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QDA Miner User’s Manual 171

Content Analysis

WordStat is a quantitative content analysis add-on module that can be used to analyze words and phrases

found in documents. When used with QDA Miner, WordStat can perform these analyses on entire

documents or on selected code segments. It may also perform simple descriptive analysis or let you

explore potential relationships between words, phrases or categories of words and other numeric or

categorical variables.

There are many benefits to combining qualitative and quantitative content analysis techniques. For

example, quantitative content analysis may be useful as an exploration tool prior to qualitative coding by

allowing one to identify subtle differences in word usage between subgroups of individuals, or to quickly

find the most common topics of phrases. Restricting the analysis to segments associated with specific

codes may also be useful to identify potential words or phrases associated with those codes. You may

then use the QDA Miner text retrieval tool to identify other segments to which this code may be

assigned. Quantitative content analysis may also be useful after qualitative coding has been performed.

For example, you may try to validate the conclusions drawn from manual coding by comparing those

conclusions with the results obtained from a quantitative content analysis. Qualitative codings may also

serve as the starting material to develop and validate a categorization dictionary that will allow automatic

categorization of documents or cases.

To perform a quantitative content analysis on documents in a project, select the CONTENT ANALYSIS

command from the ANALYSIS menu. The following dialog box will appear:

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172 QDA Miner User’s Manual

VARIABLES - This option allows you to specify on which document variables the analysis will be

performed. If the current project contains more than one document variable, you will have a

choiceof selecting either one or more of them. By default, all document variables are selected. To

restrict the analysis to a few of them, click the down arrow key at the right of the list box. A list of

all available document variables will appear. Select all variables that you want to analyze.

By default, text analysis is performed on the entire document (All Text option). However, it is

also possible to restrict the analysis to specific coded segments or exclude segments from the

analysis by enabling the Coded Segments option. Set the list box immediately to the right of this

option to Is if you want to analyze only the text found in the selected segments. Set it to Is not to

exclude these text segments from the analysis. Finally, select the code segments to analyze or to

exclude either from a drop-down checklist by clicking the arrow button at the right end of the list

box or from a tree representation of the codebook by clicking the

button.

IN RELATION WITH - This set of options allows you to specify whether the quantitative content

analysis should consider potential differences or relationship with one or more quantitative or

categorical variables. If Nothing is selected, WordStat will allow descriptive as well as co-

occurrence analysis on words or categories of words. Selecting the Other Variables option adds

the possibility of examining in WordStat the relationship between these words or categories and

the values of quantitative or categorical variables. The drop-down list box below this option is

used to select the variables in relation to which the analysis will be performed. Selecting

Assigned Codes or Category allows one to analyze the text assigned to codes and identify

potential differences in words or categories of words associated with different codes. When this

option is enabled, a temporary file is created containing all text segments associated with the

selected codes along with a categorical variable to represent the code name. To add to this

temporary file any remaining text segments not associated with any one of those codes, enable the

Include category for remaining text option.

Once the options have been set, click the OK button to call WordStat.

WordStat can also be called from various dialog boxes to analyze retrieved text segments. This feature is

available by clicking the

button located on the toolbar above the result table. In such a situation, the

text analysis is performed on a temporary data file. For this reason, all coding features normally available

in WordStat to apply codes to text segments are disabled.

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QDA Miner User’s Manual 173

Statistical Analysis

QDA Miner shares the same file structure and format as Simstat, an easy-to-use, yet powerful statistical

program. When both applications are installed on the same computer, you can call Simstat from within

QDA Miner and perform statistical analysis on any numerical or categorical variable in the project

without having to export data to an external file, quit QDA Miner, or even close the active project.

Simstat offers a wide range of statistical analysis and graphics. It can also perform numeric and

alphanumeric computation, transformation and recoding of variables, as well as advanced file

management procedures such as data file merging, file aggregation, etc.

To run Simstat from QDA Miner, simply select the STATISTICAL ANALYSIS command from the

ANALYSIS menu. While Simstat is running, QDA Miner will be disabled and it will not be possible to

access any of its features. You will regain access to the program as soon as you quit Simstat.

Please note that QDA Miner can produce frequency tables and crosstabulation on numerical and cate-

gorical variables and produce various charts (bar charts,pie charts, histograms, box-&-whiskers plots)

without the need of run Simstat. For more information on this feature, see page 36.

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174 QDA Miner User’s Manual

Automatic Text Classification

Automated text classification is a supervised machine-learning task by which new documents are

classified into one or several predefined category labels based on an inductive learning process

performed on a set of previously classified documents. This machine-learning approach of classification

has been known to achieve comparable if not superior accuracy to classification performed by human

coders, yet at a very low manpower cost. It has been used to automatically classify documents into

proper categories or to find relevant keywords describing the content and nature of a document. It has

also been used to automatically file or re-route documents or messages to their appropriate destinations,

to classify newspaper articles into proper sections or conference papers into relevant sessions, to filter

emails or documents (like spam filtering), or to route a specific request in an organization to the

appropriate department. Automated text categorization may also be used to identify the author of a

document of unknown or disputed authorship.

Developing, optimizing and testing automatic document classification models often takes time and is

performed by special applications such as the WordStat content analysis and text mining module. QDA

Miner by itself, however does not provide the necessary tools to develop classification models. To do so

one needs the WordStat add-on module. Once developed, these classification models can be saved to

disk and used for classification tasks. QDA Miner can read those saved models and apply them to text

segments (sentences, paragraphs, coded segments) or whole documents. Results of classification may

then be used to automatically assign codes to text segments or values to existing or new categorical

variables.

In order to be accessible by QDA Miner, WordStat classification models should be stored on disk under

the Provalis Research\Models subfolder.

The automatic classification option in QDA Miner is available from the result table of the following text-

retrieval tools:

Text Retrieval

Query by Example

Section Retrieval

Keyword Retrieval

Coding Retrieval

Code Sequence Analysis

It is available from those analysis dialog boxes in a form of an optional panel appearing at the bottom of

the result table. This panel can be displayed or hidden by clicking the

button and looks like the

dialog box below:

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QDA Miner User’s Manual 175

The Classsification Model list box lists all WordStat classification models stored in the Models

subfolder. The first step one needs to do in order to classify retrieved text segments or documents is to

select a classification model. Once a model has been selected, the other panel options become available.

You may then click the

button to obtain information about the classification model developed by

WordStat, such as the number of classes to be predicted, the algorithm used for prediction, etc..

To apply the selected model, click the

button. QDA Miner will take each entry in the result

table above the classification panel and will apply the classification method to the associated text and

will display the classification result in a table. The Predicted Class column indicates which class was

identified as the most likely candidate for classifying this text. The Ratio command computes a ratio

between the similarity score obtained for the predicted class and the second-best class. For example, if

for a specific row, the ratio is equal to 8x, then we could say that the probability that this text belongs to

the predicted class or that the similarity of this document to other documents from this predicted class is

about 8 times higher than for documents in the second most likely class. If this value is low, such as 1.1x

or 1.2x, we could say that the classified text is almost as likely to belong to the second class as the

predicted class, and we may decide to ignore the classification results. All the other columns on the right

of this Ratio column contain relative probability or similarity scores associated with each class. The score

for the predicted class is shown in blue characters and a bold typeface.

By default, the table is sorted in descending order of the ratio statistic, so that classifications for which

we would be the most confident are listed at the top, while the classifications that are more uncertain are

located at the bottom of the list. To sort this table in ascending order on any other column values, simply

click the column header. Clicking a second time on the same column header sorts the rows again in

descending order.

To remove a specific classification result, select its row and then click the

button.

Results of an automatic classification may be stored in the project in two different ways. If full

documents are classified, one can store the value of the predicted class, and optionally, the probability or

similarity score associated with each class into new numerical variables. If paragraphs or sentences are

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176 QDA Miner User’s Manual

classified, then predicted classes may be attached to the text segments as codes. If codes corresponding

to the name of the classes do not already exist, QDA Miner will automatically add those to the codebook,

before applying them.

To store classification results as variables:

Click the

button. A dialog box similar to this one will appear:

To save the predicted class, click the check box beside the Save Predicted Class option and enter

a variable name.

To save the scores associated with each class and upon which the classification has been made,

click the check box beside Save Scores and enter a variable prefix (up to 7 characters). Variable

names are created by adding successive numeric values to this prefix. For example, if the edit box

at the right of the Variable Prefix option is set to "CLASS_", the variable names will be

CLASS_1, CLASS_2, CLASS_3 etc.

If any one of the specified variables does not exist, QDA Miner will create new ones and store the

numerical values associated with either the predicted class or the class scores. A confirmation

dialog box will ask you to confirm the creation of those new variables as well as the overwriting

of any existing ones.

To store classification results as codings:

Click the

button. QDA Miner will ask you to confirm the autocoding of all items in the table

with a code corresponding to the predicted class. If you choose Yes, the program will browse

through the entire project, select the corresponding text segment and assign the code

corresponding to the predicted class. If any one of the codes is currently not in the codebook, a

new category corresponding to the name of the classification model will be created, and the

missing codes will be added to this new category.

To export the classification table to disk:

Click the

button. A Save File dialog box will appear.

In the Save As Type list box, select the file format under which to save the table. The following

formats are supported: ASCII file (*.TXT), Tab delimited file (*.TAB), Comma delimited file

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QDA Miner User’s Manual 177

(*.CSV), MS Word (*.DOC), HTML file (*.HTM; *.HTML), XML files (*.XML) and Excel

spreadsheet file (*.XLS).

Type a valid file name with the proper file extension.

Click the Save button.

To append a copy of the table in the Report Manager:

Click the

button. A descriptive title will be provided automatically for the table. To edit this

title or to enter a new one, hold down the SHIFT keyboard key while clicking this button.

For more information on the Report Manager, see the Report Manager Feature

topic.

To print the table:

Click the

button.

178 QDA Miner User’s Manual

Storing Code Statistics into Numeric Variables

Most output tables created by QDA Miner may be exported to disk in various file formats including

Excel, MS Word, HTML, XML, and delimited ASCII files. Code statistics may also be appended to the

existing project file, allowing you to use those new variables for further numerical processing or case

filtering. To append code statistics to your current project, select the COMPUTE command from the

VARIABLES menu. The following dialog box will appear:

COMPUTE - This option allows you to specify which statistic calculated on selected codes will be

stored in the numeric variables. Four statistics are available:

• Occurrence

• Frequency.

• Word Count

• Rate per 1000 words

OF - Select the codes on which to calculate the above statistic by clicking the arrow button at the right

end of the list box and by selecting these codes. The number of variables calculated will be equal

to the number of codes selected .

IN - This option allows you to specify on which document variables the calculation will be performed.

If the current project contains more than one document variable, you will have a choice to select

either one or more of them. By default, all document variables are selected. To restrict the

computation to a few of them, click the down arrow key at the right of the list box. A list of all

available document variables will appear. Select all the document variables that you want to

analyze.

VARIABLE NAMES - This option sets what method should be used by QDA Miner to create new

variable names. When set to CODE NAME, the program will attempt to use each code name as

the name of a new variable. Illegal characters are automatically removed and long names are

QDA Miner User’s Manual 179

truncated to the first 10 characters. Duplicated variable names are distinguished by the

substitution of numerical digits at the end of the name. When this option is set to PREFIX,

variable names are created by adding successive numeric values to a user-defined prefix. For

example, if the edit box at the right of the prefix option is set to "FREQ", the variable names will

be FREQ1, FREQ2, FREQ3, etc.

VARIABLE TYPE - By default, QDA Miner saves code statistics in as many variables as there are

codes. For example, if your codebook contains 100 codes, then 100 variables will be necessary to

store the statistics associated with each code, on code statistics per variable. When you choose to

store the occurrence of codes, QDA Miner offers you the possibility to store the observed

occurrences in a limited number of polynomial (or multinomial) variables. For example, if the

maximum number of different codes per case is no more than 10, then you may instruct QDA

Miner to create 10 nominal variables and store in each of those, a nominal value representing one

of the codes. If less than 10 codes are found in a specific case, then the remaining variables are

left empty. To store values in a limited set of nominal variables, choose the Multiple Polynomial

Variables option and enter the Maximum Number of Variables that should be used for storing

the code values. To store code names rather than nominal values, select Multiple String

Variables instead. If the maximum number of codes found in a single case is higher than the

specified number of variables, then a warning message will appear to let you know that some code

occurrence information have been lost and to indicate what was the maximum number of codes

encountered in the project. To store occurrences as zeros and ones in as many variables as there

are codes, selecting the Multiple Dichotomous Variables option.

When you click the OK button, the program prompts you to confirm the calculation of the required

number of variables. If variables with similar names exist in the project, the confirmation dialog box will

display the number of existing variables that would be overwritten as well as the number of variables that

need to be created by this command. Click the YES button to confirm the creation and overwriting of

variables. Click NO to abort the procedure.

To exit from the dialog box without performing the recoding, click the CANCEL button.

180 QDA Miner User’s Manual

Exporting Code Statistics

Various code statistics may be exported to disk in different file formats including Excel, HTML, XML

and delimited ASCII files, allowing those data to be further analyzed using a statistical program. To

export code statistics to disk, select the EXPORT | CODE STATISTICS command sequence from the

PROJECT menu. The following dialog box will appear:

EXPORT - This option allows you to specify which statistic calculated on selected codes will be

stored into numeric variables and exported. Four statistics are available:

• Occurrence

• Frequency

• Word Count

• Percentage of Words

OF - Select the codes on which to calculate the above statistic by clicking the arrow button at the

right end of the list box and by selecting these codes. The number of variables calculated will be

equal to the number of codes selected.

IN - This option allows you to specify on which document variables the calculation will be

performed. If the current project contains more than one document variable, you will have a

choice to select either one or more of them. By default, all document variables are selected. To

restrict the computation to a few of them, click the down arrow key at the right of the list box. A

list of all available document variables will appear. Select all the document variables that you

want to analyze.

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