Contexts in source publication

Context 1
... predictor variables are confidence solving mathematics problems (confid), worry (worry), and finally mathematics intrinsic value (int.val). Possible relationships among variables are illustrated by Figure 1. ...
Context 2
... Venn diagram illustrating commonality analysis in Figure 1 serves as a model for the comparison of data examined in the present paper. The data was collected in a southeastern state and represents 287 sixth grade students' scores on three measures, the Space Relations portion of the Differential Aptitude Test (Bennett, Seashore, & Wesman, 1973), the Geometry Content Knowledge test (Carroll, 1998), and the Mathematics Attitude Scale (Gierl & Bisanz, 1997). ...
Context 3
... must return to the Venn diagram ( Figure 1) and then reconstruct it using the actual data from Table 2. This graphic helps one to visualize the relationships of the partitioned variance. ...

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... We found it creates more challenges than insight in commonality analysis, as the visualisations and interpretations become opaque when exploring that many predictors. For example, whereas a three-cue model produces seven variance components in the commonality analysis results, a four predictor model produces 15 components: four components representing unique variance, six representing common variance between two variable pairings, four representing common variance between three variable pairings and one that represents the variance common to all variables in the model (Capraro & Capraro, 2001). ...
... These results were visualized with EulerAPE (Micallef and Rodgers, 2014), which can be used to plot overlapping ellipses proportional to the variance partition of the total explained variance (Groen et al., 2018). Although in principle, negative variance can reflect informative relationships among predictors (Capraro and Capraro, 2001), in the present context these values were typically so small they are negligible (e.g., À0.1% of the total explained variance). They were therefore excluded from the visualization. ...
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... Also, canonical correlation values less than .30 are not interpreted as the variance explained by variable pairs is below 10% (Capraro & Capraro, 2001). Therefore, the findings of the first canonical variable pair were interpreted, and the findings of the second canonical variable pair were not interpreted. ...
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... These coefficients are analogous to beta weights in multiple regression. Importantly, discrepancies between these indicators are informative of multicollinearity and suppressor effects (Capraro & Capraro, 2001;Kuylen & Verhallen, 1981;Nimon et al., 2010). Thus, variables with high loadings but low weights may be indicative of multicollinearity, i.e., variance in these variables has been explained by other variables in the function coefficients. ...
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... In our study, we adopted bootstrap estimation (10,000 iterations) to calculate the standard error of each partition, and the percentile-based 95% two-tailed confidence intervals produced by the bootstrap estimation were used to determine the significance of each partition. It is worth noting that, unlike unique partitions, the common partitions may assume both positive and negative values, with the latter allowing us to recognize the presence of suppressor predictor variables (Pedzahur, 1997;Capraro and Capraro, 2001;Kraha et al., 2012). The commonality analysis was calculated using the R software (www.R-project.org) with the "yhat" package . ...
... Here, ''shared'' variance between predictors (overlapping regions in Figure 2) represent the variance those variables have in common with the dependent variable (Ray-Mukherjee et al., 2014). The presence of negative commonalities occurs when correlations among predictor variables have opposite signs (Pedhazur, 1997), or in the case that a variable confounds the explained variance of another variable in the model (Capraro & Capraro, 2001), such as a suppressor variable. Suppressor variables remove error variance in other predictors. ...
... Suppressor variables remove error variance in other predictors. As a result, the variable ''suppresses'' irrelevant variance and increases the predictive ability of the other predictor and regression model overall (Cohen & Cohen, 1983;Capraro & Capraro, 2001). Cue contributions. ...
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... This approach is different from other methods that are used to establishing complementarity. Commonality analysis provides information about the variance uniquely explained by each of the variables and their shared contribution to the outcome (Capraro and Capraro, 2001). Compared to regression, in which the performance effects from predictors are sequentially analyzed and each variable adds predictive power, commonality analysis involves a simultaneous estimation of the effects on a dependent variable and yields the explained variance that is shared between the variables. ...
Thesis
Strategic orientations, which reflect strategic behaviors implemented by a firm, have been widely associated with a firm’s competitiveness and performance. An extensive theoretical and empirical consideration has been received by entrepreneurial orientation, market orientation, and learning orientation, which reflect a firm’s behavior toward new product creation, market, and organizational learning, respectively. Extant literature has examined the relationship between strategic orientations and firm performance, yet research has often focused on one orientation in isolation without considering the orientations’ relative performance contributions and potential complementarities. Furthermore, previous results are ambiguous with regard to performance implications of strategic orientations, suggesting a closer examination of contextual effects. This dissertation aims to narrow these gaps by investigating how strategic orientations are individually and jointly related to firm performance in different environmental contexts. The study utilizes a quantitative methodology based on three datasets obtained from small and medium-sized enterprises from different countries. Complementarity of strategic orientations and their embeddedness in various country- and industry-level contexts are examined. The results suggest different individual effects of entrepreneurial, market, and learning orientations on firm performance, and highlight the significant role of their shared effect. By questioning complementarity of entrepreneurial and market orientations during an economic crisis and revealing distinctive effects of entrepreneurial orientation in developed and emerging markets, as well as across institutional and industry settings, the study emphasizes contextuality of strategic orientations–performance relationship. These results contribute to strategic orientations literature by applying an integrative approach to the phenomenon and specifying performance effects of strategic orientations in a wide variety of contexts. Moreover, the results have implications for research on complementarities in organizations as well as contextual research in management. The dissertation comprises an introductory part with an overview of the literature and discussion of the framing, methodology, results, and contributions of the whole study, as well as five independent publications.
... Commonality analysis (CA) was used to quantify the relative con- This method was developed for multiple regression analysis in the late 1960s and provides information regarding the variance explained by each of the measured variables and the common contribution from one or more of the other variables (Capraro & Capraro, 2001 (Table 3). and Exp2, respectively. ...
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