Approaches to analysing datasets

 

Approaches to analyzing datasets


This topic explains various ways that you might choose to analyze and explore a dataset, starting with the simplest approach and then moving to more complex methods.

As datasets commonly contain survey data, this topic describes how you might approach analyzing a set of survey responses.

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Explore your survey data in Detail View

When you open a dataset in Detail View, you can visually explore the dataset. When you are working with the dataset in Detail View, you can:

You can also run queries to find and code at themes in your data:

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Gather responses to each question

Do you want to see how all respondents replied to a question? Gathering responses to each survey question at a node allows you to group the data into broad themes.

Using the example dataset below, you could create a node Question 1 and code the entire column at that node. You could create another node to contain all responses to Question 2.

Respondent Age Sex Question 1 Question 2
Anna 29 Female I think there should be more car-free zones Electric buses and taxis would help reduce pollution in the inner city
Jack 31 Male Pedestrians need to feel safe. There should be better lighting and more police We should create more green spaces
Maria 52 Female Safety barriers at busy intersections I don't think they should tax car parks
Peter 47 Male Better education in schools about road safety More street trees

You can code the column manually or automatically:

NOTE When each respondent is represented by multiple rows (a row per survey question), you can still use the Auto Code Dataset Wizard to gather the responses to a single question at a node—refer to Gather survey responses from multiple rows for more information.

Whichever method you use, you will create and code at the following nodes:

Once you have grouped all responses to a question at a single node, you can use some of NVivo's powerful analysis tools, including:

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Gather responses of each survey respondent

If your data contains classifying fields that describe your survey participants—for example, the name, age and sex of the participant—you can use these fields to create nodes that represent your survey participants. You can code everything a participant said in response to survey questions at the node that represents them.

Using the data below, for each respondent you would create one node, of the classification 'person', with attributes for Age and Sex. Responses to both Question 1 and Question 2 would be coded at this node.

Respondent Age Sex Question 1 Question 2
Anna 29 Female I think there should be more car-free zones Electric buses and taxis would help reduce pollution in the inner city
Jack 31 Male Pedestrians need to feel safe. There should be better lighting and more police We should create more green spaces
Maria 52 Female Safety barriers at busy intersections I don't think they should tax car parks
Peter 47 Male Better education in schools about road safety More street trees

Using the NVivo's automated tools, you can do this in two steps:

  1. Code content at nodes for each person

Use the Auto Code Dataset Wizard, selecting Code at nodes for each row on the first step of the wizard, to code the Question 1 and Question 2 responses at nodes representing the values from the 'Respondent' column. This creates the following nodes:

At this point, the nodes have coding, but are not classified, and have no attribute values.

  1. Use the Classify Nodes from Dataset Wizard to add the demographic information (age and sex) of each participant to their node.

Once you have created and coded responses at nodes for each respondent, you can use analysis tools which compare their attribute values. You can:

When you have gathered responses both at question nodes (Question 1, Question 2 ) and at respondent nodes (Anna, Jack, Maria, Peter), you can analyze what respondents in different demographic groups are saying in response to particular questions:

NOTE

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Grouping demographic values into ranges

When you use demographic information in the dataset to set attribute values for nodes, you can optionally group values into ranges.

For example, if your dataset contains the age of your respondents, it may be more useful to know that an individual participant is within the 21-29 age range, than to know their precise age.  

The Classify Nodes from Dataset Wizard allows you to the group values—refer to Classify nodes (set attribute values to record information)  for more information.

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Gather responses based on demographic values

Most commonly, you will use the classifying fields in your dataset to set the attribute values on your respondent nodes. However, it is also possible to create a node structure that reflects the demographic characteristics of your respondents—this provides another way of looking at your data.

Respondent Age Sex Question 1 Question 2
Anna 29 Female I think there should be more car-free zones Electric buses and taxis would help reduce pollution in the inner city
Jack 31 Male Pedestrians need to feel safe. There should be better lighting and more police We should create more green spaces
Maria 52 Female Safety barriers at busy intersections I don't think they should tax car parks
Peter 47 Male Better education in schools about road safety More street trees

Using the dataset above, you could use the Auto Code Dataset Wizard (select Code at nodes for each row on the first step of the wizard) to create nodes to represent the male and female respondents, and then child nodes for each age range (by grouping the age values into ranges)—responses to survey questions are coded at the appropriate age-range node.

Your resulting node structure (depending on how you group ages into ranges) might be:

This can be a quick way to gather responses by demographic groupings. You can see what males aged 30-39 are saying. If you want to see what all males are saying, turn on aggregation at the parent node (Male).

NOTE

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