Proceedings of the 2009 northeastern recreation research symposium

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A COMPARISON OF TWO FOLLOW-UP ANALYSES AFTER MULTIPLE ANALYSIS OF VARIANCE, ANALYSIS OF VARIANCE, AND DESCRIPTIVE DISCRIMINANT ANALYSIS: A CASE STUDY OF THE PROGRAM EFFECTS ON EDUCATION-ABROAD PROGRAMS Alvin H. Yu St. Cloud State University hyu@stcloudstate.edu Garry Chick Th e Pennsylvania State University Abstract.—Th is study compared the utility of two diff erent post-hoc tests after detecting signifi cant diff erences within factors on multiple dependent variables using multivariate analysis of variance (MANOVA). We compared the univariate F test (the Scheff é method) to descriptive discriminant analysis (DDA) using an educational-tour survey of university study-abroad students. Diff erences on fi ve dependent measures of study-abroad program outcomes were compared by students’ academic major and in-country residency arrangements (residence hall, home stay, apartment). Univariate F test and DDA were used to detect group diff erences in the dependent variables after the overall signifi cant F test in MANOVA. Th e results indicated that the two post-hoc tests were congruent. However, DDA served better than the univariate F test to determine which outcome variables contributed most to separating the independent variables. We conclude that both post-hoc analyses should be utilized after a signifi cant MANOVA to obtain accurate and full insight into the data. 1.0 INTRODUCTION Multiple analysis of variance (MANOVA) is a statistical test that captures the eff ects of multiple independent variables on more than one dependent variable (Bray and Maxwell 1985). An important question in multivariate research is how best to examine and identify signifi cant diff erences, if any, of varying values of multiple independent variables on multiple dependent variables. The univariate F test and descriptive discriminant analysis (DDA) are two major post-hoc approaches utilized to identify the diff erences between groups (Tabachnick and Fidell 2007). In reviewing the literature, Kieff er et al. (2001) found that more than 80 percent of reviewed studies utilizing MANOVA also employed univariate F tests, such as the Scheff é and Tukey tests, as post-hoc analyses. Th is common approach to post-hoc analysis fails to consider the shared variance between dependent variables, as a series of univariate F tests examines group diff erences only individually on each dependent variable. In light of this discrepancy in the research literature, our primary concern is the eff ectiveness of post-hoc analysis for examining group diff erences following a signifi cant MANOVA. DDA, a multivariate post-hoc procedure, may serve as an alternative approach to identify group diff erences because it takes all dependent variables into account and follows MANOVA ethos. As Huberty and Smith (1982) suggest, researchers might need to “think multivariately” (p. 429) as they conduct multivariate studies. Specifi cally, DDA identifi es which variables contribute the most to separating predictor groups with one procedure, instead of individually evaluating a predictor group’s unique eff ect on each of the dependent variables. Th e purpose of this study was to compare the utility of the univariate F test with DDA using data from a survey of study-abroad students on an educational tour. Comparisons of the two post-hoc procedures were made after signifi cant results were found using MANOVA. 2.0 METHODS We used data from a survey of students in a study-abroad program to observe the diff erences between two post-hoc analyses after MANOVA. Specifi cally, we attempted to identify the relationships of independent predictors to the combined program eff ects as evidenced by examining the relationships with multiple dependent variables. In summer 2005, the Web-based survey collected data on participants’ perceptions of program eff ects on educational tours. Th e advantages of this survey program included lower publication costs (Nahm et al. 2004), convenient access (Schutt 2001), and an autonomous database system. Proceedings of the 2009 Northeastern Recreation Research Symposium GTR-NRS-P-66 235
Table 1.—Characteristics of participants in effectmodeling study Demographic Characteristics Majors, N=263 Frequency Percentage Art 28 11 Business 68 26 Liberal Arts 108 41 Science Residency options, N=265 59 22 Residency hall 104 39 Home stay 60 23 Apartment 101 38 In this example, 25 program eff ect items were generated from an intensive literature review and entered into an exploratory factor analysis. Th e fi ve factors extracted from this analysis served as dependent variables in MANOVA and subsequent post-hoc tests. MANOVA was used to test for signifi cant mean diff erences across the fi ve program-eff ect variables for individuals who varied in terms of two independent predictors: academic majors and residency arrangements. MANOVA is used to control the possibilities of Type I error infl ation as it examines the eff ects of independent variables on the dependent variables simultaneously instead of examining each dependent variable separately (Tabachnick and Fidell 2007). When signifi cant overall F-test values were identifi ed in each MANOVA, we compared the two post-hoc procedures, Scheff é method for univariate F (see Hair et al. 2006) and DDA, to contrast their diff erent approaches in determining the eff ects of group diff erences on the dependent variables. Unless otherwise noted, the criterion for statistical signifi cance was set at .05 for these analyses. 3.0 RESULTS Participating in this online study were 265 students, a sample size that provides adequate statistical power (Mertler and Vannata 2002). Participants’ academic majors were categorized as art, business, liberal arts, and science. Residency options were sorted into three categories: apartment, home stay, and residence hall. Table 1 illustrates sample characteristics. Most participants were liberal arts or business majors. Th e home-stay option was not very common as most students lived in apartments and residence halls during their stay abroad. A value of more than .60 levels in the Kaiser-Meyer- Olkin measurement and a signifi cant Bartlett’s test of sphericity suggested that this dataset was suitable for exploratory factor analysis (Tabachnick and Fidell 2007). Th e analysis produced a fi ve-factor solution that was evaluated on the basis of three criteria: Eigenvalues, loading values, and scree plot (Mertler andVannatta 2002). Specifi cally, an item was deleted before the next statistical test if any of the following conditions were met: item with Eigenvalue lower than 1, factor loading value lower than .45 (with 20 percent overlapping variance (Comrey and Lee 1992), or being outside of the sharply descending line in a scree plot. As for cutoff levels for loading values, Comrey and Lee (1992) suggest that 0.40 levels with about 25 percent overlapping variance are appropriate criteria for discriminating factor loadings. In this study, two items (i.e., “I discovered that local people have opinions that diff er from mine on some issues” and “I am more willing to interact with people with diff erent cultural backgrounds than I was before my trip abroad”) failed to meet the above criteria. Th ese items were deleted before the next statistical test, MANOVA. About 51 percent of the total variance was explained in this analysis. Th e fi ve factors served as scales measuring program outcomes for language learning, personal development, foreign connection, cultural immersion, and career development, which were each dependent variables for this study. Reliability analyses demonstrated that all scales met acceptable levels of reliability as Cronbach’s alpha was 0.6 or higher for each, and this study represented exploratory research (Robinson et al. 1991). Table 2 lists the items associated with each scale and the corresponding factor loading values and Cronbach’s alpha coeffi cients. MANOVA was conducted to determine diff erences in students’ majors with respect to the combined program outcomes. Because of its robustness, Pillai’s Trace was utilized as the test statistic because the Box’s M test showed that equal variances could not be assumed, M = 66.262, F(45, 42863.66) = 1.406, p
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United States Department of Agricul
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PROCEEDINGS OF THE 2009 NORTHEASTER
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OUTDOOR RECREATION—TRAILS Experie
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GIS APPLICATIONS AND RECREATION RES
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diff erent points along the spectru
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Proceedings of the 2009 Northeaster
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amount of bare rock. Camel’s Hump
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Larson, M. 2004. A Passive Approach
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THE ROLE OF IMAGINATION IN EXPERIEN
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Table 1.—Special places surveys L
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For some people, being in their spe
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CLIMATE CHANGE AND RESOURCE PLANNIN
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large geographical area (Fig. 1). T
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Divers seen = 4.4 Figure 2.—Norm
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Table 4.—Most-specialized divers
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Sarah Young Human Dimensions of Mar
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3.0 RESULTS AND DISCUSSION Th e FRR
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Table 4.—Divers’ perception of
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YOUTH AND OUTDOOR RECREATION
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A qualitative research design was u
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containers, instead of plastic bags
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WALK, RIDE AND LEARN: STUDENTS’ D
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school. Because of the large sample
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Table 8.—Observation of built str
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“I’M TOO OLD TO GO OUTSIDE!”
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Mean Eco-Affinity Score 5.0 4.5 4.0
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children. In: Klenosky, David B., F
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Cherie LeBlanc Fisher U.S Forest Se
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Table 1.—Comparative statistics f
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did some fi shing himself at each s
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On the other hand, some personal kn
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Illinois Department of Natural Reso
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Consumer behavior researchers have
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50 percent of the households were d
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y respondents indicated that these
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a bivariate approach (instead of th
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HERITAGE TOURISM DEVELOPMENT IN RUR
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Short cruise season. Th e river fre
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Lynda J. Sperazza, Ph.D. Recreation
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2.3 Leisure Participation Values Re
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Dychtwald, K. 1999. Age power: How
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With all research, the research que
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comprehensive. Cross (2005) even ar
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Stillwater, OK: Oklahoma State Univ
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EXPERIENCE USE HISTORY AND ITS RELA
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Table 1.—Comparison of experience
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5.0 DISCUSSION As mentioned previou
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HUMAN DIMENSIONS OF WILDLIFE
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2.0 METHODS Data were collected at
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0 buses 4 buses 8 buses 12 buses Fi
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4.0 DISCUSSION Visitor surveys and
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LEISURE AND HEALTH
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Second, in a world where clean drin
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Table 1.—Importance of different
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Countries also need to be careful a
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Proceedings of the 2009 Northeaster
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Table 1.—Informant profi les Info
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do something and we might think it
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Th ese relational interactions enab
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EXPLORING ELEMENTS THAT INFLUENCE S
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trails, and asking others to stay o
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time/fi nances” in dune protectio
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In summary, this study explored the
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Jinhee Jun Texas A&M University jju
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Table 1.—Sample demographics Indi
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Grayson 1995). Th e fi t of this mo
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Stets, J.E.; Burke, P.J. 2003. A so
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Jiaying Lu Texas A& M University Ji
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Table 1.—Demographic and particip
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Table 3.—Factor analysis of motiv
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species sought, satisfaction, and r
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social nature within sense of place
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Table 1.—Descriptions of key info
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sharing the place with others and a
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Manzo, L.C. 2005. For better or wor
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NORMATIVE TOLERANCES FOR SCUBA DIVE
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neutral point. Bubbles at the top o
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Table 1.—Snorkeler evaluations of
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Davis, D.; Tisdell, C. 1995. Recrea
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William Valliere University of Verm
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��
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Manning, R. 2007. Parks and carryin
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mission is “...to promote and reg
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Figure 1.—Pedestrian walkway LOS
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Acceptability 4 3 2 1 0 -1 -2 -3 -4
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TOURISM TRENDS AND CHALLENGES
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the rankings of 12 attendance motiv
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Table 3.—Comparison of socio-econ
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Table 6.—Entertainment Seekers’
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PLACE MEANINGS
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Stedman (1999) posited that sense o
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points in a focus group session, Ne
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are not shared by current residents
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Ishwar Dhami Division of Forestry a
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Figure 2.—Model for the calculati
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Page 253 and 254: 6.0 CITATIONS Della Bitta, A.; Lond
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Page 281 and 282: Th e importance of “getting away
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