Difference between revisions of "AY1516 T2 Inpatient Meals Survey ProjectOverview Methodology"
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[[AY1516_T2_Inpatient_Meals_Survey_ProjectOverview | <font color="#FFFFFF">PROJECT OVERVIEW</font>]] | [[AY1516_T2_Inpatient_Meals_Survey_ProjectOverview | <font color="#FFFFFF">PROJECT OVERVIEW</font>]] | ||
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==<div style="background: #848484; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px; font-size:24px"><font color= #FFFFFF>Current Method</font></div>== | ==<div style="background: #848484; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px; font-size:24px"><font color= #FFFFFF>Current Method</font></div>== | ||
<br/> | <br/> | ||
| + | MRC currently conducts these surveys using Qualtrics and then uses SPSS software to analyze the data. The main statistical measure used to analyze the data is the mean of the ratings given for each attribute. They drill down to analyze the average rating by wards, dietary preferences, types of patients and other dimensions. However, as discussed before in the report, this method is flawed and does not provide the best results. This is primarily because the distance between the ratings are not measurable and cannot be interpreted. | ||
| + | The mean can fall in a decimal and a mean of say 3.4 or 3.6 is not telling much of the story that the client will want to read. What can one make of the data when the mean comes to a decimal point between 3 and 4? Say for example the mean is 3.4, the report would say that the patients are satisfied on an average, but then again this is a mean what if this mean arrived due to a lot of people rating 5 and just a lesser number than that rating 1. Is the analysis correct then? No, because there are a lot of people who are not at all satisfied and their needs are overlooked. | ||
| + | Different graphs are generated and then insights are collaborated. Below are some screenshots of the analysis done by MRC on the survey data for October 2015 and November 2015.<br/><br/> | ||
| + | |||
| + | [[Image:DataStats-Oldgraph1.png|500px]]<br/> | ||
| + | Overall Satisfaction by Ward Class<br/><br/> | ||
| + | [[Image:DataStats-Oldgraph2.png|500px]]<br/> | ||
| + | Overall Satisfaction by Patient Type<br/><br/> | ||
| + | [[Image:DataStats-Oldgraph3.png|500px]]<br/> | ||
| + | Service Gap Analysis<br/><br/> | ||
| + | [[Image:DataStats-Oldgraph4.png|500px]]<br/> | ||
| + | Analysis by Patient Ward<br/><br/> | ||
| + | |||
| + | <i>Images source: MRC Mediacorp Oct 2015-Nov 2015 Survey analysis</i> | ||
==<div style="background: #848484; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px; font-size:24px"><font color= #FFFFFF>New Method</font></div>== | ==<div style="background: #848484; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px; font-size:24px"><font color= #FFFFFF>New Method</font></div>== | ||
<br/> | <br/> | ||
| + | The main objective is to provide the users with a dashboard which provides visualizations that appropriately represents the data and can be interpreted easily. The picture below shows the architecture diagram of the dashboard which was developed using NetBeans. | ||
| + | <br/> | ||
| + | <center> | ||
| + | [[Image:DataStats-architecture.png|700px]]<br/> | ||
| + | Architecture Diagram<br/><br/></center> | ||
| + | |||
| + | The dashboard was built using the MVC framework. The data was stored in flat files which could be accessed by all three layers of the application. The view layer is the layer which can be seen by users on the web application. Every time there is a user action the view page calls the controller which in turns updates the model. Once the model is updated, the users can see the results. | ||
| + | |||
| + | The picture below shows the technologies used by the application. After the midterm, we dropped the database as the data wasn't very heavy and using a database would just be a waste of resources. | ||
| + | <br/> | ||
| + | <br/> | ||
| + | <center> | ||
| + | [[Image:DataStats-technologies.png|700px]]<br/> | ||
| + | Technologies Used<br/><br/></center> | ||
| + | |||
| + | The dashboard provides upload functionality where the user can upload the survey data that he/she wants to analyze. This ensures that the dashboard is dynamic and can be used for analysing future surveys. The statistical measure used to represent the data is frequency of each rating for each factor as a percentage of the total population of interviewees. The basic graph shows the frequency of the ratings across each attribute for all patients using a divergent bar graph for both importance and satisfaction. As discussed earlier, this graph is easy to understand and interpret and provides an overall view of the ratings across each attribute. | ||
| + | |||
| + | In order to maintain the confidentiality of the data, the entire application is password protected. In the application the user has the option to select and view the overall divergent graph. Apart from the overall view, the dashboard lets the user to filter the data by 3 categories: | ||
| + | * Ward | ||
| + | * Patient Type | ||
| + | * Diet Type | ||
| + | <br/> | ||
| + | This helps the user to make different combinations of the categories and narrow down the main area where Sodexo should focus in order to improve their service. | ||
| + | |||
| + | Apart from the filters, users can drill down to specific factors and check the distribution of data by ward, patient type or diet type. This gives the user an in-depth analysis of the most important factors and figure out the reason for the feedback obtained for that factor. | ||
| + | |||
| + | All the above analysis is represented using Divergent bar graph which can be easily implemented by using D3.js. The dashboard is a simple, easy-to-use application which provides interactive graphs with appropriate filters for in-depth analysis. The graphs are easy to read and interpret and provide accurate representation of Likert scale data. | ||
| + | |||
| + | The picture below gives a snapshot of the different functionalities in the application and how they interact with each other. | ||
| + | <br/> | ||
| + | <center> | ||
| + | [[Image:DataStats-functionaliter.png|700px]]<br/> | ||
| + | Application Functionalities<br/><br/></center> | ||
Latest revision as of 02:59, 15 April 2016
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| Project Description | Data | Methodology |
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Current Method
MRC currently conducts these surveys using Qualtrics and then uses SPSS software to analyze the data. The main statistical measure used to analyze the data is the mean of the ratings given for each attribute. They drill down to analyze the average rating by wards, dietary preferences, types of patients and other dimensions. However, as discussed before in the report, this method is flawed and does not provide the best results. This is primarily because the distance between the ratings are not measurable and cannot be interpreted.
The mean can fall in a decimal and a mean of say 3.4 or 3.6 is not telling much of the story that the client will want to read. What can one make of the data when the mean comes to a decimal point between 3 and 4? Say for example the mean is 3.4, the report would say that the patients are satisfied on an average, but then again this is a mean what if this mean arrived due to a lot of people rating 5 and just a lesser number than that rating 1. Is the analysis correct then? No, because there are a lot of people who are not at all satisfied and their needs are overlooked.
Different graphs are generated and then insights are collaborated. Below are some screenshots of the analysis done by MRC on the survey data for October 2015 and November 2015.
Overall Satisfaction by Ward Class
Overall Satisfaction by Patient Type
Service Gap Analysis
Analysis by Patient Ward
Images source: MRC Mediacorp Oct 2015-Nov 2015 Survey analysis
New Method
The main objective is to provide the users with a dashboard which provides visualizations that appropriately represents the data and can be interpreted easily. The picture below shows the architecture diagram of the dashboard which was developed using NetBeans.
The dashboard was built using the MVC framework. The data was stored in flat files which could be accessed by all three layers of the application. The view layer is the layer which can be seen by users on the web application. Every time there is a user action the view page calls the controller which in turns updates the model. Once the model is updated, the users can see the results.
The picture below shows the technologies used by the application. After the midterm, we dropped the database as the data wasn't very heavy and using a database would just be a waste of resources.
The dashboard provides upload functionality where the user can upload the survey data that he/she wants to analyze. This ensures that the dashboard is dynamic and can be used for analysing future surveys. The statistical measure used to represent the data is frequency of each rating for each factor as a percentage of the total population of interviewees. The basic graph shows the frequency of the ratings across each attribute for all patients using a divergent bar graph for both importance and satisfaction. As discussed earlier, this graph is easy to understand and interpret and provides an overall view of the ratings across each attribute.
In order to maintain the confidentiality of the data, the entire application is password protected. In the application the user has the option to select and view the overall divergent graph. Apart from the overall view, the dashboard lets the user to filter the data by 3 categories:
- Ward
- Patient Type
- Diet Type
This helps the user to make different combinations of the categories and narrow down the main area where Sodexo should focus in order to improve their service.
Apart from the filters, users can drill down to specific factors and check the distribution of data by ward, patient type or diet type. This gives the user an in-depth analysis of the most important factors and figure out the reason for the feedback obtained for that factor.
All the above analysis is represented using Divergent bar graph which can be easily implemented by using D3.js. The dashboard is a simple, easy-to-use application which provides interactive graphs with appropriate filters for in-depth analysis. The graphs are easy to read and interpret and provide accurate representation of Likert scale data.
The picture below gives a snapshot of the different functionalities in the application and how they interact with each other.
