Step 7: Data Analysis
The seventh step in the process of generating a well-informed research design has to do with the selection and definition of the data analysis strategies that will be used in your study. Please check the resources below depending on the approach to research that you are following.
Data Analysis in Qualitative Studies
Data Analysis in Quantitative Studies
Resources
IRML's website
Step 7: Data Analysis in qualitative studies
Analyzing text and multiple other forms of data (images, videos, social media posts, etc) presents a challenging task for qualitative researchers. Moreover, deciding how to represent the data in tables, matrices, and narrative form, adds to the challenge.
The processes of data collection, data analysis, and report writing are not distinct steps in a research study; they are interrelated and often go on simultaneously in a research project.
IRML's website
The central steps of Data Analysis in Qualitative Research are:
1. Preparing and organizing the data (i.e., text data as in transcripts, or image data as in photographs) for analysis 2. Coding the data (reducing the data into meaningful segments and assigning names for the segments) 3. Combining the codes into broader categories or themes 4. Representing, displaying and making comparisons in the data graphs, tables, and charts.
IRML's website
Creswell (2007) proposes the following data analysis Spiral for qualitative studies
1. Data managing
At an early stage in the analysis process, researchers organize their data into file folders, index cards, or computer files. Besides organizing files, researchers convert their files to appropriate text units (e.g., a word, a sentence, an entire story) for analysis either by hand or by computer.
Materials must be easily located in large databases of text (or images).
IRML's website
2. Reading, Memoing
Researchers continue analysis by getting a sense of the whole database. Agar (1980), suggests that researchers "... read the transcripts in their entirety several times. Immerse yourself in the details, trying to get a sense of the data as a whole before breaking it into parts". Writing memos in the margins of fieldnotes or transcripts or under photographs helps in this initial process of exploring a database. Memos are short phrases, ideas, or key concepts that occur to the reader. This process consists of moving from the reading and memoing loop into the spiral to the describing, classifying, and interpreting loop.
IRML's website
3. Describing, classifying, interpreting
In this loop, code or category formation represents the heart of qualitative data analysis. Here researchers describe in detail, develop themes or dimensions through some classification system, and provide an interpretation in light of their own views or views of perspectives in the literature. During this process of describing, classifying and interpreting, qualitative researchers develop codes or categories and sort text or visual images into categories.
What is a Code? A code in qualitative inquiry is most often a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data (Saldaña, 2009). When codes are applied and reapplied to qualitative data, you are codifying – a process that permits data to be “segregated, grouped, regrouped and relinked in order to consolidate meaning and explanation” (Grabich, 2007).
IRML's website
3. Describing, classifying, interpreting
Bernard (2006) succinctly states that analysis “is the search for patterns in data and for ideas that help explain why those patterns are there in the first place”. Coding is thus a method that enables you to organize and group similarly coded data into categories or “families” because they share some characteristic – the beginning of a pattern.
4. Representing, visualizing
In the final phase of the spiral defined by Creswell (2007), researchers present the data, a packaging of what was found in text, tabular, or figure form. For example, creating a visual image of the information. At this point, the researcher might obtain feedback on the initial summaries by taking information back to informants.
IRML's website
4. Representing, visualizing
The following visual representation was generated using Atlas.ti. It shows the different codes and families of codes obtained in the analysis of a dataset regarding the impact advanced degrees of teachers have on student's achievement.
IRML's website
From the previous 4 steps described by Creswell (2007), we will now focus our attention in the third one (Describing, classifying, interpreting), particularly in the mechanics of coding.
Saldaña (2009) defines the following types of coding:
Open coding: Basically, you read through your data several times and then start to create tentative labels for chunks of data that summarize what you see happening (not based on existing theory – just based on the meaning that emerges from the data). Record examples of participants’ words and establish properties of each code (see my charts below). Axial coding: Axial coding consists of identifying relationships among the open codes. What are the connections among the codes? This will be easier to understand when you see the last chart of this blog post. Selective coding: It consists in figuring out the core variable that includes all of the data. Then you have to reread the transcripts and selectively code any data that relates to the core variable you identified.
IRML's website
Example of Open Coding
Example of Axial Coding
IRML's website
Example of Selective Coding
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
Nowadays, most qualitative researchers use specific software to assist the analysis process. It is what we call Computer-assisted (or aided) qualitative data analysis software (CAQDAS). It offers tools that assist with qualitative research such as transcription analysis, coding and text interpretation.
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
Nowadays, most qualitative researchers use specific software to assist the analysis process. It is what we call Computer-assisted (or aided) qualitative data analysis software (CAQDAS). It offers tools that assist with qualitative research such as transcription analysis, coding and text interpretation.
The following clip presents a summary of the most used qualitative data analysis software:
Check main software analysis tools
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
Open/free qualitative data analysis software (CAQDAS)
Check comparison table from NYU
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://atlasti.com
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://www.qsrinternational.com/
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://www.dedoose.com
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://www.maxqda.com
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://www.quirkos.com
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
IRML's website
Step 7: Data analysis in quantitative studies
Quantitative data analysis is a systematic approach in which numerical data is collected and/or the researcher transforms what is collected or observed into numerical data. It often describes a situation or event, answering the 'what' and 'how many' questions you may have about something. This is research which involves measuring or counting attributes (i.e. quantities).
Quantitative data analysis can be conducted following the next steps (CIRT, 2019):
1- Identification of the levels or scales of measurement
2- Preliminary Analyses (descriptive statistics)
3- Inferential statistics
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
The first step in quantitative data analysis is to identify the levels or scales of measurement as nominal, ordinal, interval or ratio. The level of measurement refers to the relationship among the values that are assigned to the attributes for a variable. This is an important first step because it will help you determine how best to organize the data. The data can typically be entered into a spreadsheet and organized or “coded” in some way that begins to give meaning to the data.
For instance, for the variable "party affiliation," we have three relevant attributes: Republican; democrat, and; independent. We can arbitrarily assign the values 1, 2 and 3 to the previous three attributes. The level of measurement describes the relationship among these three values.
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
In this particular case we only use the values (1, 2 & 3) as a shorter name for the attribute. We don't assume that republicans are in first place or have the highest priority because of having the value #1. We could describe the level of measurement as "nominal." -Nominal Scale: The nominal scales is essentially a type of coding that simply puts people, events, perceptions, objects or attributes into categories based on a common trait or characteristic. The coding can be accomplished by using numbers, letters, colors, labels or any symbol that can distinguish between the groups. The nominal scale is the lowest form of a measurement because it is used simply to categorize and not to capture additional information. Some examples include distinguishing between males and females; types of religious affiliations, etc
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
-Ordinal Scale: The ordinal scale differs from the nominal scale in that it ranks the data from lowest to highest and provides information regarding where the data points lie in relation to one another. An ordinal scale typically uses non-numerical categories such as low, medium and high to demonstrate the relationships between the data points. Ordinal scales do not provide information regarding the magnitude of the difference between the data points or rankings.
An example could be the T-shirt sizes (small, medium, large).
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
-Interval Scale: An interval scale is one in which the actual distances, or intervals between the categories or points on the scale can be compared. The distance between the numbers or units on the scale are equal across the scale.
An example would be a temperature scale, such as the Farenheit scale. The distance between 20 degrees and 40 degrees is the same as between 60 degrees and 80 degrees. A distinguishing feature of interval scales is that there is no absolute zero point because the key is simply the consistent distance or interval between categories or data points.
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
-Ratio Scale: The ratio scale contains the most information about the values in a study. It contains all of the information of the other three categories because it categorizes the data, places the data along a continuum so that researchers can examine categories or data points in relation to each other, and the data points or categories are equal distances or intervals apart. However, the difference is the ratio scale also contains a non-arbitrary absolute zero point. The lowest data point collected serves as a meaningful absolute zero point which allows for interpretation of ratio comparisons.
Time is one example of the use of a ration measurement scale in a study because it is divided into equal intervals and a ratio comparison can be made. For example, 20 minutes is twice as long as 10 minutes.
IRML's website
2- Preliminary Analyses (descriptive statistics) (CIRT, 2019)
The second step in the data analysis would be to use descriptive statistics to summarize or “describe” the data. It can be difficult to identify patterns or visualize what the data is showing if you are just looking at raw data. Following is a list of commonly used descriptive statistics: -Frequencies – a count of the number of times a particular score or value is found in the data set -Percentages – used to express a set of scores or values as a percentage of the whole -Mean – numerical average of the scores or values for a particular variable -Mode – the most common score or value for a particular variable -Minimum and maximum values (range) – the highest and lowest values or scores for any variable
IRML's website
3- Inferential statistics (CIRT, 2019)
If you want to utilize your data to make inferences or predictions about the population, you will need to go further and use inferential statistics. Inferential statistics examine the differences and relationships between two or more samples of the population. These are more complex analyses and are looking for significant differences between variables and the sample groups of the population. Inferential statistics allow you test hypotheses and generalize results to population as whole. Following is a list of basic inferential statistical tests:
Correlation
Analysis of Variance (ANOVA)
Regression
IRML's website
3- Inferential statistics (CIRT, 2019)
Correlation: It seeks to describe the nature of a relationship between two variables, such as strong, negative positive, weak, or statistically significant. An important thing to remember when using correlations is that a correlation does not explain causation. A correlation merely indicates that a relationship or pattern exists, but it does not mean that one variable is the cause of the other.
For example, you might see a strong positive correlation between participation in the summer program and students’ grades the following school year; however, the correlation will not tell you if the summer program is the reason why students’ grades were higher.
IRML's website
3- Inferential statistics (CIRT, 2019)
Analysis of Variance (ANOVA): It tries to determine whether or not the means of two sampled groups is statistically significant or due to random chance. For example, the test scores of two groups of students are examined and proven to be significantly different. The ANOVA will tell you if the difference is significant, but it does not speculate regarding “why”.
For example, an analysis of variance will help you determine if the high school grades of those students who participated in the summer program are significantly different from the grades of students who did not participate in the program.
IRML's website
3- Inferential statistics (CIRT, 2019)
Regression: It is used to determine whether one variable is a predictor of another variable. For example, a regression analysis may indicate to you whether or not participating in a test preparation program results in higher ACT scores for high school students. It is important to note that regression analysis are like correlations in that causation cannot be inferred from the analyses.
For example, a regression would help you determine if the length of participation (number of weeks) in the summer program is actually predictor of students’ high school grades the following year. Like correlations, causation can not be inferred from regression.
IRML's website
3- Inferential statistics (CIRT, 2019)
Finally, the type of data analysis to be conducted, will also depend on the number of variables in the study. Studies may be univariate, bivariate or multivariate in nature. -Univariate studies: The description of patterns found in this type of data can be made by drawing conclusions using central tendency measures (mean, median and mode), dispersion or spread of data (range, minimum, maximum, quartiles, variance and standard deviation) and by using frequency distribution tables, histograms, pie charts, frequency polygon and bar charts. -Bivariate studies: bivariate data analysis involves comparisons, relationships, causes and explanations. These variables are often plotted on X and Y axis on the graph for better understanding of data and one of these variables is independent while the other is dependent.
IRML's website
3- Inferential statistics (CIRT, 2019)
-Multivariate: It is similar to bivariate but contains more than one dependent variable. The ways to perform analysis on this data depends on the goals to be achieved.Some of the techniques are regression analysis,path analysis,factor analysis and multivariate analysis of variance (MANOVA). Multivariate analysis of variance (MANOVA) is simply an ANOVA with several dependent variables. That is to say, ANOVA tests for the difference in means between two or more groups, while MANOVA tests for the difference in two or more vectors of means. .
IRML's website
Resources
Data Analysis in Qualitative Studies
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
Library Resources
Data Analysis in Quantitative Studies
Library Resources
Describing single variables
Describing Statistical Relationships
Conducting Quantitative Analyses
IRML's website
Resources for Data Analysis in Quantitative Studies
IRML's website
Resources for Data Analysis in Quantitative Studies
IRML's website
Resources for Data Analysis in Quantitative Studies
IRML's website
Resources for Data Analysis in Quantitative Studies
IRML's website
Resources for Data Analysis in Qualitative Studies
IRML's website
Step 6: Data Gathering Methods
The following AI tools can assist you in step 7 of the process of generating your design: AI data analysis is on the rise. For instance, the AI module of Atlas.ti can be used to analyze qualitative data.
Step 7-Data Analysis
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Transcript
Step 7: Data Analysis
The seventh step in the process of generating a well-informed research design has to do with the selection and definition of the data analysis strategies that will be used in your study. Please check the resources below depending on the approach to research that you are following.
Data Analysis in Qualitative Studies
Data Analysis in Quantitative Studies
Resources
IRML's website
Step 7: Data Analysis in qualitative studies
Analyzing text and multiple other forms of data (images, videos, social media posts, etc) presents a challenging task for qualitative researchers. Moreover, deciding how to represent the data in tables, matrices, and narrative form, adds to the challenge.
The processes of data collection, data analysis, and report writing are not distinct steps in a research study; they are interrelated and often go on simultaneously in a research project.
IRML's website
The central steps of Data Analysis in Qualitative Research are:
1. Preparing and organizing the data (i.e., text data as in transcripts, or image data as in photographs) for analysis 2. Coding the data (reducing the data into meaningful segments and assigning names for the segments) 3. Combining the codes into broader categories or themes 4. Representing, displaying and making comparisons in the data graphs, tables, and charts.
IRML's website
Creswell (2007) proposes the following data analysis Spiral for qualitative studies
1. Data managing
At an early stage in the analysis process, researchers organize their data into file folders, index cards, or computer files. Besides organizing files, researchers convert their files to appropriate text units (e.g., a word, a sentence, an entire story) for analysis either by hand or by computer. Materials must be easily located in large databases of text (or images).
IRML's website
2. Reading, Memoing
Researchers continue analysis by getting a sense of the whole database. Agar (1980), suggests that researchers "... read the transcripts in their entirety several times. Immerse yourself in the details, trying to get a sense of the data as a whole before breaking it into parts". Writing memos in the margins of fieldnotes or transcripts or under photographs helps in this initial process of exploring a database. Memos are short phrases, ideas, or key concepts that occur to the reader. This process consists of moving from the reading and memoing loop into the spiral to the describing, classifying, and interpreting loop.
IRML's website
3. Describing, classifying, interpreting
In this loop, code or category formation represents the heart of qualitative data analysis. Here researchers describe in detail, develop themes or dimensions through some classification system, and provide an interpretation in light of their own views or views of perspectives in the literature. During this process of describing, classifying and interpreting, qualitative researchers develop codes or categories and sort text or visual images into categories.
What is a Code? A code in qualitative inquiry is most often a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data (Saldaña, 2009). When codes are applied and reapplied to qualitative data, you are codifying – a process that permits data to be “segregated, grouped, regrouped and relinked in order to consolidate meaning and explanation” (Grabich, 2007).
IRML's website
3. Describing, classifying, interpreting
Bernard (2006) succinctly states that analysis “is the search for patterns in data and for ideas that help explain why those patterns are there in the first place”. Coding is thus a method that enables you to organize and group similarly coded data into categories or “families” because they share some characteristic – the beginning of a pattern.
4. Representing, visualizing
In the final phase of the spiral defined by Creswell (2007), researchers present the data, a packaging of what was found in text, tabular, or figure form. For example, creating a visual image of the information. At this point, the researcher might obtain feedback on the initial summaries by taking information back to informants.
IRML's website
4. Representing, visualizing
The following visual representation was generated using Atlas.ti. It shows the different codes and families of codes obtained in the analysis of a dataset regarding the impact advanced degrees of teachers have on student's achievement.
IRML's website
From the previous 4 steps described by Creswell (2007), we will now focus our attention in the third one (Describing, classifying, interpreting), particularly in the mechanics of coding.
Saldaña (2009) defines the following types of coding:
Open coding: Basically, you read through your data several times and then start to create tentative labels for chunks of data that summarize what you see happening (not based on existing theory – just based on the meaning that emerges from the data). Record examples of participants’ words and establish properties of each code (see my charts below). Axial coding: Axial coding consists of identifying relationships among the open codes. What are the connections among the codes? This will be easier to understand when you see the last chart of this blog post. Selective coding: It consists in figuring out the core variable that includes all of the data. Then you have to reread the transcripts and selectively code any data that relates to the core variable you identified.
IRML's website
Example of Open Coding
Example of Axial Coding
IRML's website
Example of Selective Coding
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
Nowadays, most qualitative researchers use specific software to assist the analysis process. It is what we call Computer-assisted (or aided) qualitative data analysis software (CAQDAS). It offers tools that assist with qualitative research such as transcription analysis, coding and text interpretation.
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
Nowadays, most qualitative researchers use specific software to assist the analysis process. It is what we call Computer-assisted (or aided) qualitative data analysis software (CAQDAS). It offers tools that assist with qualitative research such as transcription analysis, coding and text interpretation.
The following clip presents a summary of the most used qualitative data analysis software:
Check main software analysis tools
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
Open/free qualitative data analysis software (CAQDAS)
Check comparison table from NYU
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://atlasti.com
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://www.qsrinternational.com/
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://www.dedoose.com
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://www.maxqda.com
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
https://www.quirkos.com
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
IRML's website
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
IRML's website
Step 7: Data analysis in quantitative studies
Quantitative data analysis is a systematic approach in which numerical data is collected and/or the researcher transforms what is collected or observed into numerical data. It often describes a situation or event, answering the 'what' and 'how many' questions you may have about something. This is research which involves measuring or counting attributes (i.e. quantities).
Quantitative data analysis can be conducted following the next steps (CIRT, 2019):
1- Identification of the levels or scales of measurement
2- Preliminary Analyses (descriptive statistics)
3- Inferential statistics
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
The first step in quantitative data analysis is to identify the levels or scales of measurement as nominal, ordinal, interval or ratio. The level of measurement refers to the relationship among the values that are assigned to the attributes for a variable. This is an important first step because it will help you determine how best to organize the data. The data can typically be entered into a spreadsheet and organized or “coded” in some way that begins to give meaning to the data.
For instance, for the variable "party affiliation," we have three relevant attributes: Republican; democrat, and; independent. We can arbitrarily assign the values 1, 2 and 3 to the previous three attributes. The level of measurement describes the relationship among these three values.
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
In this particular case we only use the values (1, 2 & 3) as a shorter name for the attribute. We don't assume that republicans are in first place or have the highest priority because of having the value #1. We could describe the level of measurement as "nominal." -Nominal Scale: The nominal scales is essentially a type of coding that simply puts people, events, perceptions, objects or attributes into categories based on a common trait or characteristic. The coding can be accomplished by using numbers, letters, colors, labels or any symbol that can distinguish between the groups. The nominal scale is the lowest form of a measurement because it is used simply to categorize and not to capture additional information. Some examples include distinguishing between males and females; types of religious affiliations, etc
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
-Ordinal Scale: The ordinal scale differs from the nominal scale in that it ranks the data from lowest to highest and provides information regarding where the data points lie in relation to one another. An ordinal scale typically uses non-numerical categories such as low, medium and high to demonstrate the relationships between the data points. Ordinal scales do not provide information regarding the magnitude of the difference between the data points or rankings.
An example could be the T-shirt sizes (small, medium, large).
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
-Interval Scale: An interval scale is one in which the actual distances, or intervals between the categories or points on the scale can be compared. The distance between the numbers or units on the scale are equal across the scale.
An example would be a temperature scale, such as the Farenheit scale. The distance between 20 degrees and 40 degrees is the same as between 60 degrees and 80 degrees. A distinguishing feature of interval scales is that there is no absolute zero point because the key is simply the consistent distance or interval between categories or data points.
IRML's website
1- Identification of the levels or scales of measurement (CIRT, 2019)
-Ratio Scale: The ratio scale contains the most information about the values in a study. It contains all of the information of the other three categories because it categorizes the data, places the data along a continuum so that researchers can examine categories or data points in relation to each other, and the data points or categories are equal distances or intervals apart. However, the difference is the ratio scale also contains a non-arbitrary absolute zero point. The lowest data point collected serves as a meaningful absolute zero point which allows for interpretation of ratio comparisons.
Time is one example of the use of a ration measurement scale in a study because it is divided into equal intervals and a ratio comparison can be made. For example, 20 minutes is twice as long as 10 minutes.
IRML's website
2- Preliminary Analyses (descriptive statistics) (CIRT, 2019)
The second step in the data analysis would be to use descriptive statistics to summarize or “describe” the data. It can be difficult to identify patterns or visualize what the data is showing if you are just looking at raw data. Following is a list of commonly used descriptive statistics: -Frequencies – a count of the number of times a particular score or value is found in the data set -Percentages – used to express a set of scores or values as a percentage of the whole -Mean – numerical average of the scores or values for a particular variable -Mode – the most common score or value for a particular variable -Minimum and maximum values (range) – the highest and lowest values or scores for any variable
IRML's website
3- Inferential statistics (CIRT, 2019)
If you want to utilize your data to make inferences or predictions about the population, you will need to go further and use inferential statistics. Inferential statistics examine the differences and relationships between two or more samples of the population. These are more complex analyses and are looking for significant differences between variables and the sample groups of the population. Inferential statistics allow you test hypotheses and generalize results to population as whole. Following is a list of basic inferential statistical tests:
Correlation
Analysis of Variance (ANOVA)
Regression
IRML's website
3- Inferential statistics (CIRT, 2019)
Correlation: It seeks to describe the nature of a relationship between two variables, such as strong, negative positive, weak, or statistically significant. An important thing to remember when using correlations is that a correlation does not explain causation. A correlation merely indicates that a relationship or pattern exists, but it does not mean that one variable is the cause of the other.
For example, you might see a strong positive correlation between participation in the summer program and students’ grades the following school year; however, the correlation will not tell you if the summer program is the reason why students’ grades were higher.
IRML's website
3- Inferential statistics (CIRT, 2019)
Analysis of Variance (ANOVA): It tries to determine whether or not the means of two sampled groups is statistically significant or due to random chance. For example, the test scores of two groups of students are examined and proven to be significantly different. The ANOVA will tell you if the difference is significant, but it does not speculate regarding “why”.
For example, an analysis of variance will help you determine if the high school grades of those students who participated in the summer program are significantly different from the grades of students who did not participate in the program.
IRML's website
3- Inferential statistics (CIRT, 2019)
Regression: It is used to determine whether one variable is a predictor of another variable. For example, a regression analysis may indicate to you whether or not participating in a test preparation program results in higher ACT scores for high school students. It is important to note that regression analysis are like correlations in that causation cannot be inferred from the analyses.
For example, a regression would help you determine if the length of participation (number of weeks) in the summer program is actually predictor of students’ high school grades the following year. Like correlations, causation can not be inferred from regression.
IRML's website
3- Inferential statistics (CIRT, 2019)
Finally, the type of data analysis to be conducted, will also depend on the number of variables in the study. Studies may be univariate, bivariate or multivariate in nature. -Univariate studies: The description of patterns found in this type of data can be made by drawing conclusions using central tendency measures (mean, median and mode), dispersion or spread of data (range, minimum, maximum, quartiles, variance and standard deviation) and by using frequency distribution tables, histograms, pie charts, frequency polygon and bar charts. -Bivariate studies: bivariate data analysis involves comparisons, relationships, causes and explanations. These variables are often plotted on X and Y axis on the graph for better understanding of data and one of these variables is independent while the other is dependent.
IRML's website
3- Inferential statistics (CIRT, 2019)
-Multivariate: It is similar to bivariate but contains more than one dependent variable. The ways to perform analysis on this data depends on the goals to be achieved.Some of the techniques are regression analysis,path analysis,factor analysis and multivariate analysis of variance (MANOVA). Multivariate analysis of variance (MANOVA) is simply an ANOVA with several dependent variables. That is to say, ANOVA tests for the difference in means between two or more groups, while MANOVA tests for the difference in two or more vectors of means. .
IRML's website
Resources
Data Analysis in Qualitative Studies
Computer-assisted (or aided) qualitative data analysis software (CAQDAS)
Library Resources
Data Analysis in Quantitative Studies
Library Resources
Describing single variables
Describing Statistical Relationships
Conducting Quantitative Analyses
IRML's website
Resources for Data Analysis in Quantitative Studies
IRML's website
Resources for Data Analysis in Quantitative Studies
IRML's website
Resources for Data Analysis in Quantitative Studies
IRML's website
Resources for Data Analysis in Quantitative Studies
IRML's website
Resources for Data Analysis in Qualitative Studies
IRML's website
Step 6: Data Gathering Methods
The following AI tools can assist you in step 7 of the process of generating your design: AI data analysis is on the rise. For instance, the AI module of Atlas.ti can be used to analyze qualitative data.