Factor analysis and counseling research.

Weiss, David J.

doi:10.1037/h0029894

Cited by 66 publications

(33 citation statements)

References 24 publications

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“…After these deletions, an exploratory factor analysis of the remaining items in each category was conducted to assess the unidimensionality of the retained items for each group and, where appropriate, eliminate items that were not factorially pure (Weiss 1970). We tried to avoid the use of imprecise and ambiguous terms to label the factors (Bagozzi 1981).…”

Section: Phasementioning

confidence: 99%

Measuring Factors that Influence the Success of Internet Commerce

Torkzadeh

Dhillon

2002

Information Systems Research

424

253

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E fforts to develop measures of Internet commerce success have been hampered by (1) the rapid development and use of Internet technologies and (2) the lack of conceptual bases necessary to develop success measures. In a recent study, Keeney (1999) proposed two sets of variables labeled as means objectives and fundamental objectives that influence Internet shopping. Means objectives, he argues, help businesses achieve what is important for their customersfundamental objectives. Based on Keeney's work, this paper describes the development of two instruments that together measure the factors that influence Internet commerce success. One instrument measures the means objectives that influence online purchase (e.g., Internet vendor trust) and the other measures the fundamental objectives that customers perceive to be important for Internet commerce (e.g., Internet product value). In phase one of the instrument development process, we generated 125 items for means and fundamental objectives. Using a sample of 199 responses by individuals with Internet shopping experience, these constructs were examined for reliability and validity. The Phase 1 results suggested a 4-factor, 21-item instrument to measure means objectives and a 4-factor, 17-item instrument to measure fundamental objectives. In Phase 2 of the instrument development process, we gathered a sample of 421 responses to further explore the 2 instruments. With minor modifications, the Phase 2 data support the 2 models. The Phase 2 results suggest a 5-factor, 21-item instrument that measures means objectives in terms of Internet product choice, online payment, Internet vendor trust, shopping travel, and Internet shipping errors. Results also suggest a 4-factor, 16-item instrument that measures fundamental objectives in terms of Internet shopping convenience, Internet ecology, Internet customer relation, and Internet product value. Evidence of reliability and discriminant, construct, and content validity is presented for the hypothesized measurement models. The paper concludes with discussions on the usefulness of these measures and future research ideas.

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Section: Phasementioning

confidence: 99%

Measuring Factors that Influence the Success of Internet Commerce

Torkzadeh

Dhillon

2002

Information Systems Research

424

253

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“…Content validity was addressed by drawing representative questions from a universal pool (Kerlinger, 1978). Discriminant validity was addressed by examining how the Weiss, 1970). Ninety nine of the 125 students responded to our questionnaire (79%).…”

Section: Resultsmentioning

confidence: 99%

Sociocultural Learning in Globally Distributed Teams: An Exploratory Study

Vogel¹,

Davison²,

Shroff³

et al. 2001

InSITE Conference

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“…Results from the Myers-Briggs Type Indcator, Gregorc Style Delmeator, examination scores from the course, and over-all grade point average were analyzed by a principal component factor analysis (14) using a varimax rotation, which provides the widest resolution for the factor analysis (17). This analysis was used to isolate the variables in the analysis that tended to cluster together.…”

Section: Statzstzcal Analyszsmentioning

confidence: 99%

“…This analysis was used to isolate the variables in the analysis that tended to cluster together. Factor loadings greater than f 0.5 were considered significant (17).…”

Section: Statzstzcal Analyszsmentioning

confidence: 99%

Relationship between Myers-Briggs Type Indicator and Gregorc Style Delineator

et al. 1996

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The relationship between the Myers-briggs Type Indicator and Gregorc Style Delineator, and achievement was examined by administering these instruments to 259 first-year nursing students enrolled in an introductory human anatomy and physiology course. A principal component factor analysis using a varimax rotation of the scores from the two psychometric instruments, achievement examinations and an over-all grade point average indicated that each learning style from the Gregorc Style Delineator corresponds to certain traits on the Myers-Briggs Type Indicator. An individual who had a preference for the learning style of Concrete Sequential tended to have the traits of sensing and judging on the Myers-Briggs Type Indicator, while an individual who used the learning style of Concrete Random tended to have the traits of intuition and perceiving on the Myers-Briggs Type Indicator. One who had a preference for the learning style of Abstract Sequential tended to use the trait of thinking while another who used the learning style of Abstract Random tended to have the trait of feeling. The factor analysis also indicates no relationship of any scores of the traits on the Myers-Briggs Type Indicator or learning styles of the Gregorc Style Delineator with the examination scores achieved in the human anatomy and physiology course or to the students' over-all grade point average. However, factor analysis indicates that the Myers-Briggs Type Indicator traits of Judging vs Perceiving collapsed into the Sensing vs Intuition scale, and that the Gregorc Style Delineator consists of two bipolar scales that are different from those proposed by Gregorc.

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Factor analysis and counseling research.

Abstract: This issue initiates a section of reviews of methodological problems in counseling psychology research-interpretations of current research trends and techniques to help counseling psychologists utilize developments in psychometrics, statistics, research design, and related areas.

Cited by 66 publications

References 24 publications

Measuring Factors that Influence the Success of Internet Commerce

Measuring Factors that Influence the Success of Internet Commerce

Sociocultural Learning in Globally Distributed Teams: An Exploratory Study

Relationship between Myers-Briggs Type Indicator and Gregorc Style Delineator

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