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