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Kanthawongs, Penjira; Jabutay, Felicito Angeles; Upalanala, Ruangrit; Kanthawongs, Penjuree – International Association for Development of the Information Society, 2016
Apart from Internet addiction, smartphone addiction has played important roles in students' lives as observed in recent studies. There are positive and negative aspects in using smartphones especially in higher education. University administrators or instructors should take into account what factors are affecting students' smartphone addiction in…
Descriptors: Handheld Devices, Telecommunications, Addictive Behavior, Foreign Countries
Kariuki, Patrick N.; Morris, Dustin A. – Online Submission, 2013
The purpose of this study was to determine the relationship between reading comprehension scores and conceptual mathematics scores of third grade students at a selected elementary school. The sample consisted of 27 students of which 15 were females and 12 were males. Data were collected using a teacher made conceptual math exam and the scores from…
Descriptors: Elementary School Mathematics, Numeracy, Reading Comprehension, Scores
Newman, Isadore; And Others – 1980
When investigating differences between two sets of scores, the t test is appropriate. If the two sets of data are from two groups of subjects, then the independent t test is appropriate. If the two sets are from the same subjects, the dependent t test is required. In this paper, the authors describe the use of a third test when part of a data set…
Descriptors: Hypothesis Testing, Mathematical Models, Multiple Regression Analysis, Research Design
Leitner, Dennis W. – 1979
Statistics such as chi-square, phi, and Cramer's V are related to the R squared statistic of regression analysis. It is shown that the proportion of variance accounted for can be computed from many contingency table situations. (JKS)
Descriptors: Expectancy Tables, Hypothesis Testing, Multiple Regression Analysis, Nonparametric Statistics
Peer reviewedLeitner, Dennis W. – Multiple Linear Regression Viewpoints, 1979
This paper relates common statistics from contingency table analysis to the more familiar R squared terminology in order to better understand the strength of the relation implied. The method of coding contingency tables was shown, as well as how R squared related to phi, V, and chi squared. (Author/CTM)
Descriptors: Correlation, Expectancy Tables, Hypothesis Testing, Multiple Regression Analysis
Wunderlich, Kenneth W.; Borich, Gary D. – 1974
Considerable thought, research, and concern has been expanded in an effort to determine whether the assumption of a quadratic relation between a single predictor and a criterion violated the assumptions which Johnson and Neyman (1936) state for calculating regions of significance about interacting regressions. In particular, there has been special…
Descriptors: Computer Programs, Educational Research, Hypothesis Testing, Mathematical Models
Hoedt, Kenneth C.; And Others – 1984
Using a Monte Carlo approach, comparison was made between traditional procedures and a multiple linear regression approach to test for differences between values of r sub 1 and r sub 2 when sample data were dependent and independent. For independent sample data, results from a z-test were compared to results from using multiple linear regression.…
Descriptors: Correlation, Hypothesis Testing, Monte Carlo Methods, Multiple Regression Analysis
Fish, Larry – 1986
A growing controversy surrounds the strict interpretation of statistical significance tests in social research. Statistical significance tests fail in particular to provide estimates for the stability of research results. Methods that do provide such estimates are known as invariance or cross-validation procedures. Invariance analysis is largely…
Descriptors: Correlation, Hypothesis Testing, Multiple Regression Analysis, Multivariate Analysis
Williams, John D. – 1976
The use of characteristic coding (dummy coding) is made in showing solutions to four multivariate problems using canonical analysis. The canonical variates can be themselves analyzed by the use of multiple linear regression. When the canonical variates are used as criteria in a multiple linear regression, the R2 values are equal to 0, where 0 is…
Descriptors: Analysis of Variance, Hypothesis Testing, Matrices, Multiple Regression Analysis
Monge, Peter R. – 1977
This paper examines the multivariate multiple regression model and explores its applicability to communication research. The first section discusses partitioning of a data matrix, as a heuristic device for distinguishing among alternative regression models. Section two examines various aspects of univariate multiple regression, including the form…
Descriptors: Communication (Thought Transfer), Educational Research, Error Patterns, Hypothesis Testing
Underwood, Benton J. – 1974
A proposal to formulate nomothetic theories -- general statements or scientific laws -- in a way to allow for an immediate test of individual differences is presented. The need for such a proposal is cited in recent developments in structural model building in the area of memory called information processing and in the concern to bring individual…
Descriptors: Educational Psychology, Hypothesis Testing, Individual Differences, Learning Processes
Watt, James H., Jr. – 1979
A relatively simple procedure for modeling periodic components in time series data is presented in this paper, along with an example of the procedure's use with communication data. Similar to multiple regression analysis, the described procedure has four steps that are based on information about periodic waves and their components, how to create…
Descriptors: Communication Research, Componential Analysis, Data Analysis, Evaluation Methods
Chu, Hsiang-Chi Julie – 1987
Variables and conditions that influence the learning of computer-presented expository text were investigated in this study, in which on-screen underlining was used as a learning aid to increase "mindful" processing of information. The effects of three intervening variables--amount of invested mental effort (AIME), learning style, and…
Descriptors: Aptitude Treatment Interaction, Cognitive Style, College Students, Computer Assisted Instruction


