Tablet VI: A Comparative Analysis
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Moro, P. Cortez, and P. Move the metric you want to investigate into the Analyze field. Move fields that you think might influence Rating into the Explain by field. You can move as many fields as you want. In this case, start with:. Leave the Expand by field empty. This field is only used when analyzing a measure or summarized field. To focus on the negative ratings, select Low in the What influences Rating to be drop-down box. The analysis runs on the table level of the field that's being analyzed.
In this case, it's the Rating metric. This metric is defined at a customer level. Each customer has given either a high score or a low score. All the explanatory factors must be defined at the customer level for the visual to make use of them. In the previous example, all of the explanatory factors have either a one-to-one or a many-to-one relationship with the metric. In this case, each customer assigned a single theme to their rating. Similarly, customers come from one country, have one membership type, and hold one role in their organization.
The explanatory factors are already attributes of a customer, and no transformations are needed. The visual can make immediate use of them. Later in the tutorial, you look at more complex examples that have one-to-many relationships. In those cases, the columns have to first be aggregated down to the customer level before you can run the analysis. Measures and aggregates used as explanatory factors are also evaluated at the table level of the Analyze metric. Some examples are shown later in this article. The customer in this example can have three roles: consumer, administrator, and publisher.
Being a consumer is the top factor that contributes to a low rating. More precisely, your consumers are 2. The key influencers chart lists Role in Org is consumer first in the list on the left. The comparative effect of each role on the likelihood of a low rating is shown. The key influencers visual compares and ranks factors from many different variables. The second influencer has nothing to do with Role in Org. Select the second influencer in the list, which is Theme is usability. Customers who commented about the usability of the product were 2.
Between the visuals, the average, which is shown by the red dotted line, changed from 5. The average is dynamic because it's based on the average of all other values. For the first influencer, the average excluded the customer role. For the second influencer, it excluded the usability theme. Select the Only show values that are influencers check box to filter by using only the influential values.
In this case, they're the roles that drive a low score. Every time you select a slicer, filter, or other visual on the canvas, the key influencers visual reruns its analysis on the new portion of data. For example, you can move Company Size into the report and use it as a slicer. Use it to see if the key influencers for your enterprise customers are different than the general population. An enterprise company size is larger than 50, employees. For large enterprise customers, the top influencer for low ratings has a theme related to security.
You might want to investigate further to see if there are specific security features your large customers are unhappy about. So far, you've seen how to use the visual to explore how different categorical fields influence low ratings. It's also possible to have continuous factors such as age, height, and price in the Explain by field. Tenure depicts how long a customer has used the service. As tenure increases, the likelihood of receiving a lower rating also increases. This trend suggests that the longer-term customers are more likely to give a negative score. This insight is interesting, and one that you might want to follow up on later.
The visualization shows that every time tenure goes up by In this case, So the insight you receive looks at how increasing tenure by a standard amount, which is the standard deviation of tenure, affects the likelihood of receiving a low rating. The scatter plot in the right pane plots the average percentage of low ratings for each value of tenure. It highlights the slope with a trend line. In some cases you may find that your continuous factors were automatically turned into categorical ones. This is because we realized the relationship between the variables is not linear and so we cannot describe the relationship as simply increasing or decreasing like we did in the example above.
We run correlation tests to determine how linear the influencer is with regard to the target. If the target is continuous, we run Pearson correlation and if the target is categorical, we run Point Biserial correlation tests. If we detect the relationship is not sufficiently linear, we conduct supervised binning and generate a maximum of five bins. To figure out which bins make the most sense, we use a supervised binning method that looks at the relationship between the explanatory factor and the target being analyzed.
You can use measures and aggregates as explanatory factors inside your analysis. For example, you might want to see what effect the count of customer support tickets or the average duration of an open ticket has on the score you receive. In this case, you want to see if the number of support tickets that a customer has influences the score they give. Now you bring in Support Ticket ID from the support ticket table. Because a customer can have multiple support tickets, you aggregate the ID to the customer level. Aggregation is important because the analysis runs on the customer level, so all drivers must be defined at that level of granularity. Let's look at the count of IDs. Each customer row has a count of support tickets associated with it. In this case, as the count of support tickets increases, the likelihood of the rating being low goes up 5.
The visual on the right shows the average number of support tickets by different Rating values evaluated at the customer level. You can use the Key influencers tab to assess each factor individually. You also can use the Top segments tab to see how a combination of factors affects the metric that you're analyzing. Top segments initially show an overview of all the segments that Power BI discovered. The following example shows that six segments were found. These segments are ranked by the percentage of low ratings within the segment. Segment 1, for example, has The higher the bubble, the higher the proportion of low ratings.
The size of the bubble represents how many customers are within the segment. Selecting a bubble displays the details of that segment. If you select Segment 1, for example, you find that it's made up of relatively established customers. They've been customers for over 29 months and have more than four support tickets. Finally, they're not publishers, so they're either consumers or administrators. In this group, The average customer gave a low rating It's 63 percentage points higher. Segment 1 also contains approximately 2. Sometimes an influencer can have a significant effect but represent little of the data. For example, Theme is usability is the second biggest influencer for low ratings. However, there might have only been a handful of customers who complained about usability.
Counts can help you prioritize which influencers you want to focus on. Anyone who has been exposed to the development of the field of public-administration education and training in Central and Eastern Europe will recognize and understand the importance of NISPAcee to the development of public-administration education and training in the region. I do hope to see even higher expectations and new achievements for NISPAcee: for the good of young people, for the good of our countries and for the good of the entire world. To my mind, the core NISPAcee activities of the very greatest value are: the annual conferences; other specialised conferences and workshops, mainly TEDs; the publications, especially the Journal; and the way in which relationships with partners from outside our region are maintained.
The first of these occurred in in Beijing and the most recent one took place in Guangzhou in IIAS and NISPAcee address the challenges of public administration in different geographic regions and environments, but both are striving to make a contribution for improving public administration; to create a forum where academics and practitioners meet and discuss the emerging problems to find common solutions. NISPAcee promoted the development of new, multidisciplinary public-policy and management teaching programmes and of new research projects. Updates to policies and guidance for QA agencies. Roundtable on Higher Education Data Interoperability. New accredited doctoral programme at University of Twente.
Greater involvement in higher education quality improvement discussed with stude. National Accreditation Bureau for Higher Education. Initial Accreditation of Study Programmes. Anonymous user Login Register. IV, No.
