Examples of attribute in the following topics:
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- Attribution theory explores how individuals attribute, or explain, the causes of their own and others' behaviors.
- To do this, we make either explanatory or interpersonal attributions.
- Attributions can also be classified as either internal or external.
- Internal attributions emphasize dispositional or personality-based explanations, while external attributions emphasize situational factors.
- Research shows that culture affects how people make attributions.
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- Knowing learners' attributional beliefs can help instructors to address the value of effort.
- These three categories of attribution are:
- From a review of attributional theories, Pintrich and Schunk (1996) generated a model to present the attributional process.
- The overview of the general attributional model can help in gaining an understanding of the attributional process.
- Attributional processes.
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- We then used Properties>nodes>color>attribute-based to select the government attribute, and assign the color red to government organizations, and blue to non-government organizations.
- You could also create an attribute data file in UCINET using the same nodes as the network data file, and creating one or more columns of attributes.
- NetDraw>File>Open>UCInet dataset>Attribute data can then be used to open the attributes, along with the network, in NetDraw.
- ), and then enter this information into the attribute editor of NetDraw (Transform>node attribute editor>edit>add column).
- Once these quantities are computed, they can be added to NetDraw (Transform>node attribute editor>edit>add column), and then added to the graph (Properties>nodes>size>attribute-based).
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- The "bigger picture" is to think about network data (and any other, for that matter) as having "structure. " Once you begin to see data in this way, you can begin to better imagine the creative possibilities: for example, treating actor-by-attribute data as actor-by-actor, or treating it as attribute-by-attribute.
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- The basic question of bivariate descriptive statistics applied to variables is whether scores on one attribute align (co-vary, correlate) with scores on another attribute, when compared across cases.
- Three of the most common tools for bivariate analysis of attributes can also be applied to the bivariate analysis of relations:
- This kind of question is analogous to the test for the difference between means in paired or repeated-measures attribute analysis.
- This kind of question is analogous to the correlation between the scores on two variables in attribute analysis.
- This kind of question is analogous to the regression of one variable on another in attribute analysis.
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- Successfully positioning products on a global scale requires marketers to determine the target market's preferred combination of attributes.
- Consequently, brands competing in the global marketplace often conduct extensive research to accurately define the market, as well as the attributes that define the product's potential environment.
- Successfully positioning products on a global scale also requires marketers to determine each product's current location in the product space, as well as the target market's preferred combination of attributes.
- These attributes span the range of the marketing mix, including price, promotion, distribution, packaging and competition.
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- This question relates an attribute (gender) to a measure of the actor's position in a network (between-ness centrality).
- We might even be interested in the relationship between two individual attributes among a set of actors who are connected in a network.
- These variables may be either non-relational attributes (like gender), or variables that describe some aspect of an individual's relational position (like between-ness).
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- Models like these are very useful for examining the relationships among relational and non-relational attributes of individuals.
- One of the most distinctive ways in which statistical analysis has been applied to social network data is to focus on predicting the relations of actors, rather than their attributes.
- In many sociological theories, two actors who share some attribute are predicted to be more likely to form social ties than two actors who do not.
- Two actors who are closer to one in a network are often hypothesized to be more likely to form ties; two actors who share attributes are likely to be at closer distances to one another in networks.
- Rather than using attributes or closeness as predictors, however, the P1 model focuses on basic network properties of each actor and the network as a whole (in-degree, out-degree, global reciprocity).
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- In this chapter we've taken a look at some of the most basic and common approaches to applying statistical analysis to the attributes of actors embedded in networks, the relations among these actors, and the similarities between multiple relational networks connecting the same actors.
- These tools allow us to examine hypotheses about the relational and non-relational attributes of actors, and to draw correct inferences about relations between variables when the observations (actors) are not independent.
- And, we've taken a look at a variety of approaches that relate attributes of actors to their positions in networks.
- Much of the focus here is on how attributes may pattern relations (e.g. homophily), or how network closeness of distance may affect similarity of attributes (or vice versa).
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- There are two ways to get a browser to jump to a specific location within a web page: named anchors and id attributes.
- {/a}), but with a "name" attribute:
- Both named anchors and id attributes are used in the same way.
- Virtually all browsers support named anchors; most modern browsers support the id attribute.
- To help them do this, add a title attribute to the same element(s) where you added the "name" and/or "id" attribute, for example: