Inquiries, Investigations
and Immersion
Month, 202X
GROUP 5
Research Data Analysis
Research Data Analysis
According to LeCompte and Schensul (1999), research data analysis is a process a researcher uses to reduce data to a story and interpret it to derive insights. The data analysis process helps in reducing a large chunk of data into smaller fragments, which makes sense. Patton (1987) indicates that three things take place during data analysis: data are organized, data are reduced through summarization and categorization, and patterns and themes in the data are identified and linked. One of the essential things expected from researchers while analyzing data is to
stay open and remain unbiased towards unexpected patterns, expressions and results. Data analysis can sometimes give the most unexpected results. Therefore, researchers must rely on the data at hand and enjoy the process of exploring answers.
+ Info
Month, 202X
Types of Data in Research
Studies can either have qualitative data, quantitative data or both. The type of data to be collected and analyzed depends on your chosen research method. Qualitative data are non-numerical, it can be presented in words and descriptions. Qualitative variables can be nominal or ordinal. Examples are eye color, blood type, class letter grade and position in a race. On the other hand, quantitative data are numerical data, these are expressed in numbers and numerical figures. Quantitative variables can be continuous or discrete. Examples are height, weight, number of children and number of event attendees.
+ Info
Data Analysis in Qualitative Research
+ Info
Data analysis in qualitative research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects and sometimes symbols. It is typically used for exploratory research and data analysis.
Data analysis in qualitative research is manual and there are several ways to find patterns in the textual information. Some of these patterns are as follows:
Keyword context
Word-based method
is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.
is the most relied and widely used global technique for research and data analysis. The researchers usually read the available data and find repetitive or commonly used words.
Variable partitioning
is another technique to split variables so that researchers
can find more coherent descriptions and explanations from the enormous data.
Scrutiny-based technique
Metaphors
can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.
is also one of the highly recommended text analysis methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other.
Methods Used for Data Analysis in Qualitative Research
There are several methods used for analyzing data in qualitative research. The
following are some of the commonly used methods (Bhatia, 2018):
1. Content Analysis
3. Discourse Analysis
2. Narrative Analysis
4. Grounded Theory
+ Info
+ Info
+ Info
+ Info
Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
This method is used to analyze content gathered from various sources such personal interviews, field observation, and surveys. The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
When you want to explain why a particular phenomenon happened, then
using grounded theory for analyzing quality data is the best resort. Grounded theory
is applied to study data about the host of similar cases occurring in different
8
settings. When researchers are using this method, they might alter explanations or
produce new ones until they arrive at some conclusion.
Data Analysis in Quantitative Research
Data analysis in quantitative research is consists of several phases as stated below:
+ Info
Phase I: Data Validation
Data validation is done to understand if the collected sample is per the pre-set standards, or it is a biased data sample again divided into four different stages:
+ Info
Fraud: To ensure an actual human being records each response to the survey or the questionnaireScreening: To make sure each respondent is selected or chosen in compliance with the research criteria.
+ Info
Procedure: To ensure ethical standards were maintained while
collecting the data sample. Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.
+ Info
Phase II: Data Editing
More often, an extensive research data sample comes loaded with
errors. Respondents sometimes fill in some fields incorrectly or sometimes skip
them accidentally. Data editing is a process wherein the researchers have to confirm
that the provided data is free of such errors. They need to conduct necessary checks
and outlier checks to edit the raw edit and make it ready for analysis.
Phase III: Data Coding
Out of all three phases, this is the most critical phase of data
preparation associated with grouping and assigning values to the survey responses.
If a survey is completed with a 1000 sample size, the researcher will create an age
bracket to distinguish the respondents based on their age. Thus, it becomes easier
to analyze small data buckets than deal with the massive data pile.
Methods Used for Data Analysis in Quantitative Research
+ Info
Once the data is ready for analysis, researchers like you are free to use different research and data analysis methods to derive meaningful insights. Statistical techniques are the most commonly used to analyze numerical data. The method is again classified into two groups---descriptive statistics used to describe data and inferential statistics that helps in comparing the data.
+ Info
Descriptive Statistics
Descriptive statistics focuses on the central tendency and standard deviation. The
most common measure of central tendency is the mean, described as the arithmetic
average of a set of numbers. Means provide data about the average score of the participants
9
on a measure which makes it necessary in conducting quantitative research. For example,
the school wishes to determine the performance of the Grade 12 students in School
Achievement Test (SAT), the mean scores of the current year can be compared to the SAT
mean scores of the Grade 12 students from the previous year. Other measures for central
tendency are mode, the item or score in the data set that appears the most and the
median, the score in the middle of the set that divides the scores arranged either in
ascending or descending order into two groups.
+ Info
Example 1: Test scores of Grade 12 ABM (Group 1) in Research87 86 84 84 80 76 75
Mean = 87+86+84+84+80+76+75/7 = 81.71
Mode is 84 (most occurrence)
Median is 84 (middle score)
Standard deviation (SD)
+ Info
Example 2: Test Scores of Grade 12 ABM (Group 2) in Research81 77 77 77 75 73 70 69
Mean = 81+77+77+77+75+73+70+69/8 =74.87
Mode is 77 (most occurrence)
Median = 77+75/2 = 76
+ Info
On the other hand, standard deviation shows the extent of difference of the data from the mean. It provides the researcher with an idea about the similarities and differences of the respondents. Here are the steps to determine the standard deviation: Step 1. Compute the Mean.
Step 2. Compute the deviation (difference) between each respondent’s answer (data item) and the mean. The plus sign (+) appears before the number if the difference is higher; negative sign (-), if the difference is lower. Step 3. Compute the square of each deviation.
Step 4. Compute the sum of squares by adding the squared figures.
Step 5. Divide the sum of squares by the number of data items to get the variance.
Step 6. Compute the square root of variance figure to get standard deviation
Example 3: (refer to test scores in example 2)
(Step 1) Mean: 75 (rounded off)
+ Info
(Step 4) Sum of Squares: 113
(Step 5) Variance: 14
(Step 6) Standard Deviation: 3.7
As a researcher, it is important that you choose the best method for research and
data analysis suited to your research instrument and your topic. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided sample without generalizing it. For example, when you want to compare the average voting done in two different cities, differential statistics are enough. Descriptive analysis is also called a “univariate analysis” since it is commonly used to analyze a single variable.
+ Info
+ Info
Inferential Statistics
Inferential statistics is a branch of statistics that focuses on conclusions, generalizations, predictions, interpretations, hypotheses, and the like. There are a lot of hypotheses testing in this method of statistics that require you to perform complex and advanced mathematical operations (Argyrous 2011; Russell 2013; Levin & Fox 2014).
Here are two significant areas of inferential statistics:
+ Info
Estimating parameters: it takes statistics from the sample research data
and demonstrates something about the population parameter. Hypothesis test: It is about sampling research data to answer the survey
research questions. For example, Researchers might be interested to
understand if the teaching strategy is effective or not or if the multivitamin
capsules help children to perform better at school.
+ Info
These data analysis methods are used to showcase the relationship between
different variables instead of describing a single variable. It is often used when researchers
want something beyond absolute numbers to understand the relationship between
variables.
+ Info
Measures of Correlation
The following are the statistical tools to measure correlation or covariation:
1. Correlation Coefficient – This is a measure of the strength and direction of the linear relationship between variables and likewise gives the extent of dependence between two variables: meaning, the effect of one variable on the other variable. Determined by the following statistical tests for correlation coefficient:
+ Info
Spearman’s rho – the test to measure the dependence of the dependent variable on the independent variable.Pearson product-moment correlation – measures the strength and direction of the linear relationship of two variables and of the association between interval and ordinal variables.
Chi-square – is the statistical test for bivariate analysis of nominal
variables, specifically, to test the null hypothesis. It tests whether or not a relationship exists between or among variables and tells the probability that the relationship is caused by chance.T-test – evaluates the probability that the mean of the sample reflects the mean of the population from where the sample was drawn. It also tests the difference between two means: the sample mean and the
+ Info
+ Info
population mean. ANNOVA or analysis of variance also uses t-test to determine the variance or the difference between the predicted number of the sample and the actual measurement. The ANNOVA is of various types such as the following:
One-way analysis of variance – study of the effects of the independent
variable. ANCOVA (Analysis of Covariation) – study of two or more dependent
variables that are correlated with one another. MANCOVA (Multiple Analysis of Covariation) – multiple analyses of one
or more independent variables and one dependent variable to see if the
independent variables affect one another.
+ Info
2. Regression – Similar to correlation, regression determines the existence of
variable relationships, but does more than this by determining the following:
a. Which between the independent and dependent variable can signal
the presence of another variable; b. How strong the relationship between the two variables are; and c. When an independent variable is statistically significant as a
predator.
+ Info
THANK YOU!
GROUP 5
QUIZ TIME!
GROUP 5
Rose Vel Ilaya Cerillo
Created on March 14, 2022
Start designing with a free template
Discover more than 1500 professional designs like these:
View
Geniaflix Presentation
View
Vintage Mosaic Presentation
View
Shadow Presentation
View
Newspaper Presentation
View
Zen Presentation
View
Audio tutorial
View
Pechakucha Presentation
Explore all templates
Transcript
Inquiries, Investigations and Immersion
Month, 202X
GROUP 5
Research Data Analysis
Research Data Analysis
According to LeCompte and Schensul (1999), research data analysis is a process a researcher uses to reduce data to a story and interpret it to derive insights. The data analysis process helps in reducing a large chunk of data into smaller fragments, which makes sense. Patton (1987) indicates that three things take place during data analysis: data are organized, data are reduced through summarization and categorization, and patterns and themes in the data are identified and linked. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased towards unexpected patterns, expressions and results. Data analysis can sometimes give the most unexpected results. Therefore, researchers must rely on the data at hand and enjoy the process of exploring answers.
+ Info
Month, 202X
Types of Data in Research
Studies can either have qualitative data, quantitative data or both. The type of data to be collected and analyzed depends on your chosen research method. Qualitative data are non-numerical, it can be presented in words and descriptions. Qualitative variables can be nominal or ordinal. Examples are eye color, blood type, class letter grade and position in a race. On the other hand, quantitative data are numerical data, these are expressed in numbers and numerical figures. Quantitative variables can be continuous or discrete. Examples are height, weight, number of children and number of event attendees.
+ Info
Data Analysis in Qualitative Research
+ Info
Data analysis in qualitative research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects and sometimes symbols. It is typically used for exploratory research and data analysis.
Data analysis in qualitative research is manual and there are several ways to find patterns in the textual information. Some of these patterns are as follows:
Keyword context
Word-based method
is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.
is the most relied and widely used global technique for research and data analysis. The researchers usually read the available data and find repetitive or commonly used words.
Variable partitioning
is another technique to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.
Scrutiny-based technique
Metaphors
can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.
is also one of the highly recommended text analysis methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other.
Methods Used for Data Analysis in Qualitative Research
There are several methods used for analyzing data in qualitative research. The following are some of the commonly used methods (Bhatia, 2018):
1. Content Analysis
3. Discourse Analysis
2. Narrative Analysis
4. Grounded Theory
+ Info
+ Info
+ Info
+ Info
Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
This method is used to analyze content gathered from various sources such personal interviews, field observation, and surveys. The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different 8 settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.
Data Analysis in Quantitative Research
Data analysis in quantitative research is consists of several phases as stated below:
+ Info
Phase I: Data Validation Data validation is done to understand if the collected sample is per the pre-set standards, or it is a biased data sample again divided into four different stages:
+ Info
Fraud: To ensure an actual human being records each response to the survey or the questionnaireScreening: To make sure each respondent is selected or chosen in compliance with the research criteria.
+ Info
Procedure: To ensure ethical standards were maintained while collecting the data sample. Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.
+ Info
Phase II: Data Editing
More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.
Phase III: Data Coding
Out of all three phases, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses. If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets than deal with the massive data pile.
Methods Used for Data Analysis in Quantitative Research
+ Info
Once the data is ready for analysis, researchers like you are free to use different research and data analysis methods to derive meaningful insights. Statistical techniques are the most commonly used to analyze numerical data. The method is again classified into two groups---descriptive statistics used to describe data and inferential statistics that helps in comparing the data.
+ Info
Descriptive Statistics
Descriptive statistics focuses on the central tendency and standard deviation. The most common measure of central tendency is the mean, described as the arithmetic average of a set of numbers. Means provide data about the average score of the participants 9 on a measure which makes it necessary in conducting quantitative research. For example, the school wishes to determine the performance of the Grade 12 students in School Achievement Test (SAT), the mean scores of the current year can be compared to the SAT mean scores of the Grade 12 students from the previous year. Other measures for central tendency are mode, the item or score in the data set that appears the most and the median, the score in the middle of the set that divides the scores arranged either in ascending or descending order into two groups.
+ Info
Example 1: Test scores of Grade 12 ABM (Group 1) in Research87 86 84 84 80 76 75 Mean = 87+86+84+84+80+76+75/7 = 81.71 Mode is 84 (most occurrence) Median is 84 (middle score) Standard deviation (SD)
+ Info
Example 2: Test Scores of Grade 12 ABM (Group 2) in Research81 77 77 77 75 73 70 69 Mean = 81+77+77+77+75+73+70+69/8 =74.87 Mode is 77 (most occurrence) Median = 77+75/2 = 76
+ Info
On the other hand, standard deviation shows the extent of difference of the data from the mean. It provides the researcher with an idea about the similarities and differences of the respondents. Here are the steps to determine the standard deviation: Step 1. Compute the Mean. Step 2. Compute the deviation (difference) between each respondent’s answer (data item) and the mean. The plus sign (+) appears before the number if the difference is higher; negative sign (-), if the difference is lower. Step 3. Compute the square of each deviation. Step 4. Compute the sum of squares by adding the squared figures. Step 5. Divide the sum of squares by the number of data items to get the variance. Step 6. Compute the square root of variance figure to get standard deviation
Example 3: (refer to test scores in example 2) (Step 1) Mean: 75 (rounded off)
+ Info
(Step 4) Sum of Squares: 113 (Step 5) Variance: 14 (Step 6) Standard Deviation: 3.7 As a researcher, it is important that you choose the best method for research and data analysis suited to your research instrument and your topic. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided sample without generalizing it. For example, when you want to compare the average voting done in two different cities, differential statistics are enough. Descriptive analysis is also called a “univariate analysis” since it is commonly used to analyze a single variable.
+ Info
+ Info
Inferential Statistics
Inferential statistics is a branch of statistics that focuses on conclusions, generalizations, predictions, interpretations, hypotheses, and the like. There are a lot of hypotheses testing in this method of statistics that require you to perform complex and advanced mathematical operations (Argyrous 2011; Russell 2013; Levin & Fox 2014).
Here are two significant areas of inferential statistics:
+ Info
Estimating parameters: it takes statistics from the sample research data and demonstrates something about the population parameter. Hypothesis test: It is about sampling research data to answer the survey research questions. For example, Researchers might be interested to understand if the teaching strategy is effective or not or if the multivitamin capsules help children to perform better at school.
+ Info
These data analysis methods are used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.
+ Info
Measures of Correlation
The following are the statistical tools to measure correlation or covariation: 1. Correlation Coefficient – This is a measure of the strength and direction of the linear relationship between variables and likewise gives the extent of dependence between two variables: meaning, the effect of one variable on the other variable. Determined by the following statistical tests for correlation coefficient:
+ Info
Spearman’s rho – the test to measure the dependence of the dependent variable on the independent variable.Pearson product-moment correlation – measures the strength and direction of the linear relationship of two variables and of the association between interval and ordinal variables.
Chi-square – is the statistical test for bivariate analysis of nominal variables, specifically, to test the null hypothesis. It tests whether or not a relationship exists between or among variables and tells the probability that the relationship is caused by chance.T-test – evaluates the probability that the mean of the sample reflects the mean of the population from where the sample was drawn. It also tests the difference between two means: the sample mean and the
+ Info
+ Info
population mean. ANNOVA or analysis of variance also uses t-test to determine the variance or the difference between the predicted number of the sample and the actual measurement. The ANNOVA is of various types such as the following:
One-way analysis of variance – study of the effects of the independent variable. ANCOVA (Analysis of Covariation) – study of two or more dependent variables that are correlated with one another. MANCOVA (Multiple Analysis of Covariation) – multiple analyses of one or more independent variables and one dependent variable to see if the independent variables affect one another.
+ Info
2. Regression – Similar to correlation, regression determines the existence of variable relationships, but does more than this by determining the following:
a. Which between the independent and dependent variable can signal the presence of another variable; b. How strong the relationship between the two variables are; and c. When an independent variable is statistically significant as a predator.
+ Info
THANK YOU!
GROUP 5
QUIZ TIME!