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Questions and Answers
What is the difference between qualitative and quantitative data analysis?
What is the purpose of data cleansing?
What is the difference between mean, median, and mode?
What is the difference between inductive and deductive approaches to qualitative data analysis?
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What is the purpose of critical incident analysis?
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What is the purpose of affinity diagrams in qualitative data analysis?
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What is the purpose of content analysis?
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What is the difference between grounded theory and thematic analysis?
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What is the purpose of conversation analysis?
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What is the purpose of interaction analysis?
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What is the purpose of systems-based frameworks in qualitative data analysis?
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What is the purpose of critical incident analysis in the study by Elise Grison et al. on travelers' choices of transport mode in Paris?
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What is the difference between quantitative and qualitative data analysis?
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What is the first step in qualitative analysis?
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What is thematic analysis?
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What is critical incident analysis?
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What is the purpose of data cleansing?
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What is the difference between mean, median, and mode?
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What is the difference between inductive and deductive analysis?
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What is the purpose of affinity diagrams?
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What is the purpose of identifying outliers in data analysis?
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What is the purpose of producing graphical representations of data in basic quantitative analysis?
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What is the purpose of using more sophisticated analytical frameworks in qualitative data analysis?
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What is the purpose of identifying patterns or calculating simple numerical values in data analysis?
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What is the difference between qualitative and quantitative data analysis?
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What is the initial step of data analysis?
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What is the purpose of data cleansing?
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What is the common mistake in data analysis, interpretation, and presentation?
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What is the difference between quantitative and qualitative data?
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What is the difference between close-ended questions and open-ended questions?
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What is the purpose of outliers in data analysis?
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What is the difference between inductive and deductive analysis?
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What is thematic analysis?
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What is critical incident analysis?
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What is the purpose of conversation analysis?
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What is grounded theory?
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What is the difference between qualitative and quantitative data analysis?
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What is the first step in qualitative data analysis?
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What is the difference between inductive and deductive approaches to qualitative data analysis?
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What is critical incident analysis?
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What is the difference between mean, median, and mode?
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What is the purpose of data cleansing?
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What is the difference between quantitative and qualitative data?
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What is the objective of qualitative data analysis?
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What is the difference between quantitative and qualitative approaches to data analysis?
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What is the best way to present findings in data analysis?
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What is the difference between basic quantitative and qualitative analysis techniques?
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What is the purpose of thematic analysis?
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What is the difference between quantitative and qualitative data analysis?
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What is the first step in qualitative analysis?
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What is the difference between inductive and deductive approaches in qualitative data analysis?
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What is critical incident analysis?
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What is the purpose of identifying themes in qualitative data analysis?
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What is the difference between mean, median, and mode?
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What is the purpose of data cleansing in data analysis?
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What is the difference between quantitative and qualitative data?
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What is the objective of grounded theory in qualitative data analysis?
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What is the purpose of finding the best way to present findings in data analysis?
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What is the difference between open-ended and close-ended questions in survey design?
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What is the purpose of critical incident analysis in usability studies?
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What is the difference between quantitative and qualitative data analysis?
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What is the purpose of data cleansing in data analysis?
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Which of the following is an example of basic quantitative analysis technique?
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What is the difference between inductive and deductive approaches in qualitative data analysis?
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What is the objective of qualitative data analysis?
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Which of the following is an analytical framework for qualitative data analysis?
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What is critical incident analysis?
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What is the difference between quantitative and qualitative data?
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What is the purpose of finding outliers in data analysis?
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What is the difference between thematic analysis and categorizing data in qualitative data analysis?
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What is the purpose of producing graphical representations of data in basic quantitative analysis?
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What is the difference between quantitative and qualitative approaches in data analysis?
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What is the main difference between qualitative and quantitative data?
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What is the goal of qualitative data analysis?
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What is the difference between inductive and deductive approaches to qualitative data analysis?
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What is critical incident analysis?
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What is the purpose of data cleansing?
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What is the difference between mean, median, and mode?
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What is the best way to present findings?
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What is the purpose of thematic analysis?
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What is the purpose of categorizing data?
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What is the difference between quantitative and qualitative analysis?
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What is the purpose of interaction analysis?
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What is the difference between quantitative and qualitative data?
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What is the difference between qualitative and quantitative data analysis?
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What is the goal of qualitative data analysis?
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What is the difference between inductive and deductive approaches to data analysis?
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What is critical incident analysis?
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What is the purpose of finding averages in basic quantitative analysis?
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What is the difference between transcription and expansion of notes in the initial processing of data?
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What is the purpose of data cleansing?
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What is the difference between mean, median, and mode?
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Study Notes
Data Analysis, Interpretation, and Presentation
-
Qualitative and quantitative data and analysis are different.
-
A combination of qualitative and quantitative approaches is common in data analysis.
-
Initial reactions or observations from the data involve identifying patterns or calculating simple numerical values such as ratios, averages, or percentages.
-
Data cleansing is important to check for any anomalies that might be erroneous.
-
Interpretation of the findings often proceeds in parallel with analysis, but it is important to make sure that the data supports any conclusions.
-
Making claims that go beyond what the data can support is a common mistake in data analysis, interpretation, and presentation.
-
Finding the best way to present findings depends on the goals and audience for whom the study was performed.
-
Quantitative data is in the form of numbers, while qualitative data is in the form of words and images.
-
All forms of data gathering may result in qualitative and quantitative data.
-
Quantitative analysis uses numerical methods, while qualitative analysis focuses on the nature of something and can be represented by themes, patterns, and stories.
-
Initial processing of data involves transcription, expansion of notes, entry of answers to close-ended questions into a spreadsheet, clean up data, filter into different data sets, synchronization between data recordings, and records of behavior.
-
Basic quantitative analysis techniques include averages and percentages, and there are three types of average: mean, median, and mode.Basic Data Analysis Techniques
-
Data analysis is the process of examining data to extract meaningful insights and conclusions.
-
There are two basic approaches to data analysis: quantitative analysis and qualitative analysis.
-
Quantitative analysis involves numerical data and statistical methods, while qualitative analysis involves non-numerical data and subjective interpretation.
-
Quantitative data can be analyzed using spreadsheet software like Excel or Google Sheets, while qualitative data requires manual analysis and interpretation.
-
The design of survey questions affects the kind of analyses that can be performed and the conclusions that can be drawn.
-
Close-ended questions with fixed answer options can be analyzed quantitatively, while open-ended questions require qualitative analysis.
-
Basic quantitative analysis involves finding averages, identifying outliers, and producing graphical representations of the data.
-
Data visualization tools like heatmaps can create more sophisticated representations of the data.
-
Outliers are usually removed from the main data set because they distort general patterns, but they may also be interesting cases to investigate further.
-
Basic qualitative analysis involves identifying themes, categorizing data, and analyzing critical incidents.
-
Critical incident analysis is a way to isolate subsets of data for more detailed analysis.
-
The first step in qualitative analysis is to gain an overall impression of the data and start looking for interesting features, topics, repeated observations, or things that stand out.Basic Qualitative Data Analysis Techniques
-
Qualitative data can be analyzed inductively or deductively, depending on the data obtained and the goal of the study.
-
The objective of qualitative data analysis is to classify elements of the data in order to gain insights toward the study’s goal.
-
Identifying themes (thematic analysis) takes an inductive approach, while categorizing data takes a deductive approach.
-
Analysis is often performed iteratively, and it is common for themes identified inductively then to be applied deductively to new data.
-
Reliability of the analysis can be achieved by training another person to do the coding and calculating the inter-rater reliability.
-
Using more sophisticated analytical frameworks to structure the analysis of qualitative data can lead to additional insights that go beyond the results of basic techniques.
-
Thematic analysis is a widely used analytical technique that aims to identify, analyze, and report patterns in the data.
-
After an initial pass through the data, the next step is to look more systematically for themes across participants’ transcripts, seeking further evidence both to confirm and disconfirm initial impressions in all of the data.
-
An overall narrative is starting to emerge from the set of themes, and some of the original themes may not seem as relevant and can be removed.
-
Affinity diagrams are a common technique for exploring data, identifying themes, and looking for an overall narrative.
-
Inductive analysis is appropriate when the study is exploratory, and it is important to let the themes emerge from the data itself.
-
Categorization is appropriate when the analysis frame is chosen beforehand, based on the study goal.Analytical Frameworks for Qualitative Data Analysis
-
Usability studies can use think-aloud protocols to identify usability problems and categorize them for analysis.
-
Critical incident analysis helps to identify significant subsets of data for more detailed analysis by focusing on incidents that are significant or pivotal.
-
Six different analytical frameworks can be used for qualitative data analysis, ordered by granularity.
-
Conversation analysis focuses on recordings of spoken conversations to provide insights into how conversations are managed and progress.
-
Discourse analysis focuses on recordings of speech or writing to understand how words are used to convey meaning.
-
Content analysis examines any form of text to determine the frequency of items appearing in a text at varying levels.
-
Interaction analysis uses video recordings to analyze verbal and non-verbal interactions between people and artifacts to understand how knowledge and action are used within an activity.
-
Grounded theory constructs a theory around the phenomenon of interest using empirical data of any kind.
-
Systems-based frameworks focus on large-scale and heterogeneous data involving people and technology to provide insights about organizational effectiveness and efficiency.
-
Tuomas Kari et al. used critical incident analysis to identify types of behavior change induced in players of the game Pokémon GO.
-
Elise Grison et al. used critical incident analysis to investigate factors influencing travelers' choices of transport mode in Paris.
-
In a task of identifying the next available theater performance or movie to attend, critical incidents may include searching social media, discovering a favorite movie is playing, and needing a credit card to purchase tickets.
Data Analysis, Interpretation, and Presentation
-
Qualitative and quantitative data and analysis are different.
-
A combination of qualitative and quantitative approaches is common in data analysis.
-
Initial reactions or observations from the data involve identifying patterns or calculating simple numerical values such as ratios, averages, or percentages.
-
Data cleansing is important to check for any anomalies that might be erroneous.
-
Interpretation of the findings often proceeds in parallel with analysis, but it is important to make sure that the data supports any conclusions.
-
Making claims that go beyond what the data can support is a common mistake in data analysis, interpretation, and presentation.
-
Finding the best way to present findings depends on the goals and audience for whom the study was performed.
-
Quantitative data is in the form of numbers, while qualitative data is in the form of words and images.
-
All forms of data gathering may result in qualitative and quantitative data.
-
Quantitative analysis uses numerical methods, while qualitative analysis focuses on the nature of something and can be represented by themes, patterns, and stories.
-
Initial processing of data involves transcription, expansion of notes, entry of answers to close-ended questions into a spreadsheet, clean up data, filter into different data sets, synchronization between data recordings, and records of behavior.
-
Basic quantitative analysis techniques include averages and percentages, and there are three types of average: mean, median, and mode.Basic Data Analysis Techniques
-
Data analysis is the process of examining data to extract meaningful insights and conclusions.
-
There are two basic approaches to data analysis: quantitative analysis and qualitative analysis.
-
Quantitative analysis involves numerical data and statistical methods, while qualitative analysis involves non-numerical data and subjective interpretation.
-
Quantitative data can be analyzed using spreadsheet software like Excel or Google Sheets, while qualitative data requires manual analysis and interpretation.
-
The design of survey questions affects the kind of analyses that can be performed and the conclusions that can be drawn.
-
Close-ended questions with fixed answer options can be analyzed quantitatively, while open-ended questions require qualitative analysis.
-
Basic quantitative analysis involves finding averages, identifying outliers, and producing graphical representations of the data.
-
Data visualization tools like heatmaps can create more sophisticated representations of the data.
-
Outliers are usually removed from the main data set because they distort general patterns, but they may also be interesting cases to investigate further.
-
Basic qualitative analysis involves identifying themes, categorizing data, and analyzing critical incidents.
-
Critical incident analysis is a way to isolate subsets of data for more detailed analysis.
-
The first step in qualitative analysis is to gain an overall impression of the data and start looking for interesting features, topics, repeated observations, or things that stand out.Basic Qualitative Data Analysis Techniques
-
Qualitative data can be analyzed inductively or deductively, depending on the data obtained and the goal of the study.
-
The objective of qualitative data analysis is to classify elements of the data in order to gain insights toward the study’s goal.
-
Identifying themes (thematic analysis) takes an inductive approach, while categorizing data takes a deductive approach.
-
Analysis is often performed iteratively, and it is common for themes identified inductively then to be applied deductively to new data.
-
Reliability of the analysis can be achieved by training another person to do the coding and calculating the inter-rater reliability.
-
Using more sophisticated analytical frameworks to structure the analysis of qualitative data can lead to additional insights that go beyond the results of basic techniques.
-
Thematic analysis is a widely used analytical technique that aims to identify, analyze, and report patterns in the data.
-
After an initial pass through the data, the next step is to look more systematically for themes across participants’ transcripts, seeking further evidence both to confirm and disconfirm initial impressions in all of the data.
-
An overall narrative is starting to emerge from the set of themes, and some of the original themes may not seem as relevant and can be removed.
-
Affinity diagrams are a common technique for exploring data, identifying themes, and looking for an overall narrative.
-
Inductive analysis is appropriate when the study is exploratory, and it is important to let the themes emerge from the data itself.
-
Categorization is appropriate when the analysis frame is chosen beforehand, based on the study goal.Analytical Frameworks for Qualitative Data Analysis
-
Usability studies can use think-aloud protocols to identify usability problems and categorize them for analysis.
-
Critical incident analysis helps to identify significant subsets of data for more detailed analysis by focusing on incidents that are significant or pivotal.
-
Six different analytical frameworks can be used for qualitative data analysis, ordered by granularity.
-
Conversation analysis focuses on recordings of spoken conversations to provide insights into how conversations are managed and progress.
-
Discourse analysis focuses on recordings of speech or writing to understand how words are used to convey meaning.
-
Content analysis examines any form of text to determine the frequency of items appearing in a text at varying levels.
-
Interaction analysis uses video recordings to analyze verbal and non-verbal interactions between people and artifacts to understand how knowledge and action are used within an activity.
-
Grounded theory constructs a theory around the phenomenon of interest using empirical data of any kind.
-
Systems-based frameworks focus on large-scale and heterogeneous data involving people and technology to provide insights about organizational effectiveness and efficiency.
-
Tuomas Kari et al. used critical incident analysis to identify types of behavior change induced in players of the game Pokémon GO.
-
Elise Grison et al. used critical incident analysis to investigate factors influencing travelers' choices of transport mode in Paris.
-
In a task of identifying the next available theater performance or movie to attend, critical incidents may include searching social media, discovering a favorite movie is playing, and needing a credit card to purchase tickets.
Data Analysis, Interpretation, and Presentation
-
Qualitative and quantitative data and analysis are different.
-
A combination of qualitative and quantitative approaches is common in data analysis.
-
Initial reactions or observations from the data involve identifying patterns or calculating simple numerical values such as ratios, averages, or percentages.
-
Data cleansing is important to check for any anomalies that might be erroneous.
-
Interpretation of the findings often proceeds in parallel with analysis, but it is important to make sure that the data supports any conclusions.
-
Making claims that go beyond what the data can support is a common mistake in data analysis, interpretation, and presentation.
-
Finding the best way to present findings depends on the goals and audience for whom the study was performed.
-
Quantitative data is in the form of numbers, while qualitative data is in the form of words and images.
-
All forms of data gathering may result in qualitative and quantitative data.
-
Quantitative analysis uses numerical methods, while qualitative analysis focuses on the nature of something and can be represented by themes, patterns, and stories.
-
Initial processing of data involves transcription, expansion of notes, entry of answers to close-ended questions into a spreadsheet, clean up data, filter into different data sets, synchronization between data recordings, and records of behavior.
-
Basic quantitative analysis techniques include averages and percentages, and there are three types of average: mean, median, and mode.Basic Data Analysis Techniques
-
Data analysis is the process of examining data to extract meaningful insights and conclusions.
-
There are two basic approaches to data analysis: quantitative analysis and qualitative analysis.
-
Quantitative analysis involves numerical data and statistical methods, while qualitative analysis involves non-numerical data and subjective interpretation.
-
Quantitative data can be analyzed using spreadsheet software like Excel or Google Sheets, while qualitative data requires manual analysis and interpretation.
-
The design of survey questions affects the kind of analyses that can be performed and the conclusions that can be drawn.
-
Close-ended questions with fixed answer options can be analyzed quantitatively, while open-ended questions require qualitative analysis.
-
Basic quantitative analysis involves finding averages, identifying outliers, and producing graphical representations of the data.
-
Data visualization tools like heatmaps can create more sophisticated representations of the data.
-
Outliers are usually removed from the main data set because they distort general patterns, but they may also be interesting cases to investigate further.
-
Basic qualitative analysis involves identifying themes, categorizing data, and analyzing critical incidents.
-
Critical incident analysis is a way to isolate subsets of data for more detailed analysis.
-
The first step in qualitative analysis is to gain an overall impression of the data and start looking for interesting features, topics, repeated observations, or things that stand out.Basic Qualitative Data Analysis Techniques
-
Qualitative data can be analyzed inductively or deductively, depending on the data obtained and the goal of the study.
-
The objective of qualitative data analysis is to classify elements of the data in order to gain insights toward the study’s goal.
-
Identifying themes (thematic analysis) takes an inductive approach, while categorizing data takes a deductive approach.
-
Analysis is often performed iteratively, and it is common for themes identified inductively then to be applied deductively to new data.
-
Reliability of the analysis can be achieved by training another person to do the coding and calculating the inter-rater reliability.
-
Using more sophisticated analytical frameworks to structure the analysis of qualitative data can lead to additional insights that go beyond the results of basic techniques.
-
Thematic analysis is a widely used analytical technique that aims to identify, analyze, and report patterns in the data.
-
After an initial pass through the data, the next step is to look more systematically for themes across participants’ transcripts, seeking further evidence both to confirm and disconfirm initial impressions in all of the data.
-
An overall narrative is starting to emerge from the set of themes, and some of the original themes may not seem as relevant and can be removed.
-
Affinity diagrams are a common technique for exploring data, identifying themes, and looking for an overall narrative.
-
Inductive analysis is appropriate when the study is exploratory, and it is important to let the themes emerge from the data itself.
-
Categorization is appropriate when the analysis frame is chosen beforehand, based on the study goal.Analytical Frameworks for Qualitative Data Analysis
-
Usability studies can use think-aloud protocols to identify usability problems and categorize them for analysis.
-
Critical incident analysis helps to identify significant subsets of data for more detailed analysis by focusing on incidents that are significant or pivotal.
-
Six different analytical frameworks can be used for qualitative data analysis, ordered by granularity.
-
Conversation analysis focuses on recordings of spoken conversations to provide insights into how conversations are managed and progress.
-
Discourse analysis focuses on recordings of speech or writing to understand how words are used to convey meaning.
-
Content analysis examines any form of text to determine the frequency of items appearing in a text at varying levels.
-
Interaction analysis uses video recordings to analyze verbal and non-verbal interactions between people and artifacts to understand how knowledge and action are used within an activity.
-
Grounded theory constructs a theory around the phenomenon of interest using empirical data of any kind.
-
Systems-based frameworks focus on large-scale and heterogeneous data involving people and technology to provide insights about organizational effectiveness and efficiency.
-
Tuomas Kari et al. used critical incident analysis to identify types of behavior change induced in players of the game Pokémon GO.
-
Elise Grison et al. used critical incident analysis to investigate factors influencing travelers' choices of transport mode in Paris.
-
In a task of identifying the next available theater performance or movie to attend, critical incidents may include searching social media, discovering a favorite movie is playing, and needing a credit card to purchase tickets.
Data Analysis, Interpretation, and Presentation
-
Qualitative and quantitative data and analysis are different.
-
A combination of qualitative and quantitative approaches is common in data analysis.
-
Initial reactions or observations from the data involve identifying patterns or calculating simple numerical values such as ratios, averages, or percentages.
-
Data cleansing is important to check for any anomalies that might be erroneous.
-
Interpretation of the findings often proceeds in parallel with analysis, but it is important to make sure that the data supports any conclusions.
-
Making claims that go beyond what the data can support is a common mistake in data analysis, interpretation, and presentation.
-
Finding the best way to present findings depends on the goals and audience for whom the study was performed.
-
Quantitative data is in the form of numbers, while qualitative data is in the form of words and images.
-
All forms of data gathering may result in qualitative and quantitative data.
-
Quantitative analysis uses numerical methods, while qualitative analysis focuses on the nature of something and can be represented by themes, patterns, and stories.
-
Initial processing of data involves transcription, expansion of notes, entry of answers to close-ended questions into a spreadsheet, clean up data, filter into different data sets, synchronization between data recordings, and records of behavior.
-
Basic quantitative analysis techniques include averages and percentages, and there are three types of average: mean, median, and mode.Basic Data Analysis Techniques
-
Data analysis is the process of examining data to extract meaningful insights and conclusions.
-
There are two basic approaches to data analysis: quantitative analysis and qualitative analysis.
-
Quantitative analysis involves numerical data and statistical methods, while qualitative analysis involves non-numerical data and subjective interpretation.
-
Quantitative data can be analyzed using spreadsheet software like Excel or Google Sheets, while qualitative data requires manual analysis and interpretation.
-
The design of survey questions affects the kind of analyses that can be performed and the conclusions that can be drawn.
-
Close-ended questions with fixed answer options can be analyzed quantitatively, while open-ended questions require qualitative analysis.
-
Basic quantitative analysis involves finding averages, identifying outliers, and producing graphical representations of the data.
-
Data visualization tools like heatmaps can create more sophisticated representations of the data.
-
Outliers are usually removed from the main data set because they distort general patterns, but they may also be interesting cases to investigate further.
-
Basic qualitative analysis involves identifying themes, categorizing data, and analyzing critical incidents.
-
Critical incident analysis is a way to isolate subsets of data for more detailed analysis.
-
The first step in qualitative analysis is to gain an overall impression of the data and start looking for interesting features, topics, repeated observations, or things that stand out.Basic Qualitative Data Analysis Techniques
-
Qualitative data can be analyzed inductively or deductively, depending on the data obtained and the goal of the study.
-
The objective of qualitative data analysis is to classify elements of the data in order to gain insights toward the study’s goal.
-
Identifying themes (thematic analysis) takes an inductive approach, while categorizing data takes a deductive approach.
-
Analysis is often performed iteratively, and it is common for themes identified inductively then to be applied deductively to new data.
-
Reliability of the analysis can be achieved by training another person to do the coding and calculating the inter-rater reliability.
-
Using more sophisticated analytical frameworks to structure the analysis of qualitative data can lead to additional insights that go beyond the results of basic techniques.
-
Thematic analysis is a widely used analytical technique that aims to identify, analyze, and report patterns in the data.
-
After an initial pass through the data, the next step is to look more systematically for themes across participants’ transcripts, seeking further evidence both to confirm and disconfirm initial impressions in all of the data.
-
An overall narrative is starting to emerge from the set of themes, and some of the original themes may not seem as relevant and can be removed.
-
Affinity diagrams are a common technique for exploring data, identifying themes, and looking for an overall narrative.
-
Inductive analysis is appropriate when the study is exploratory, and it is important to let the themes emerge from the data itself.
-
Categorization is appropriate when the analysis frame is chosen beforehand, based on the study goal.Analytical Frameworks for Qualitative Data Analysis
-
Usability studies can use think-aloud protocols to identify usability problems and categorize them for analysis.
-
Critical incident analysis helps to identify significant subsets of data for more detailed analysis by focusing on incidents that are significant or pivotal.
-
Six different analytical frameworks can be used for qualitative data analysis, ordered by granularity.
-
Conversation analysis focuses on recordings of spoken conversations to provide insights into how conversations are managed and progress.
-
Discourse analysis focuses on recordings of speech or writing to understand how words are used to convey meaning.
-
Content analysis examines any form of text to determine the frequency of items appearing in a text at varying levels.
-
Interaction analysis uses video recordings to analyze verbal and non-verbal interactions between people and artifacts to understand how knowledge and action are used within an activity.
-
Grounded theory constructs a theory around the phenomenon of interest using empirical data of any kind.
-
Systems-based frameworks focus on large-scale and heterogeneous data involving people and technology to provide insights about organizational effectiveness and efficiency.
-
Tuomas Kari et al. used critical incident analysis to identify types of behavior change induced in players of the game Pokémon GO.
-
Elise Grison et al. used critical incident analysis to investigate factors influencing travelers' choices of transport mode in Paris.
-
In a task of identifying the next available theater performance or movie to attend, critical incidents may include searching social media, discovering a favorite movie is playing, and needing a credit card to purchase tickets.
Data Analysis, Interpretation, and Presentation
-
Qualitative and quantitative data and analysis are different.
-
A combination of qualitative and quantitative approaches is common in data analysis.
-
Initial reactions or observations from the data involve identifying patterns or calculating simple numerical values such as ratios, averages, or percentages.
-
Data cleansing is important to check for any anomalies that might be erroneous.
-
Interpretation of the findings often proceeds in parallel with analysis, but it is important to make sure that the data supports any conclusions.
-
Making claims that go beyond what the data can support is a common mistake in data analysis, interpretation, and presentation.
-
Finding the best way to present findings depends on the goals and audience for whom the study was performed.
-
Quantitative data is in the form of numbers, while qualitative data is in the form of words and images.
-
All forms of data gathering may result in qualitative and quantitative data.
-
Quantitative analysis uses numerical methods, while qualitative analysis focuses on the nature of something and can be represented by themes, patterns, and stories.
-
Initial processing of data involves transcription, expansion of notes, entry of answers to close-ended questions into a spreadsheet, clean up data, filter into different data sets, synchronization between data recordings, and records of behavior.
-
Basic quantitative analysis techniques include averages and percentages, and there are three types of average: mean, median, and mode.Basic Data Analysis Techniques
-
Data analysis is the process of examining data to extract meaningful insights and conclusions.
-
There are two basic approaches to data analysis: quantitative analysis and qualitative analysis.
-
Quantitative analysis involves numerical data and statistical methods, while qualitative analysis involves non-numerical data and subjective interpretation.
-
Quantitative data can be analyzed using spreadsheet software like Excel or Google Sheets, while qualitative data requires manual analysis and interpretation.
-
The design of survey questions affects the kind of analyses that can be performed and the conclusions that can be drawn.
-
Close-ended questions with fixed answer options can be analyzed quantitatively, while open-ended questions require qualitative analysis.
-
Basic quantitative analysis involves finding averages, identifying outliers, and producing graphical representations of the data.
-
Data visualization tools like heatmaps can create more sophisticated representations of the data.
-
Outliers are usually removed from the main data set because they distort general patterns, but they may also be interesting cases to investigate further.
-
Basic qualitative analysis involves identifying themes, categorizing data, and analyzing critical incidents.
-
Critical incident analysis is a way to isolate subsets of data for more detailed analysis.
-
The first step in qualitative analysis is to gain an overall impression of the data and start looking for interesting features, topics, repeated observations, or things that stand out.Basic Qualitative Data Analysis Techniques
-
Qualitative data can be analyzed inductively or deductively, depending on the data obtained and the goal of the study.
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The objective of qualitative data analysis is to classify elements of the data in order to gain insights toward the study’s goal.
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Identifying themes (thematic analysis) takes an inductive approach, while categorizing data takes a deductive approach.
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Analysis is often performed iteratively, and it is common for themes identified inductively then to be applied deductively to new data.
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Reliability of the analysis can be achieved by training another person to do the coding and calculating the inter-rater reliability.
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Using more sophisticated analytical frameworks to structure the analysis of qualitative data can lead to additional insights that go beyond the results of basic techniques.
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Thematic analysis is a widely used analytical technique that aims to identify, analyze, and report patterns in the data.
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After an initial pass through the data, the next step is to look more systematically for themes across participants’ transcripts, seeking further evidence both to confirm and disconfirm initial impressions in all of the data.
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An overall narrative is starting to emerge from the set of themes, and some of the original themes may not seem as relevant and can be removed.
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Affinity diagrams are a common technique for exploring data, identifying themes, and looking for an overall narrative.
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Inductive analysis is appropriate when the study is exploratory, and it is important to let the themes emerge from the data itself.
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Categorization is appropriate when the analysis frame is chosen beforehand, based on the study goal.Analytical Frameworks for Qualitative Data Analysis
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Usability studies can use think-aloud protocols to identify usability problems and categorize them for analysis.
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Critical incident analysis helps to identify significant subsets of data for more detailed analysis by focusing on incidents that are significant or pivotal.
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Six different analytical frameworks can be used for qualitative data analysis, ordered by granularity.
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Conversation analysis focuses on recordings of spoken conversations to provide insights into how conversations are managed and progress.
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Discourse analysis focuses on recordings of speech or writing to understand how words are used to convey meaning.
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Content analysis examines any form of text to determine the frequency of items appearing in a text at varying levels.
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Interaction analysis uses video recordings to analyze verbal and non-verbal interactions between people and artifacts to understand how knowledge and action are used within an activity.
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Grounded theory constructs a theory around the phenomenon of interest using empirical data of any kind.
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Systems-based frameworks focus on large-scale and heterogeneous data involving people and technology to provide insights about organizational effectiveness and efficiency.
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Tuomas Kari et al. used critical incident analysis to identify types of behavior change induced in players of the game Pokémon GO.
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Elise Grison et al. used critical incident analysis to investigate factors influencing travelers' choices of transport mode in Paris.
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In a task of identifying the next available theater performance or movie to attend, critical incidents may include searching social media, discovering a favorite movie is playing, and needing a credit card to purchase tickets.
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Test your knowledge of data analysis, interpretation, and presentation with this informative quiz. Learn about the differences between qualitative and quantitative data, understand the basic techniques for analyzing both types of data, and discover the various analytical frameworks used in qualitative data analysis. This quiz will challenge your understanding of data analysis and interpretation, providing you with valuable insights into how to effectively present your findings to your intended audience.
