China
Africa
Explore chapters and articles related to this topic
Will the VAT-easing encourage willingness for car loans amid the pandemic?
Published in Siska Noviaristanti, Contemporary Research on Management and Business, 2023
This study conducted a pilot test using 50 data to test the validity and reliability of the questionnaire. The validity test was carried out using factor analysis, while the reliability test was conducted using Cronbach's alpha. The factor analysis results revealed that the risk perception construct is not valid. Thus, the risk perception construct is excluded from the model. Furthermore, the reliability test using Cronbach's alpha found the value of emotion (0.930), materialism (0.738), financial planning behavior (0.863), and propensity toward indebtedness (0.673). Accordingly, the variables are valid and reliable for further analysis. The analysis of variance (ANOVA) obtained an F-Value of 2.6783 with a p-value of 0.000. This result means that all variables (emotion, materialism, and financial planning behavior) simultaneously affected one's propensity toward indebtedness. Thus, a t-test in regression analysis was conducted to test the hypotheses of the study.
Bi-Iterative Optimized K-Nearest Neighbours Algorithm
Published in Durgesh Kumar Mishra, Nilanjan Dey, Bharat Singh Deora, Amit Joshi, ICT for Competitive Strategies, 2020
H. Bhavsar, A. Jivani, T. Bhatt, N. Patel, N. Shiledarbaxi
Also, fclassif has been used for Covertype dataset due to the negative values of the features. It is used to select quantitative features having highest ANOVA-F values from a set of attributes. Unlike chi2, it also accepts negative valued features. The ANOVA (Analysis of variance) test is a statistical technique to compare two kinds of variation, one between the sample means and the other within each sample under consideration. An ANOVA model can have complete set of features or any of its subset, but conventionally it includes complex interaction terms only if it also includes all the simpler terms for those factors [25].
Analysis of Investors’ Perceptions of Mutual Fund Investment in the Context of the Delhi/NCR Region
Published in Avinash K. Shrivastava, Rana Sudhir, Amiya Kumar Mohapatra, Mangey Ram, Advances in Management Research, 2019
Gurinder Singh, Vikas Garg, Shalini Srivastav
Data were classified, tabulated, and processed in an organized manner. ANOVA was used as a statistical tool. ANOVA stands for analysis of variance, which is a statistical tool for analyzing variances among and between the group data.
When communicative AIs are cooperative actors: a prisoner’s dilemma experiment on human–communicative artificial intelligence cooperation
Published in Behaviour & Information Technology, 2022
The sample size of this study was determined by presuming a medium effect size for 2 × 2 two-way analysis of variance (ANOVA), partial eta squared ()= .06. As suggested by Cohen (1992), assuming a medium effect size can achieve sufficient power in social science research (Cooper and Findley 1982; Sedlmeier and Gigerenzer 1989). Based on a priori power analysis G*Power 3.1 (Faul et al. 2009), number of conditions = 2 × 2 = 4, numerator df = (2−1) + (2−1) + (1 × 1) = 3, at α = .05, 1 − β = .80, the minimum sample size that can be sufficient to detect a medium effect (= .06) is 179. A previous study on human–AI interaction found that approximately 10% of the participants failed the manipulation check (Velez et al. 2019). However, it is difficult to estimate the number of excluded participants due to careless and invalid responses. By adopting a conservative approach to achieve sufficient power, this study recruited more participants that can be adequate to detect a medium effect. This study thus targeted to recruit 220 participants aged over 18 via Amazon Mechanical Turk (MTurk).
Chip sealing macro texture performance evaluation using split-plot repeated measures
Published in Road Materials and Pavement Design, 2021
Analysis of variance (ANOVA) is a statistical method that has been used in most studies to test differences between two or more means of different group(s). The normal-model based ANOVA analysis assumes independence, normality and homogeneity of the experimental units. The null hypothesis (H0) states that the means are equal, while the alternative hypothesis (HA) states that the related groups’ population means are not equal or at least one mean is different from at least one another. Four basic assumptions should be true when using ANOVA analysis (Akritas, 2015): Random errors are normally distributed,Random errors are independent,The expected values of the errors are zero, andThe variances of all errors are equal to each other.
Improving the carrying capacity of irrigation canals: Al-Tawfiky diversion
Published in Water Science, 2021
The linear stepwise regression analysis showed that the most effective parameters on the section factor and so as to conveyance were the deposition and scour areas. The less effective parameters were excluded from the equation. To determine whether there is any statistically significant difference between the independent data groups, the analysis of variance test (ANOVA) is applied. The result of ANOVA test is shown in Table 1. The high value of (F) and zero significance ensure that there is significant difference between data groups. The coefficient and significance of the different variables in the equation are listed in the Table 2. It was clear that the correlation between dependent variable (SFr) and independent ones (Asr, and Adr) is high which reached 77.5% and 82.7%, respectively, with significance value = 0.00 which mean strong relationship between variables.