Generalization in qualitative research allows researchers to have expectations and sometimes make predictions, although a generalization may not be true in every case. There is seldom justification for generalizing findings from a particular qualitative study, so replication is important. Not only ideas but also skills and images can be generalized according to Eisner. In quantitative research, researchers generalize from a sample to the population, while in qualitative research practitioners determine if findings apply to their situation. Qualitative investigators are less definitive and certain in their conclusions, which are viewed as ideas to explore further rather than absolute truths.
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
Characteristics of a Good Sample
Representativeness
Absence of sampling error
Economically viable
Generalized and applicable
Goal oriented
Proportional
Randomly Selected
Actual information provider
Practical
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
Characteristics of a Good Sample
Representativeness
Absence of sampling error
Economically viable
Generalized and applicable
Goal oriented
Proportional
Randomly Selected
Actual information provider
Practical
Hypothesis -Concept Sources Types
Hypothesis
It is a tentative prediction about the nature of the relationship between two or more variables.
It is a tentative explanation of the research problem
Hypotheses are always in declarative sentence form
An hypothesis is a statement or explanation that is suggested by knowledge or observation but has not, yet, been proved or disproved
Sources of hypothesis
Experience of researcher
Review of literature
Findings of the pilot study
Interaction with knowledgeable persons of the concerned field
Knowledge of culture and society
Creative thinking and imagination of researcher
Types of Hypotheses
Directional Hypotheses / One tailed Hypothesis
Non-Directional Hypotheses / Two tailed Hypothesis
Null Hypotheses
Directional Hypotheses / One Tailed Hypothesis
A directional hypothesis is a prediction made by a researcher regarding a positive or negative change, relationship, or difference between two variables /two groups or conditions
directional hypothesis predicts the nature of the effect of the independent variable on the dependent variable.
It is often symbolized as H1
Non-Directional Hypotheses / Two Tailed Hypothesis
A non-directional simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc.
non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified.
Null Hypotheses
A null hypothesis is a hypothesis that says there is no statistical significance between the two variables.
null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other).
It is the hypothesis that the researcher is trying to disprove.
the null hypothesis is a statement of
-‘no effect’ or ‘no difference’
It is often symbolized as H0.
Examples
“ In a clinical trial of a new drug with the current drug ”
We would write Null Hypotheses (H0):
H0 : there is no difference between the two drugs.
We would write Directional Hypotheses (H1):
H1 : the new drug is better than the current drug.
We would write Non-Directional Hypothesis:
the two drugs have different effects, on average.
To those who would like to have a copy of this slide, just email me at martzmonette@yahoo.com and please tell me why would you want this presentation. Thank you very much and GOD BLESS YOU
Hypothesis -Concept Sources Types
Hypothesis
It is a tentative prediction about the nature of the relationship between two or more variables.
It is a tentative explanation of the research problem
Hypotheses are always in declarative sentence form
An hypothesis is a statement or explanation that is suggested by knowledge or observation but has not, yet, been proved or disproved
Sources of hypothesis
Experience of researcher
Review of literature
Findings of the pilot study
Interaction with knowledgeable persons of the concerned field
Knowledge of culture and society
Creative thinking and imagination of researcher
Types of Hypotheses
Directional Hypotheses / One tailed Hypothesis
Non-Directional Hypotheses / Two tailed Hypothesis
Null Hypotheses
Directional Hypotheses / One Tailed Hypothesis
A directional hypothesis is a prediction made by a researcher regarding a positive or negative change, relationship, or difference between two variables /two groups or conditions
directional hypothesis predicts the nature of the effect of the independent variable on the dependent variable.
It is often symbolized as H1
Non-Directional Hypotheses / Two Tailed Hypothesis
A non-directional simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc.
non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified.
Null Hypotheses
A null hypothesis is a hypothesis that says there is no statistical significance between the two variables.
null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other).
It is the hypothesis that the researcher is trying to disprove.
the null hypothesis is a statement of
-‘no effect’ or ‘no difference’
It is often symbolized as H0.
Examples
“ In a clinical trial of a new drug with the current drug ”
We would write Null Hypotheses (H0):
H0 : there is no difference between the two drugs.
We would write Directional Hypotheses (H1):
H1 : the new drug is better than the current drug.
We would write Non-Directional Hypothesis:
the two drugs have different effects, on average.
To those who would like to have a copy of this slide, just email me at martzmonette@yahoo.com and please tell me why would you want this presentation. Thank you very much and GOD BLESS YOU
To gain familiarity with a phenomenon or to archive new insights into it.
To portray accurately the characteristics of a particular individual, situation or a group.
To determine the frequency with which something occurs or with which it is associated with something else.
To test a hypothesis of a causal relationship between variables.
Principles of Learning: A Conceptual Framework for Domain-Specific Theories of Learning Christian J. Weibell (we'-bull) Department of Instructional Psychology and Technology Doctor of Philosophy
This study is predicated on the belief that there does not now exist, nor will there ever exist, any single theory of learning that is broad enough to account for all types of learning yet specific enough to be maximally useful in practical application. Perhaps this dichotomy is the reason for the apparent gap between existing theories of learning and the practice of instructional design. As an alternative to any supposed grand theory of learning—and following the lead of prominent thinkers in the fields of clinical psychology and language teaching—this study proposes a shift toward principles. It presents a principle-based conceptual framework of learning, and recommends use of the framework as a guide for creating domain-specific theories of learning. The purpose of this study was to review theories of learning in the behavioral, cognitive, constructive, human, and social traditions to identify principles of learning local to those theories that might represent specific instances of more universal principles, fundamentally requisite to the facilitation of learning in general. Many of the ideas reviewed have resulted from, or been supported by, direct empirical evidence. Others have been suggested based on observational or practical experience of the theorist. The ideas come from different points in time, are described from a variety of perspectives, and emphasize different aspects and types of learning; yet there are a number of common themes shared among them regarding the means by which learning occurs. It is hypothesized that such themes represent universal and fundamental principles of learning. These principles were the objective of the present study. They have been sought through careful review and analysis of both theoretical and empirical literature by methods of textual research (Clingan, 2008) and constant comparative analysis (Glaser & Strauss, 1967). By way of textual research a methodological lens was defined to identify general themes, and by way of constant comparative analysis these themes were developed further through the analysis and classification of specific instances of those themes in the texts reviewed. Ten such principles were identified: repetition, time, step size, sequence, contrast, significance, feedback, context, engagement, and agency. These ten facilitative principles were then organized in the context of a comprehensive principles-of-learning framework, which includes the four additional principles of potential, target, change, and practice. Keywords: principles of learning, domain-specific theories of learning, learning framework, learning theories, learning theory, learning principles, learning, principles, theory, theories
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
3. Value: Generalization
• Allows the researcher to have expectations.
• Sometimes to make predictions
Although a generalization might not be true
in every case, it describes, more often than not,
what we would expect to find.
4. Limitation: Qualitative Research
There is seldom methodological justification for
generalizing the findings of a particular study.
Thus, replication of qualitative research is very
important.
5. Eisner points out…
Not only ideas can be generalized, also
skills and images.
• Skill can be generalized when we apply it in a
different situation than the one which we
learned the skill.
6. Eisner points out…
Not only ideas can be generalized, also
skills and images.
• “…the creation of an image – a vivid portrait of
excellent teaching, for example – can become a
prototype that can be used in the education of
teachers or for the appraisal of teaching.”
--Eisner
7. Quantitative vs Qualitative
In Quantitative research, the researcher
generalizes from the sample under investigation
to the population of interest.
In Qualitative research, the practitioner who
judges the applicability of the research’s findings
and conclusions and who determines whether
the findings fit his/her situation.
8. Note
• Not all qualitative researchers look at
generalizing in the same way
• Some are less concerned with the question of
whether their findings are generalizable, but
rather with the question to which other settings
and subjects they are generalizable.
9. Qualitative Investigators
• They are less definitive
• They are less certain about the conclusions
drawn from the research
• Conclusions for them are viewed as ideas to be
shared, discussed and investigated further.
• Modification in different circumstances and
under different conditions will almost always be
necessary.