The document discusses moderation in research methods. It defines moderation as a third variable that changes the relationship between two related variables. A moderator variable interacts with an independent variable to influence a dependent variable. The document provides examples of moderation and outlines the Aiken and West approach for testing moderation, which involves regressing the dependent variable on the independent variable, moderator, and an interaction term between the two. Two examples of studies examining potential moderation effects are also described.
It gives you insight into the meaning of variables and their types such as Independent variables
Dependent variables
Intervening variables
Moderating variables
Control variables
Extraneous variables
Quantitative variables
Qualitative variables
Confounding variables
Composite variables
These slides discuss about the concept and definition of variables, variables in research, operationalisation, types and functions of variables and measurement scales.
It gives you insight into the meaning of variables and their types such as Independent variables
Dependent variables
Intervening variables
Moderating variables
Control variables
Extraneous variables
Quantitative variables
Qualitative variables
Confounding variables
Composite variables
These slides discuss about the concept and definition of variables, variables in research, operationalisation, types and functions of variables and measurement scales.
Variables in social science research and its measurement pptAbhijeetSatpathy2
variables in social science research and its measurement describes the various types of variables in social sciences with examples and the measurement of variables.
Research Variables are the variables affecting one's research study. They are the Independent Variable, Dependent Variable, Constant/Controlled Variable, Extraneous Variables and Intervening Variables.
A measurable characteristic that varies and may change from group to group, person to person, or even within one person over time.
Variable is a logical grouping of attributes, characteristics or qualities that describe an object. It may be either height, weight, anxiety levels, body temperature, income and so on.
Variable is frequently used in quantitative research projects pertinent to define and identify variables.
A variable incites excitement in any research than constants as it facilitate accurate explanation of relationship between the variables.
This presentation will provide relevant information about research methodology and variables and types of variables,Dissertation and it’s Etymology,Sources of Data
Major Approches in mathodology
Qualitative
Quantitative
Mixed method
Participatory
Variables: Types and their Operational Definitions
Unit III: Problem identification formulation of research objectives and hypothesis (as part of M.Optom Curriculum of Pokhara University, Nepal)
An introduction to mediation analysis using SPSS software (specifically, Andrew Hayes' PROCESS macro). This was a workshop I gave at the Crossroads 2015 conference at Dalhousie University, March 27, 2015.
Variables in social science research and its measurement pptAbhijeetSatpathy2
variables in social science research and its measurement describes the various types of variables in social sciences with examples and the measurement of variables.
Research Variables are the variables affecting one's research study. They are the Independent Variable, Dependent Variable, Constant/Controlled Variable, Extraneous Variables and Intervening Variables.
A measurable characteristic that varies and may change from group to group, person to person, or even within one person over time.
Variable is a logical grouping of attributes, characteristics or qualities that describe an object. It may be either height, weight, anxiety levels, body temperature, income and so on.
Variable is frequently used in quantitative research projects pertinent to define and identify variables.
A variable incites excitement in any research than constants as it facilitate accurate explanation of relationship between the variables.
This presentation will provide relevant information about research methodology and variables and types of variables,Dissertation and it’s Etymology,Sources of Data
Major Approches in mathodology
Qualitative
Quantitative
Mixed method
Participatory
Variables: Types and their Operational Definitions
Unit III: Problem identification formulation of research objectives and hypothesis (as part of M.Optom Curriculum of Pokhara University, Nepal)
An introduction to mediation analysis using SPSS software (specifically, Andrew Hayes' PROCESS macro). This was a workshop I gave at the Crossroads 2015 conference at Dalhousie University, March 27, 2015.
RESEARCH DESIGN , Sampling Designs , Dependent and Independent Variables, Extraneous Variables, Hypothesis, Exploratory Research Design, Descriptive and Diagnostic Research
These are some slides I use in my Multivariate Statistics course to teach psychology graduate student the basics of structural equation modeling using the lavaan package in R. Topics are at an introductory level, for someone without prior experience with the topic.
Webscience is an affiliate project of Sciencetutors. All Slideshare presentation by sciencetutors + Webscience. Please for more resources visit: www.sciencetutors.zoomshare.com or www.slideshare.net/sciencetutors.
Thanks Ivan for Sciencetutors
This presentation is for educational purpose only. I do not own the rights to written material or pictures or illustrations used.
This is being uploaded for students who are in search of, or trying to understand how a quasi-experimental research design should look like.
Tribhuvan University, Nepal
Masters in Arts
Population Studies
Research method in Population analysis
Validity and Threats to validity
If any mistakes, feel free to suggest me for the improvement.
Hope its useful for reference
thank You :)
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
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Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
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4. Prioritize Data Quality with Automation:
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5. Cultivate a Data-Driven Mindset:
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By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
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Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
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Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. Terminology
• Criterion Variable (dependant variable)
• Predictor Variable ( Independent variable)
y = criterion
variable
x = predictor
variable
Worshop on Research Methods at GUDMS
3. Moderation
• A third variable changes the relationship
between two related variable.
In other words
• Relationship between two variables changes
with the level of another variable of construct
Worshop on Research Methods at GUDMS
8. • Pure Moderator
Predictor
Criterion
Moderator
Interaction
Not Significant
Significant
Worshop on Research Methods at GUDMS
9. • Quasi Moderator
Predictor
Criterion
Moderator
Interaction
Significant
Significant
Worshop on Research Methods at GUDMS
10. Moderator v/s Mediation
• Mediation – significant relationship of
mediator between independent variable and
dependant variable
IV
Mediator
DV
Worshop on Research Methods at GUDMS
11. Moderator v/s Mediation
• Moderator – significant / no significant
relationship between moderator variable and
dependant variable
Interaction
IV
Mod
DV
Χ
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting
interactions. Sage.
Worshop on Research Methods at GUDMS
12. Moderator v/s Mediation
• Mediator variable is consequent of predictor
variable.
IV
Mediator
DV
Worshop on Research Methods at GUDMS
13. Moderator v/s Mediation
• Moderator variable is at same level as
predictor variable.
Moderator
IV
DV
x
Worshop on Research Methods at GUDMS
14. Classification of Moderator
• Non- Metric moderators ( Gender, race etc)
• Metric Moderators (level of reward,
motivation etc)
Worshop on Research Methods at GUDMS
15. Aiken and West Approach
• Step1 : Regress on dependant variable the
independent variable and moderator variable
• Step2: Center all the continuous variables
• Step3: Multiply the independent and
moderator variable and create interaction
variable
• Step 4: Regress on dependant variable the
independent variable ,moderator variable and
Interaction variable
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting
interactions. Sage. Worshop on Research Methods at GUDMS
16. Aiken and West Approach
• Step 5: Check whether interaction variable is
significant else no Moderation
• Step 6: ΔR2
• Step 7 : If moderator is continuous variable
classify into meaning full groups or classify the
same based at ± >= 1 σ level
• Step8: Plot Graphs and interpret the same
with significant slopes
Worshop on Research Methods at GUDMS
17. Example 1
• Students who were attending an orientation before
starting their first year of college.
• Students were asked to report on the number of minor
stressful events (labeled hassles) that they had recently
experienced, and also to report on their perceived level
of social support.
• They then completed a symptom checklist on the
number of symptoms they had experienced in the past
month.
• For this part of the study there were complete data on
56 participants.
Worshop on Research Methods at GUDMS
18. Example 1
Hassles
Support
symptoms
Worshop on Research Methods at GUDMS
19. Example 2
Attitude
towards
coworkers
Job
Satisfaction
Gender
Worshop on Research Methods at GUDMS
