This document provides an overview of research methodology and biostatistics. It discusses key steps in the research process including collecting literature, identifying problems, planning methodology, data collection and analysis, and reporting. Various study designs such as descriptive studies, analytical observational studies including cross-sectional, case-control and cohort studies are described. The strengths and limitations of different study types are also highlighted.
Biostatics and Research Methodology are essential components of the field of Pharmacy related research. They involve the application of statistical techniques and research methodologies to analyze and interpret data in biological, medical, and public health studies. This subject is applicable to B. Pharm as well as M. pharm students
Graphs(Biostatistics and Research Methodology) B.pharmacy(8th sem.)Pranjal Saxena
This slides contains the description about the Graphs(Histograms, Pie-Chart, Cubic Graph, Response surface Plot, Counter surface plot ) mainly Histograms with advantages, disadvantages and examples, Pie-chart with advantages, disadvantages and examples, Cubic Graph with examples, Response surface plot and Counter plot with examples and uses.
What are the applications of Biostatistics in Pharmacy?pharmacampus
Biostatistics broadly deals with statistical applications in the context of biological problems, including medicine, pharmacy, and public health. Government organizations, research institutes and industry have been extensively using statistics and biostatistics
Biostatics and Research Methodology are essential components of the field of Pharmacy related research. They involve the application of statistical techniques and research methodologies to analyze and interpret data in biological, medical, and public health studies. This subject is applicable to B. Pharm as well as M. pharm students
Graphs(Biostatistics and Research Methodology) B.pharmacy(8th sem.)Pranjal Saxena
This slides contains the description about the Graphs(Histograms, Pie-Chart, Cubic Graph, Response surface Plot, Counter surface plot ) mainly Histograms with advantages, disadvantages and examples, Pie-chart with advantages, disadvantages and examples, Cubic Graph with examples, Response surface plot and Counter plot with examples and uses.
What are the applications of Biostatistics in Pharmacy?pharmacampus
Biostatistics broadly deals with statistical applications in the context of biological problems, including medicine, pharmacy, and public health. Government organizations, research institutes and industry have been extensively using statistics and biostatistics
Statistical tests of significance and Student`s T-TestVasundhraKakkar
Statistical tests of significance is explained along with steps involve in Statistical tests of significance and types of significance test are also mentioned. Student`s T-Test is explained
Cross over design, Placebo and blinding techniques Dinesh Gangoda
A crossover design is a modified randomized block design in which each block receives more than one treatment at different dosing periods.
A block can be a patient or a group of patients.
Patients in each block receive different sequences of treatments.
A crossover design is called a complete crossover design if each sequence contains all treatments under investigation.
A placebo is a dummy medicine containing no active substance.
This substance has no therapeutic effect, used as a control in testing new drugs.
Latin- ‘ I shall please’
Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Statistical tests of significance and Student`s T-TestVasundhraKakkar
Statistical tests of significance is explained along with steps involve in Statistical tests of significance and types of significance test are also mentioned. Student`s T-Test is explained
Cross over design, Placebo and blinding techniques Dinesh Gangoda
A crossover design is a modified randomized block design in which each block receives more than one treatment at different dosing periods.
A block can be a patient or a group of patients.
Patients in each block receive different sequences of treatments.
A crossover design is called a complete crossover design if each sequence contains all treatments under investigation.
A placebo is a dummy medicine containing no active substance.
This substance has no therapeutic effect, used as a control in testing new drugs.
Latin- ‘ I shall please’
Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Archer USMLE step 3 Endocrinology lecture notes. These lecture notes are samples and are intended for use with Archer video lectures. For video lectures, please log in at http://www.ccsworkshop.com/Pay_Per_View.html
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
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All research endeavors share a common goal of furthering our understanding of the problem and thus all share certain basic stages forming a process called the research process.
Research Design, Concepts, Types, Sampling and sampling design, Measurement and Scaling, types, Error in scaling, criterion of good scale and classification of scaling
Introduction to Research & Research methodology
2. Selection and formulation of research problem
3. Research design and plan
4. Experimental designs
5. Sampling and sampling strategy or plan
6. Measurement and scaling techniques
7. Data collection methods and techniques
8. Testing of hypotheses
9. Statistical techniques for processing & analysis of data
10. Analysis, interpretation and drawing inferences
11. Report writing
Introduction
In life, there are universal laws that govern everything we do. These laws are so perfect that if you were to align yourself with them, you could have so much prosperity that it would be coming out of your ears. This is because God created the universe in the image and likeness of him. It is failure to follow the universal laws that causes one to fail. The laws that were created consisted of the following: ·
Law of Gratitude: The Law of Gratitude states that you must show gratitude for what you have. By having gratitude, you speed your growth and success faster than you normally would. This is because if you appreciate the things you have, even if they are small things, you are open to receiving more.
Law of Attraction: The Law of Attraction states that if you focus your attention on something long enough you will get it. It all starts in the mind. You think of something and when you think of it, you manifest that in your life. This could be a mental picture of a check or actual cash, but you think about it with an image.
Law of Karma: the Law of Karma states that if you go out and do something bad, it will come back to you with something bad. If you do well for others, good things happen to you. The principle here is to know you can create good or bad through your actions. There will always be an effect no matter what.
Law of Love: the Law of Love states that love is more than emotion or feeling; it is energy. It has substance and can be felt. Love is also considered acceptance of oneself or others. This means that no matter what you do in life if you do not approach or leave the situation out of love, it won't work.
Law of Allowing: The Law of Allowing states that for us to get what we want, we must be receptive to it. We can't merely say to the Universe that we want something if we don't allow ourselves to receive it. This will defeat our purpose for wanting it in the first place.
Law of Vibration: the Law of Vibration states that if you wish on something and use your thoughts to visualize it, you are halfway there to get it. To complete the cycle you must use the Law of Vibration to feel part of what you want. Do this and you'll have anything you want in life.
For everything to function properly there has to be structure. Without structure, our world, or universe, would be in utter chaos. Successful people understand universal laws and apply them daily. They may not acknowledge that to you, but they do follow the laws. There is a higher power and this higher power controls the universe and what we get out of it. People who know this, but wish to direct their own lives, follow the reasons. Successful people don't sit around and say "I'll try," they say yes and act on it.
Chapter - 1
The Law of Attraction
The law of attraction is the most powerful force in the universe. If you work against it, it can only bring you pain and misery. Successful people know this but have kept it hidden from the lower class for centuries because th
What are the Steps Involved to Design PhD Research Methods? Explain about Two...PhD Assistance
PhD Research Methodology Research Design helps in planning the research design and assist you in collecting and analyzing the needed information for your PhD research
The main objective of Research Design Methodology is to formulate a research problem that requires precise investigation for developing a working thesis.
The Research Design and Methodology is the integral part of the study that aims to explain how to drive the research and which data analysis method you will use to research. It is a framework that explains various other research theories, assumptions to give a working framework suitable for your study.
The research method includes information such as,
A framework of research such as assumptions and theories
Methods and techniques used to enhance the reliability and validity of the research work
The theoretical orientation of the research
Justification for choosing this method
Consideration and limitation of selected research method
Learn More: https://bit.ly/3kzQPVf
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Research Design constitute blue print for the collection, measurement and analysis of data.
Types of Research Designs and How to select good Research Design.
Research methodology plays a vital role in a research study in every field ART commerce Science , Engineering etc., ensuring adherence to research objectives and the effective utilization of suitable data collection and analysis tools aligned with the chosen research design.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
3. Steps in Research (Holy 11)
1. Collect review of literature/Situation Analysis
2. Identify and prioritize health problems
3. Decide aims & objectives
4. Planning Methodology
5. Execution
6. Compilation, Classification & Presentation of
data
7. Analysis
8. Test of Significance/Test of Hypothesis
9. Inferences
10. Report Writing
11. Dissemination of Report
12/08/2012 Dr. Kusum Gaur 3
4. Process of Concluding
8 7 6
Reporting Inferences Analysis
Data Collection
5
Execution
Execution
Research Problem
Define
1
for Pretest
Collection
Data
Review of Literature Methodology
4
2 3
Planning
12/08/2012 Dr. Kusum Gaur 4
5. STEP-1
DEFINITION
OF THE
RESEARCH PROBLEM
12/08/2012 Dr. Kusum Gaur 5
6. RESEARCH PROBLEM ?
Research Problem refers to some difficulty
which a researcher experiences and
wants to obtain a solution for the same.
i.e. a question or issue to be examined.
12/08/2012 Dr. Kusum Gaur 6
7. Process of Defining Problem
Analysis of the Situation
Identify & Prioritize Problems
Select & Define Problem
Statement of
Research Objectives
12/08/2012 Dr. Kusum Gaur 7
8. CRITERIA OF SELECTION
The selection of one appropriate researchable
problem out of the identified problems requires
evaluation of certain criteria.
* Internal / Personal criteria – Researcher‟s side
* External Criteria – Problem side factors
12/08/2012 Dr. Kusum Gaur 8
9. INTERNAL CRITERIA OF SELECTION
Researcher‟s Interest,
Researcher‟s Competence,
Researcher‟s own Resource:
Human Resource
Money
Material
Time
12/08/2012 Dr. Kusum Gaur 9
10. EXTERNAL CRITERIA OF SELECTION
Researchability of the problem,
Importance and Urgency,
Novelty of the Problem,
Feasibility,
Facilities,
Social Relevance
Public health Importance
12/08/2012 Dr. Kusum Gaur 10
11. DEFINE RESEARCH PROBLEM
(Title of the Research Topic)
Transforming the selected research problem into a
scientifically researchable statement.
Problem definition or Problem statement should be
clear, precise, self-explanatory and include:-
What
How
When
Where
12/08/2012 Dr. Kusum Gaur 11
12. RESEARCH OBJECTIVES
(Objectives)
Research Objectives are the statement of the
questions that is to be investigated with the goal of
answering the overall research problem.
Research Objectives should be clear and achievable.
Generally, they are written as statements, using the
word “to”
(For example, „to discover …‟, „to determine …‟, „to
establish …‟, „to find out -----‟, „to assess -----‟etc. )
Objectives should infer in the end of the study
12/08/2012 Dr. Kusum Gaur 12
13. Hypothetical Research Question
Problem:
PCR of Diabetes Mellitus is increasing very
fast during last five year
Mission:
Reduce the incidence of heart disease
Belief:
Meditation is good to reduce stress which
is an important precursor of DM
Hypothesis
H- Meditation decreases the risk of DM
12/08/2012 Dr. Kusum Gaur 13
14. Association of Garlic consumption with
coronary Artery Diseases
Aim: To Study the association of Meditation with
Diabetes Mellitus in patients attending at Medical
OPD of SMS Hospital, Jaipur (Raj) India.
Objectives:
1. To assess and compare the proportion of DM
cases in individuals doing regular meditation and
not doing meditation.
2. To find out the risk ratio of DM in individuals not
doing meditation on doing regular meditation.
15. STEP-2
REVIEW
OF
LITERATURE
12/08/2012 Dr. Kusum Gaur 15
17. What ?
REVIEW OF LITERATURE
Literature Review is the documentation
of a published and unpublished work
from secondary sources of data
in the areas of specific interest to the researcher.
12/08/2012 Dr. Kusum Gaur 17
18. Why ? - PURPOSE OF REVIEW
Tofind out already investigated problems and
those that need further investigation.
To formulate researchable hypothesis.
To gain a background knowledge
To identify data sources
To learn how others structured their reports.
12/08/2012 Dr. Kusum Gaur 18
19. Where ?
SOURCES OF LITERATURE
Books and Journals
Databases
Bibliographic Databases
Abstract Databases
Full-Text Databases
Govt. and NGO Records & Reports
Internet
On line journals: ww.articalbase.com …….
E. Databases – Popline, Medline …….
Research Dissertations / Thesis
12/08/2012 Dr. Kusum Gaur 19
21. Methodology
Study Area : Location of study - Hospital, community etc.
Study Period: Start to end of Study (maximum period
available for study should be defined)
*Selection of Study Design
* Selection of Study Population
Pre-requisits of study: Study Tools, Terminologies,
Orientation trainings etc.
*will be taken separately
12/08/2012 Dr. Kusum Gaur 21
22. Methodology……
• Study Tools for data collection: subjects, proforma,
examination, measurements, lab investigations
• Planning
Data collection, compilation, data entry
Data cleaning
Analysis plan:
• Confidentiality
• Ethical clearance: Consent from
Institutional Review Board
Observational units
12/08/2012 Dr. Kusum Gaur 22
23. Study Design
A study design is a specific plan or protocol
for conducting the study,
which allows the investigator
to translate the conceptual hypothesis
into an operational one.
12/08/2012 Dr. Kusum Gaur 23
24. Direction of Study
Backward Forward
Cross -sectional
Retrospective Prospective
3
4. Ambidirectional
12/08/2012 Dr. Kusum Gaur 24
25. Decision Tree
Intervention Done
No Yes
Observational Study Experimental Study
Comparison Group Randomization
No Yes
No Yes
Descriptive Study Analytic Study
NRCT Study RCT Study
Direction of Study
E O E O
Cohort Study E = O Case-Control Study
Cross-Sectional Study
12/08/2012 Dr. Kusum Gaur 25
26. Epidemiological Study Design
Observational Studies
Descriptive Studies
Analytic
Cross-Sectional
Case-Control
Cohort
Experimental / Interventional studies
As per Control: RCT/NRCT
As per Blinding: Single /Double Blind
As per Design: Simple/Cross-over
As per Area: Field/Clinical/Lab
12/08/2012 Dr. Kusum Gaur 26
27. Descriptive Studies
• Case reports
• Case series
• Population studies
12/08/2012 Dr. Kusum Gaur 27
28. Descriptive Studies: Uses
• Hypothesis generating
• Suggesting associations
12/08/2012 Dr. Kusum Gaur 28
29. Descriptive Type of Observational Study
• Other Name Case-Series/Population
• Unit of Study Case/Individuals
• Study Question What is happening
• Direction Of Inquiry
• Study Design
desired information
about cases/individuals is
collected
12/08/2012 Dr. Kusum Gaur 29
30. Case-Series …….
Advantages
• Easy to do
• Excellent at identifying unusual situation
• Good for generating hypotheses
Disadvantages
• Generally short-term
• Investigators self-select (bias!)
• no controls
09/03/2010 Dr. Kusum Gaur 30
32. Cross-sectional Study
• Data collected at a single point in time
• Describes associations
• Prevalence
A “Snapshot”
12/08/2012 Dr. Kusum Gaur 32
33. Cross-Sectional Study
• Other Name Prevalence Study
• Unit of Study Individual
• Study Question What is happening
• Direction of Inquiry
• Study Design Exposed
to Factor
Not
Exposed
Diseased to Factor
Population Exposed to
Factor
Non-
Disease Not
Exposed to
12/08/2012 Dr. Kusum Gaur Factor 33
34. Objectives of a Cross-Sectional Study
To find out association
12/08/2012 Dr. Kusum Gaur 34
35. Cross-sectional Study
Sample of Population
Defined Population
Regular Not doing meditation
Meditation
Prevalence of Prevalence of
DM DM
Time Frame = Present
12/08/2012 Dr. Kusum Gaur 35
36. Cross-sectional Study
E.G. Out of 1000 population if 100 were doing meditation regularly &
out of that only 2 were having DM. Remaining 900 were not doing
meditation at all, out of that 220 were having DM.
+ DM -
2 98
Meditation
+
- 220 680
12/08/2012 Dr. Kusum Gaur 36
37. Cross-Sectional Study
• Strengths
– Quick
– Cheap
• Weaknesses
– Cannot establish cause-effect
09/03/2010 Dr. Kusum Gaur 37
38. Case-Control Studies
Start with people who have disease(Cases)
Match them with controls that do not have
disease (Match Confounding)
Look back and assess exposures
12/08/2012 Dr. Kusum Gaur 38
39. Controls
A control is a standard of comparison
(confounded with variability but without effect)
for
• Effects
• Variability
12/08/2012 Dr. Kusum Gaur 39
40. Case-Control Study
• Other Name Retrospective Study
• Unit of Study Cases/Control
• Study Question What has happened
• Direction of Inquiry= F O
• Study Design
Exposed
Cases
Not
Exposed
Exposed
Control
Not
Exposed
12/08/2012 Dr. Kusum Gaur 40
41. Objective of a Case-Control Study
To find out association
To assess Risk Ratio
12/08/2012 Dr. Kusum Gaur 41
42. Case-Control Study
Cases
Regular Meditation
Patients with DM
No Meditation
Controls
Regular Meditation
Persons w/o DM
No Meditation
Past Present
12/08/2012 Dr. Kusum Gaur 42
43. The logic of Case-Control Studies
Cases differ from controls only in having the
disease
If exposure does not predispose to having
the disease, then exposure should be equally
distributed between the cases and controls.
The extent of greater previous exposure
among the cases reflects the increased risk
that exposure confers
12/08/2012 Dr. Kusum Gaur 43
44. Case-Control Studies: Strengths
• Good for rare outcomes: cancer
• Can examine relation of exposures to disease
• Useful to generate hypothesis
• Fast
• Cheap
• Provides Odds Ratio
09/03/2010 Dr. Kusum Gaur 44
45. Case-Control Studies: Weaknesses
• Cannot measure
– Incidence
– Prevalence
– Relative Risk
• Can only study one outcome
• High susceptibility to bias
09/03/2010 Dr. Kusum Gaur 45
46. Cohort Study
• Begin with disease-free individuals
• Classify patients as exposed/unexposed
• Record outcomes in both groups
• Compare outcomes using relative risk
12/08/2012 Dr. Kusum Gaur 46
47. Cohort Study
• Other Name Prospective Study / Follow-up Study/Incidence Study
• Unit of Study Individual
• Study Question What is happening
• Direction of Inquiry F O
• Study Design Diseased
•
Exposed to Not Non
Factor Diseased
Cohort
Cohort Diseased
Not
Exposed to
Factor
Non-Diseased
12/08/2012 Dr. Kusum Gaur 47
48. Logic of Cohort Study
Cohort is a group of persons sharing a
common characteristics
Differences in the rate at which exposed and
control subjects contract a disease is due to
the differences in exposure, since others are
known and similar.
12/08/2012 Dr. Kusum Gaur 48
49. Cohort Study
Prospective (usually)
Controlled
Can determine causes and incidence of
diseases as well as identify risk factors
Generally expensive, time consuming and
difficult to carry out
12/08/2012 Dr. Kusum Gaur 49
50. Steps for Cohort Study
Identify geographically defined group
Identify exposed subjects and not exposed
subjects
Follow over a specific time
Record the fraction in each group who
develop the condition of interest
Compare these fractions using RR, AR or OR
12/08/2012 Dr. Kusum Gaur 50
51. Objectives of a Cohort Study
To find out association
To assess Risk Ratio
To find out Relative Risk
To find out Attributed Risk
12/08/2012 Dr. Kusum Gaur 51
52. Prospective Cohort Study
DM
No Meditation
No DM
Cohort
DM
Regular
Meditation No DM
Present Future
12/08/2012 Dr. Kusum Gaur 52
53. Cohort Study: Strengths
• Can measure multiple outcomes
• Can adjust for confounding variables
• Can calculate Attributed Risk
09/03/2010 Dr. Kusum Gaur 53
54. Cohort Study: Weaknesses
• Expensive
• Time consuming
• Cannot study rare outcomes
• Confounding variables
09/03/2010 Dr. Kusum Gaur 54
55. Measurements of association
Cohort Study Case Control Study
•Significance Test •Significance Test
•Relative Risk •OR
•Attributable Risk
•OR
12/08/2012 Dr. Kusum Gaur 55
56. Measures of Association
Significance Test – to test significance of
difference in exposure between control and
Cases
Odds ratio - ratio of the odds of contracting
disease in given exposure
Relative Risk – Ratio between incidence
among exposed and incidence among non-
exposed
Attributed Risk – percentage of difference
between incidence among exposed and non-
exposed with incidence among exposed
RR or OR of 1 indicate no effect of exposure (equal odds)
12/08/2012 Dr. Kusum Gaur 56
57. ‘Z’ Score of Exposure Rates
Cases control
Exposed a b
a x 100
Exposure Rates = in Cases Non- c d
exposed
(P2) a+c
b x 100
Exposure Rates = in Controls P2 – P1
(P1) b+d Z Score =
SEDP
P1 Q 1 P 2 Q 2
SEDP = ------------- + --------
09/03/2010 Dr. Kusum Gaur 57
N1 N2
58. ad
ODD‟s Ratio = Times
bc
Incidence among Exposed
RR = Times
Incidence among Non-Exposed
a/a+b a (c+d)
= =
c/c+d c (a+b)
09/03/2010 Dr. Kusum Gaur 58
59. Attributed Risk
(Incidence among Exposed - Incidence among Non-Exposed)
AR = x 100
Incidence among Exposed
a
Incidence among Exposed= x 100
a+b
c
Incidence among Non-Exposed= x 100
c+d
09/03/2010 Dr. Kusum Gaur 59
60. Experimental Studies
Clinical trials provide the “gold standard” of
determining the relationship between factor
and the event
12/08/2012 Dr. Kusum Gaur 60
61. Types of Experimental Study
As per Randomization:
• Randomized Control Trials (RCT)
• Concurrent Parallel Design (RCT)
• Sequential RCT Design
• RCT with External Control
• Non – Randomized Trials (NRCT)
12/08/2012 Dr. Kusum Gaur 61
62. Types of Experimental Study….
As per Design:
• Simple
• Cross-Over Study Design
As per Study Area:
• Field Trials
• Clinical Trials
• Lab. Trials
12/08/2012 Dr. Kusum Gaur 62
63. Quality of Experimental Study
• Randomization
• Blinding
• Control
• Cross-Over
12/08/2012 Dr. Kusum Gaur 63
64. Controls in Clinical Trials
A clinical trial is a comparative, prospective
experiment conducted in human subjects
• Historical controls are better than no
controls
• Patients can serve as own controls - This is
usually beneficial as the comparison
removes patient differences
12/08/2012 Dr. Kusum Gaur 64
65. Blinding
Good practice: factors that can affect the
evaluation of outcome should not be permitted
to influence the evaluation process
Single-blind
Patient or evaluator (either of one) is blinded as
to intervention
Double-blind design
Neither patient nor outcome evaluator knows Rx
to which patient was assigned
12/08/2012 Dr. Kusum Gaur 65
66. Randomized Control Trials (RCT)
• Before and After Comparison
• Comparison with Placebo
• Comparison Of two medicine/procedure/tests
• Comparison Of > two medicine/procedure/tests
12/08/2012 Dr. Kusum Gaur 66
67. Experimental Study
• Other Name Intervention Study
• Objective To know the effect of intervention
• Unit of Study Individual meeting entry criteria
• Study Question What is happening after intervention in
both groups
• Direction of Inquiry I E
• Study Design 1(Intervention with Placebo) Positive
Outcome
Group 1/cases Intervention
Negative
Outcome
Positive
Outcome
Group
Placebo
2/control
Negative
Outcome
12/08/2012 Dr. Kusum Gaur 67
68. Clinical Trial
R Treatment
a Outcomes
Group
n
d
Study o
Population m
i
z Outcomes
e Control Group
12/08/2012 Dr. Kusum Gaur 68
69. Intervention Study - Design 2
(Comparison of Effect of Two Interventions)
Cases
Meeting
Entry criteria
Group - 1 Group -2
Intervention -1 Intervention Intervention - 2
Positive Negative Positive
Outcome Negative
Outcome Outcome Outcome
12/08/2012 Dr. Kusum Gaur 69
70. Cross Over Design
Group -1 Cases Group-2
Meeting
Entry
criteria Intervention - 2
Intervention - 1
Positive Negative
Positive Negative Outcome
Outcome Outcome
Outcome
Group -1
Group -2 Crossover
Intervention -2
Intervention -1
Positive Negative
Positive Negative
Outcome
Outcome Outcome
Outcome
12/08/2012 Dr. Kusum Gaur 70
71. Other Types of Experimental Study
• Quincy Experimental Study
• Block Experimental Study
12/08/2012 Dr. Kusum Gaur 71
72. Quincy Experimental Study
Cases
Meeting
Entry criteria
Group - 1 Group -2
Intervention Intervention No Intervention
Positive Negative Positive
Outcome Negative
Outcome Outcome Outcome
12/08/2012 Dr. Kusum Gaur 72
73. Block Experimental Study
Cases
Meeting
Entry criteria
Group -3
Group - 1
Group -2
Intervention Intervention-3
Intervention -1 Intervention
Intervention-2
Positive Positive Negative
Negative
Outcome Outcome Outcome Outcome
Positive Negative
Outcome Outcome
12/08/2012 Dr. Kusum Gaur 73
74. Steps of Experimental Study
Drawing up a Protocol
Reference Population
Sample Population
Exclusions
Randomization
Experimental Group Control Group
Manipulation/Intervention
Follow - up
12/08/2012 Assessment of Outcome
Dr. Kusum Gaur 74
76. STUDY QUESTIONS AND APPROPRIATE DESIGNS
Type of Question Appropriate Study Design
Burden of illness Field Surveys
- Prevalence Cross Sectional Survey
- Incidence Longitudinal survey
Causation, Risk & Prognosis Case Control Study,
Cohort study, RCT
Treatment Efficacy Randomized Controlled study
Diagnostic Test Evaluation Randomized Controlled study
Cost Effectiveness Randomized Controlled study
12/08/2012 Dr. Kusum Gaur 76
77. Hierarchy of Epidemiological Study Design
Establish Causality RCT
Cohort
Case Control
Cross-Sectional
Case Series
Generate Hypothesis Case Report
12/08/2012 Dr. Kusum Gaur 77
78. Methodology
Study Area : Location of study - Hospital, community
etc.
Study Period: Start to end of Study (maximum period
available for study should be defined)
*Selection of Study Design
* Selection of Study Population
Sample Size
Sampling Technique
Pre-requisits of study: Study Tools, Terminologies,
Orientation trainings etc.
12/08/2012 Dr. Kusum Gaur 78
79. Selection of study population
Whole Population
Sample Population
12/08/2012 Dr. Kusum Gaur 79
80. What is Sample ?
• A sample is a small representative
segment of a population
• Inferences drawn from a sample are
expected to be applicable for the source
population
12/08/2012 Dr. Kusum Gaur 80
81. Why do we need a sample?
To get inferences
applicable to universe
with minimum resources
12/08/2012 Dr. Kusum Gaur 81
82. Sample – Qualities
Sample is a part of population but it is true
representative of whole.
Qualities
Adequate size
Appropriate sampling technique
12/08/2012 Dr. Kusum Gaur 82
83. Factors on which SAMPLE SIZE depend:
• Population Factors
– Type of information available
• Type of study
– Type of Data
– Type of study design
– Type of sampling
– Type of Statistical Analysis for outcome needed
• Determined values of research by researcher
– Power
– Significance level
12/08/2012 Dr. Kusum Gaur 83
84. Power: Ability to detect right answer
Alpha Error: Chance to miss right answer
85. Type of Data & level of Measurements
Qualitative – Counted Facts – Nominal Data
Measured as Numbers expressed as proportions
Quantitative- Measured Facts - Numerical Data
Measured as quantity & expressed as Mean SD
*Ordinal Data – Rank Order Data
Measured as rank & expressed as Median Percentile
12/08/2012 Dr. Kusum Gaur 91
86. Sample size for Qualitative data
Z 2 PQ 4 PQ
Sample Size= ------------------- -- = ------------------
L2 L2
P= Prevalence of disease
Q = 100-P
L = allowable error
Z= 1.96 ≈ 2 for 95% CL
for descriptive/case-series type of study design
09/03/2010 Dr. Kusum Gaur 92
87. Sample size for Quantitative data
Z 2 SD 2 4 SD 2
Sample Size= ------------------- -- =----------------------
L2 L2
SD= Standard Deviation
L = allowable error
Z= 1.96 ≈ 2 for 95% CL
For Descriptive Studies only
09/03/2010 Dr. Kusum Gaur 94
88. Finite Correction
Sample Size – Finite Population (where the
population is less than 50,000)
SS
New SS = _________________
( 1 + ( SS – 1 ))Pop
89. How many controls?
n
k Here n0=No. of cases &
2n0 n n = expected no. of cases
• k = 13 / (2*11 – 13) = 13 / 9 = 1.44
• kn0 = 1.44*11 ≈ 16 controls (and 11 cases)
– Same precision as 13 controls and 13 cases
90. Sampling Design factors of sample size
Variance of Specified Sampling
Design Effect =
Variance of Simple Random Sampling
12/08/2012 Dr. Kusum Gaur 97
91. Sampling Technique effect on Sample Size
Sampling Technique Design Effect Size Multiplier
Simple Random Sampling 1
Systemic Random Sampling 1.2
Stratified Random Sampling 0.8
Cluster Random Sampling 2
12/08/2012 Dr. Kusum Gaur 98
92. Conventionally accepted
Researcher’s Estimations
Alpha Error 0.05
Power 80%
Confidence Limit 95%
12/08/2012 Dr. Kusum Gaur 99
93. Key Concepts: Sample size
• Sampling Design - larger sample for Custer
• Desired Power – more power for larger sample
• Allowable error – smaller error for larger sample
• Heterogeneity leads to have larger sample to
cover diversities
• Nature of Analysis – Complex multivariate
needs larger sample
12/08/2012 Dr. Kusum Gaur 100
94. Steps -Sample Size Estimation
• Stage 1- * Base Sample Size Calculation (n)
• Stage 2 – Sample Size with Design Effect (d)
=n*d
• Stage 3- Contingency Addition (e.g. 5%)
SS Estimation for study population
=(n*d)+5%of n
*Use appropriate equation for sample size
calculation
http://stat.ubc.ca/~rollin/stats/ssize/
12/08/2012 Dr. Kusum Gaur 101
95.
96.
97. E.G. Mean 1= 5, Mean 2 = 15 & SD = 14 inputting values
113. Random sampling Techniques
Aim is to give equal chance to
every observation unit to be
selected for study in sample.
(Any Observation unit
should not have Zero Probability )
12/08/2012 Dr. Kusum Gaur 120
114. * Random Sampling Techniques
Simple Random Technique
Systemic Random Technique
Stratified Random Technique
Multiphase Random Technique
Multistage Random Technique
Cluster Random Technique
12/08/2012 Dr. Kusum Gaur 121
115. Simple Random Technique
• Lottery Method
• Random Table Method
12/08/2012 Dr. Kusum Gaur 122
117. Steps –Use of Random Table
• Stage 1- Give number to each member of population
• Stage 2 – Determine total population size (N)
• Stage 3- Determine Sample size (S)
• Stage 4 – Drop one finger on Random Table with eyes
closed
• Stage 5 – Drop one finger with eyes closed on direction
to be chosen – Up/Down/Rt/Lt
• Stage 6- Determine first number within 0 to N
• Stage 7- * Determine other numbers till Sample size (S)
* Once a number is chosen do not repeat it again
12/08/2012 Dr. Kusum Gaur 124
118. Steps –Use of Random Table..
e.g. N=300, M=50
Random no. Selected no. (3 digits from 0-300)
49468
49699
14043 043
15013 013
12600
33122 122
94169 169
89916
74169 169
32007 007
www.evaluation
wikiog/index/how_to_use_a_random_number_Table
12/08/2012 Dr. Kusum Gaur 125
119. Systemic Random Technique
The selection of sample follows a systematic
interval of selection
• Find serial interval
(K) = total population/sample size
• 1st observation through simple random sampling
among 1to K. th
• Next observation = (1st +K) Observation
• Next observation = (2 nd +K)th Observation
• -------------so on till No. of observations
= Sample Size
12/08/2012 Dr. Kusum Gaur 126
120. Systemic Random Technique Population
N=100 (Given) 1 21 41 61 81
2 22 42 62 82
S=20 (Estimated) 3 23 43 63 83
K=N/S =100/20 =5 4 24 44 64 84
5 25 45 65 85
1st observation between 1 to 5 6 26 46 66 86
7 27 47 67 87
though SRS e.g. 3 8 28 48 68 88
Every 5th observation from 3rd 9 29 49 69 89
10 30 50 70 90
observation will be included in 11 31 51 71 91
sample population 12 32 52 72 92
13 33 53 73 93
So, sample population will be – 3rd 14 34 54 74 94
8th 13th 18th 23rd 28th 33rd 38th 15 35 55 75 95
16 36 56 76 96
43rd 48th 53rd 58th 63rd 68th 73rd 17 37 57 77 97
78th 83rd 88th 93rd and 98th 18 38 58 78 98
19 39 59 79 99
observation 20 40 60 80 100
12/08/2012 Dr. Kusum Gaur 127
121. Stratified Random Technique
Sample selection through Simple Random/Systemic Random Technique
Sample Strata 1
Sample
Strata 2
Sample Strata 3
12/08/2012 Dr. Kusum Gaur 128
122. Multiphase Random Technique
Specific test
Screening Test
S/S
Population
Probable cases Cases
Suspected cases For
study
12/08/2012 Dr. Kusum Gaur 129
123. Multistage Random Technique
Each stage Simple RT is used village
district
village
village
State 1 district
Population village
Study
Of Population
Nation village
district
village
State 2
village
district
village
12/08/2012 Dr. Kusum Gaur 130
124. Cluster Random Technique
The unit of random selection is a cluster rather than individual
• CI = Total population /30 (in 30 Cluster Technique)
Cluster 1 Cluster 27
Cluster 2 Cluster 28
Population Study
Of Population
Nation Cluster 3 Cluster 29
Cluster 30
Cluster 4
Through Simple RT
12/08/2012 Dr. Kusum Gaur 131
125. Stratified Vs Cluster Technique
Stratified Technique Cluster Technique
• Homogenous groups • Comparable groups of
are made population are made
• Randomly select (usually 30)
sample from each • Randomly select
group
sample from each
• To make it more truly group
representative, take
sample population • More chances of error
proportion to size (PPS) than simple random
• Less chances of error
than simple random
126. Non Probability Sampling
• When random samples are not possible
• Rare disease
• Small population
• Special population
• Special Condition
• Difficult to reach population
12/08/2012 Dr. Kusum Gaur 133
127. Non-probability Samples
Convenience
Purposive
Quota
Snow ball study
12/08/2012 Dr. Kusum Gaur 134
131. Snow ball sampling
Contact tracing
Initial respondent helps in recruiting
new population
Useful in network analysis approach
12/08/2012 Dr. Kusum Gaur 138
132. Step-4 & 5
Data Collection
and
Data Management
133. Sources of Data
• Primary –Own generated data
• Secondary –Already generated data
Published
Non-Published
12/08/2012 Dr. Kusum Gaur 140
134. Primary Vs Secondary source of Data
Primary data Secondary data
• Need to be generated • Readily available
• First hand information • Second hand information
• Questionnaire
• Not need of questionnaire
• Purpose served
• Purpose served ?
• Analysis as per
purpose
• Require more time and • Descriptive
money • Less expensive
12/08/2012 Dr. Kusum Gaur 141
135. Type of Data Collection Methods
Interview
Personnel
Telephonic
Observation
Experimental
Interview and Observation
Observation and Experimental
Interview ,Observation and Experimental
12/08/2012 Dr. Kusum Gaur 142
136. Forms of questions(Open Vs Closed)
Open ended Close ended
• Possible responses are • Categories are given
not given. already coded
• Mean, SD, Median • Proportion
• For seeking opinions, • For eliciting factual
attitudes ,perceptions information
• Not so depth
• Provides in depth info. • Investigator‟s bias
• Experience of • Ease of answering,
investigator and • Easy to analyse
analyst required
12/08/2012 Dr. Kusum Gaur 143
137. Considerations in formulating questionnaire
(Questionnaire/Interview schedule)
Use simple and everyday language
Do not use ambiguous questions(?/?)
Do not ask leading questions
The order of questions:
Guideline for filling an instrument, pen-pencil
Pre testing
12/08/2012 Dr. Kusum Gaur 144
138. Validity of a Research Instrument
Ability of an instrument to measure what it is
designed to measure being measured
Establish the logical link between the
questions and objectives
Items/questions cover the full range of
issue/attitude being measured
12/08/2012 Dr. Kusum Gaur 145
139. 1.Decide the information required.
Steps
2. Define the target respondents.
3. Method(s) of reaching target
4. Decide on question content.
5. Develop the question wording.
6. Put questions into a meaningful order.
7. Check the length of the questionnaire.
8. Pre-test the questionnaire.
9. Develop the final survey form
12/08/2012 Dr. Kusum Gaur 146
140. Organization and Compilation of Data
Organization and Compilation of Data in such a way
(Master Chart ) to have reliable, relevant, adequate
and reasonably complete data with following
requisites –
Simplicity
Briefness
Utility
Distinctively
Comparability
Scientific Arrangement
Attractive
12/08/2012 Effective
Dr. Kusum Gaur 147
161. Tabulation – Content of Table
Table No. Sequence in the text
Tile of Table –short, clear and self explanatory to say about for
what the table is ?
Body of Table –consist of rows and columns
Rows – 1st row shows headings of columns
1st column shows headings of rows
rest of rows and columns are showing data as per required
number of rows and columns should be limited to maintained
simplicity of table
source of data ( if it is other than the present study ) should be
written just below the body of table
Source of Data ?
Foot Note - written just below the body of table, if there is any
hidden information
Inferences –summary value of table
12/08/2012 Dr. Kusum Gaur 168
162. Types of Tables
As per purpose
General tables –about Socio-demographic profile
Specific tables –about Aims and objectives
As per originality
Original tables-from original Data
Derived tables –from original tables
As per Construction
Simple tables- showing one variable at one time
Complex tables – showing > one variable at one time
12/08/2012 Dr. Kusum Gaur 169
169. Multiple Bar diagram
60
50
40
(1) 1-5 Years
30
(2) 6-10 Years
(3) 11 & Above Years
20
10
0
(1) Very Dissatisfied (2) Dissatisfied (3) neither satisfied (4) Satisfied (5) Very Satisfied
nor dissatisfied
12/08/2012 Dr. Kusum Gaur 176
171. Pie diagram
Propotion of Pie = (Proportion of that variable )(360)Degree
12%
14% 1st Qtr
2nd Qtr
82% 3rd Qtr
4th Qtr
32%
12/08/2012 Dr. Kusum Gaur 178
172. Line diagram
7
6
5
4
Series 2
3
Series 1
2
1
0
2000 2001 2002 2003 2004 2005
12/08/2012 Dr. Kusum Gaur 179
173. Histogram ( Area Diagramme)
Series 1
40
30
20
10
Series 1
0
0 to 5 yrs
5yrs to 10
10 yrs to
yrs 15 yrs to
15 yrs 20 yrs to
20 yrs
25 yrs
12/08/2012 Dr. Kusum Gaur 180
174. Scatter Diagram
30
25
20
Duration of Diabetes
15
Duration of diabetes in yrs.
Linear (Duration of diabetes in yrs.)
10
5
0
0 50 100 150 200 250 300
No. of Patients
12/08/2012 Dr. Kusum Gaur 181
175. Radar diagram
5/1/2002
40
30
20
9/1/2002 6/1/2002
10
Series 1
0
Series 2
8/1/2002 7/1/2002
12/08/2012 Dr. Kusum Gaur 182
176. Box & Whisker
70
60
50
40 Open
High
30 Low
20 Close
10
0
5/1/2002 6/1/2002 7/1/2002 8/1/2002 9/1/2002
12/08/2012 Dr. Kusum Gaur 183
178. Biostatistics = Biology + Statistics
• Biostatistics is application of statistics in
biology i.e. science of figure in medical science
• Data: Set of information, facts or figures
numerically coded and from which conclusions
may be drawn is called data (singular-datum).
• Statistics: The collection of methods used in
planning an experiment
and analyzing data in order to draw accurate
conclusions.
179. Type of Biostatistics
• Descriptive statistics generally characterizes
or describes a set of data elements
• Inferential statistics tries to infer information
about a population by using information
gathered by sampling
180. Descriptive Analysis
Qualitative Data
Rates
Ratios
Proportions
Quantitative Data
Central Tendencies Disperson
Mean Standard Deviation
Mode Standard Error
Median Confidencial Limit
Skeweness
12/08/2012 Dr. Kusum Gaur 187
181. Descriptive Analysis of
Qualitative Data
No. of total Events in a year (A)
Rate = * 1000
MYP of that Region (T)
No. of total (A)
Ratio =
No. of total (B)
No. of Specific Events (A)
Percentage of Events = * 100
Total Events (T)
Event of Sp. Cause (A)
Proportional Rate = * 10 n
Total Deaths (T)
12/08/2012 Dr. Kusum Gaur 188
182. Descriptive Analysis of
Quantitative Data
Mean = Mathematical Average ∑X
N
Mode = Most commonly occurring value
Median = Center value when arrange in increasing N+1
or decreasing fashion 2
Standard Deviation = It tells how much scores deviate from the mean
it is the square root of the variance
it is the most commonly used measure of spread (X-X)
SD=√ N
Standard Error = Deviation from mean per observation
SD/ √N
Skewness = Deviation of peak from median
SK= 3 (Mean –Median)/SD
12/08/2012 Dr. Kusum Gaur 189
186. TEST OF SIGNIFICANCE OF QUALITATIVE DATA
TEST OF SIGNIFICANCE OF QUALITATIVE DATA
One Sample Two Sample >Two Sample
Sample proportion
to Independent Dependent Dependent Independent
Population Proportion
Mc Numer Cochron’s
Large Sample Small Sample
(>30) (<30)
Small Sample Large Sample Large Sample Small Sample
Yat’s Corrected
‘Z’ Score Corrected ‘Z’ Score Chi Squire
Chi Squire ‘Z’ Score Chi Squire
Yat’s Corrected Chi
Chi Squire
12/08/2012 Dr. Kusum Gaur 193
187. TEST OF SIGNIFICANCE OF QUANTITATIVE DATA
TEST OF SIGNIFICANCE OF QUANTITATIVE DATA
One Sample Two Sample >Two Sample
Sample Mean
to Independent Dependent Dependent Independent
Population Mean
Paired ‘T’ Test ANOVA Friedman
Large Sample Small Sample
(>30) (<30)
Small Sample Large Sample Large Sample Small Sample
‘Z’ Test ‘T’ Test
‘Z’ Test ANOVA ANOVA
12/08/2012 Dr. Kusum Gaur 194
188. STUDY DESIGNS AND APPROPRIATE TEST
Type Study Design Appropriate Significance Test
Descriptive Study
Analytical
Case Control Study OR
Qualitative ‘Z’ Score Test/Chi-Square Test
Quantitative ‘Z’ Test/’t’ Test
Cohort study OR, AR, & RR
Qualitative ‘Z’ Score Test/Chi-Square Test
Quantitative ‘Z’ Test/’t’ Test
12/08/2012 Dr. Kusum Gaur 195
189. STUDY DESIGNS AND APPROPRIATE TEST
Type Study Design Appropriate Significance Test
Randomized Controlled study
Quantitative (before and after)- Paired ‘t’ Test
Quantitative (before and after >1 followup)- Freidmen ANOVA
Quantitative (between two Gps)- Unpaired ‘t’ Test
Quantitative (between > two Gps)- ANOVA Test
Randomized Controlled study
Qualitative (before and after)- Mac Numer Test
Qualitative (before and after >1 followup)- Cochron’s Test
Qualitative (between two Gps)- ‘Z’ Score/Chi-square Test
12/08/2012 Qualitative (between > two Gps)- Chi-square Test
Dr. Kusum Gaur 196
190. STATISTICAL TEST OF SIGNIFICANCE
Nominal Numerical Ordinal
Two Groups ‘Z’ Score Test ‘Z’ test (n>30) Mann Whitny
Chi-square Test T Test (n<30)
> Two Groups Chi-square Test ANOVA Kruskal Wallis
Paired Two Mec Numer Paired ANOVA Wilcoxon Sign
Multiple Cohrane Repeated Friedman
Observation in Multivarient ANOVA
same individual
Association of Contegency Correlation(Pearson) Spearman
Two Variable Cofficient Regression Correlation
191. STATISTICAL TEST OF SIGNIFICANCE
Research Number and Number and Covariates Test Goal of Analysis
Question type of DV type of IV
Nominal 1 nominal chi square determine if difference between
Group croups
differences Continuous 1 dichotomous t-test
Determine significance of
1 Categorical 1 one-way ANOVA mean group
1+ one-way differences
ANCOVA
2+ Categorical 1 factorial ANOVA
1+ factorial ANCOVA
2+ Continuous 1 Categorical 1 one-way MANOVA Create linear
1+ one-way MANCOVA combo of Dependent variable
2+ Categorical 1 factorial (Dvs)
MANOVA to maximize
1+ factorial MANCOVA mean group
differences
Degree of Continuous 1 Continuous Bivariate Determine relationship/prediction
relationship Correlation
2+ Continuous Multiple Linear combination to predict the
Regression DV
1+ Continuous 2+ Continuous Path Analysis Estimate causal relations among
variables
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192. Comparing difference between
Two Sample Proportions
„Z‟ Score Test
P2 – P1 here, P1– proportion of that event in 1st Sample
„Z‟ Score = P2 - proportion of that event in 2nd Sample
SEDP SEDP – Standard Error of
Difference in Proportion
Q1 - proportion without that event
in 1st Sample i.e. Q1 = 100 – P1
Q2 - proportion without that event in
P1 Q 1 P 2 Q 2 2nd Sample i.e. 100 – P2
SEDP = ------- + -------- N1 - Sample Size of 1st Sample
N1 N2 N2 - Sample Size of 2nd Sample
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193. Inference of ‘Z’ Score Test
If „Z‟ > 2 = Difference is Significant
If „Z‟ < 2 = Difference is Not Significant
If „Z‟ > 3 = Difference is Highly Significant
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194. Comparing difference between
>Two Sample Proportions
Chi-Square Test
Indications
Qualitative data
Normal distribution
Comparing difference between
Two Sample proportions
Multiple Sample proportions
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195. Comparing difference between
>Two Sample Proportions
Chi-Square Test
Chi Square(2) = ∑all cells(O-E)2 Tr x Tc
E=
E T
(O1-E1)2 (O2-E2)2 (O3-E4)2 (On-En)2
Chi Squire = + + + ---+
E1 E2 E3 En
Tr – Total of that Row
here, O – Observed value of cell
Tc – Total of that column
E – Expected value of cell,
T – Grand Total i.e. a+b+c+d
considering Null Hypothesis
Degree of Freedom (DF) = (C – 1) (R -1)
R= No. of Rows, C = No. of Column
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196. Inference of Chi Square(x2)
Chi Square(x2 ) value is seen at Degree of Freedom
DF = (R – 1) (C – 1), from Chi Square((2) Table
(here R=No. of Rows &C= No. of Column)
at desired level of significance
Inferences
If Chi Square(x2 ) Test Value is –
Higher than Table value = Difference in proportions is
Significant at that desired level of significance.
If Chi Square(x2 ) Test Value is –
Lower than Table value = Difference in proportions is
Not Significant at that desired level of significance.
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197. Comparing difference between
Two Sample Means (>30)
„Z‟ Test
Pre-requisites
Quantitative data
Homogenous normally distributed Random Sample
Sample Size > 30
Indications
To see the Significance of any Observation in
reference of Mean Value of that sample
Comparing difference between
Sample Mean to Population Mean
Means of Two independent Samples
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198. Comparing difference between
Two Sample Means (>30)
„Z‟ Test
X2 – X1 here, X1– Mean of that event in 1st Sample
„Z‟ Test = X2 - Mean of that event in 2nd Sample
SEDM SEDM – Standard Error of
Difference in Means
SD1 – Standard Error of 1st Sample
SD2 – Standard Error of 2nd Sample
N1 - Sample Size of 1st Sample
SD2 1 SD2 2 N2 - Sample Size of 2nd Sample
SEDM = ------- + --------
N1 N2
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199. Comparing difference between
Two Sample Means (<30)
„T‟ Test
Prerequisites
Random Sample
Quantitative data
Normally Distributed
Sample Size < 30
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200. Type of ‘T’ Test
as per design
Unpaired / Paired
for inference
One Tail /Two tail
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201. Unpaired ‘T’ Test Design
Population -1 Population -2
S-1 S-2
Mean --1 Unpaired ‘T’ test Mean --2
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202. Paired ‘T’ Test Design
Intervention
Population Sam
Observations-1 Observations 2
ple-
Mean --1 Mean --2
Paired ‘T’ test
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203. One Tail ‘T’ Test
Acceptance Zone Rejection Zone
One Tail – Results are aspect only in one direction
204. Two Tail ‘T’ Test
Rejection Zone Acceptance Zone Rejection Zone
Two Tail – Results are aspect in both direction
205. Comparing difference between
Two Sample Means (<30)
„T‟ Test
X2 – X1 here, X1– Mean of that event in 1st Sample
„T‟ Test = --------------- X2 - Mean of that event in 2nd Sample
SEDM SEDM – Standard Error of
Difference in Means
SD1 – Standard Error of 1st Sample
SD2 – Standard Error of 2nd Sample
N1 - Sample Size of 1st Sample
SD2 1 SD2 2 N2 - Sample Size of 2nd Sample
SEDM = ------- + --------
N1 N2
Degree of Freedom (DF) = (N1 – 1) + (N2 -1) = N1 + N2 - 2
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206. Inference of ‘T’ Test Value
„T‟ Test Value is matched at Degree of Freedom
(DF) = N1 + N2 – 2 in the Table of “T”
at desired level of significance.
Inferences
If „T‟ Test Value is –
Higher than Table value = Difference in Means is
Significant at that desired level of significance.
If „T‟ Test Value is –
Lower than Table value = Difference in Means is
Not Significant at that desired level of significance.
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207. Comparing difference between
>Two Sample Means
ANALYSIS OF VARIENCE (ANOVA) TEST
Pre-requisites
Quantitative data
Homogenous normally distributed Random
Sample
Indications
Comparing difference between more than Two
Means
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208. Comparing difference between
>Two Sample Means
„ANOVA‟ Test
MSOSI MSOS2 - Mean Sum Of Squares Within Classes
ANOVA = ---------- = Total SOS – MSOSI
MSOS2
T SOS = X2 – (X)2/N
MSOSI – Mean Sum Of Squares Between Classes = SOSI / K-1
SOSI –Sum Of Squares Between Classes
(Xa)2 (Xb)2 (Xc)2 (Xk)2 (X)2
= --------- + ----------- + ----------- + ….+ ____ __ - ---------
Na Nb Nc Nk N
At Degree of Freedom (DF) = ( K-1) Horizontal
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(N – K) Vertical
215
209. Inference of ANOVA
Find out Variance Ratio value at Degree of Freedom
(DF) = ( K-1) Horizontal, (N – K) Vertical
from the Variance Ratio Table
at desired level of significance.
Inferences
If Test value is > Table value = Difference in Means is
Significant at that desired level of significance.
If Test value is < Table value = Difference in Means is
Not Significant at that desired level of significance.
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211. Type & Degree of Correlation
Correlation Inference Correlation (r) Inference
+1 Perfect +ve -1 Perfect +ve
Correlation Correlation
> 0.95 About Perfect +ve > - 0.95 About Perfect +ve
Correlation Correlation
> 0.75 V. Good Correlation > - 0.75 V. Good Correlation
0.75 – 0.5 Moderate Correlation - 0.75 to – 0.5 Moderate
Correlation
0.5 – 0.25 Fair Correlation - 0.5 to – 0.25 Fair Correlation
0.25 - 0 No Correlation < - 0.25 No Correlation
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212. Correlation
CORRELATION
Two Variables > Two Variables
Un-Paired Data Paired Data
Pearson‟s Spearman‟s Rank Order Multivariate
Correlation Correlation Correlation
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213. Pearson’s correlation
. ∑ ( X – X) ∑ ( Y – Y) ∑xy
Correlation (r) = =
√∑ ( X – X)2 ∑ ( Y – Y)2 √ ∑ x2 y2
Direct Method
∑ X Y - ∑ X ∑Y / N
Correlation (r) = -----------------------------
√ {∑X2 – (∑X)2/N}{ ∑Y2 – (∑Y)2 /N}
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214. Pearson’s correlation -----
here,
∑ X Y = Sum of multiplication of X and Y
∑ X = Sum of all observations of X Series
∑ Y = Sum of all observations of YX Series
N =Total no. of observations
∑X2 = Sum of Squares of all observations of X Series
∑Y2 = Sum of Squares of all observations of Y Series
(∑X)2 = Square of Sum of all observations of X Series
(∑Y)2 = Square of Sum of all observations of Y Series
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215. Spearman’s Rank Order Correlation
6∑D2
• Spearman‟s Rank (rs ) = 1 -
N3 - N
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216. Significance Test for Correlation (r)
Standard Error (SE) of rs = rs √ N-1
Inference
• If difference >2 SE of r =Difference is
Significant at 5% level
• If difference < 2SE of r =Difference is
Not Significant at 5% level
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217. REGRESSION
Indication
To find out causal relationship between
variables
REGRESSION COFFICIENT- It is a measure of
change in one dependent variable (y) with
one unit change in the other variable (x)
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218. Regression line with Regression Equation
The regression equation of ‘Y’ on ‘X’ is expressed as follows:
Here, ‘a’ is interceptor & ‘b’ is slope Yc = a + bX
219. Regression Lines
Régression line of Y on X is Y = a + bX ----(1)
Régression line of X on Y is X = a + bY ----(2)
Here- Y = one variable
X = other variable
a = interceptor of X line on Y line
b = slope of X line on Y line Regression
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220. Regression – Equations
Regression Equation of X on Y
SD of series X
(X – X)= r (Y –Y) ---- (3)
SD of series Y
Regression Equation of Y on X
SD of series Y
(Y – Y)= r (X –X) ------- (4)
SD of series X
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221. Regression – coefficients
Regression Coefficient of X on Y
SD of series X ∑(X-X)(Y –Y)
b(xy)= r =
SD of series Y ∑(X – X)2
Regression Coefficient of Y on X
SD of series Y ∑(X-X)(Y –Y)
b(yx)= r =
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SD of series Kusum Gaur
Dr.
X ∑(Y – Y)2 228
222. Relation of correlation and
Regression
Co-rrelation (r) = √ bxy byx
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223. Between
Tests/Procedure/Therapy
For comparison with Gold Standard:
Sensitivity
Specificity
PPV
NPV
ROC
For agreement of association: Kappa
For appropriate cut of value for diagnostic test: ROC
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224. Sensitivity and Specificity
Status based on gold standard test
Diseased Normal
Test positive True positive False positive
Observation in a b
new test Test negative False negative True negative
c d
Sensitivity = a /(a+c) PPV = a /(a+b)
Specificity = d /(b+d) NPV = d /(c+d)
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226. Kappa Statistics
(Measurement of Agreement)
Test Value Inference
0.93 – 1 Excellent Agreement
0.81 – 0.92 Very Good Agreement
0.61 – 0.80 Good Agreement
0.41 – 0.60 Fair Agreement
0.21 – 0.40 Slight Agreement
0.01 – 0.20 Poor Agreement
< 0.01 No Agreement
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227. Non-Parametric Tests
Advantages
Distribution free
Easier to do
Easier to understand/infer
Disadvantages
They ignore certain amount of information
Indicated only ordinal or nominal data
Statistically Less efficient
Indicated only to test hypothesis, not for
estimates
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228. Parametric Test Vs Non-Parametric
Test Quality Parametric Non-Parametric
Assumed Distribution Normal Any
Assumed Variance Homogenous Any
Data Type Interval-Continous Nominal /Ordinal
Data set Relationship Independent Any
Usual Centre Measure Mean Median
More conclusions Easier to calculate
Advantages
More efficient Less affected by outliers
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229. Parametric Test Vs Non-Parametric
Test Parametric Non-Parametric
Correlation test Pearson Spearman
Independent Independent-
Mann-Whitney test
measures, 2 groups measures t-test
One-way,
Independent
independent- Kruskal-Wallis test
measures, >2 groups
measures ANOVA
Repeated measures,
Matched-pair t-test Wilcoxon test
2 conditions
Repeated measures, One-way, repeated
Friedman's test
>2 conditions measures ANOVA
Sign Test (K Test)– nonparametric test for quantitative paired data
12/08/2012 Dr. Kusum Gaur 236
230. Sign test
• Simplest
• Based on direction(- /+/0)
• Signs as per the direction are counted
• Inference – if S≤K = Null hypothesis (H₀) is
rejected
• Here „S‟ is net sum of signs as per sign
• „K‟ is constant
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231. Sign test – Steps
Sign K Test for Small Sample (<30)
– Find out net sum of signs as per sign(S)
– S = (total + signs) – (total – signs)
– K = (n-1)/2 - 0.98√n
• Inference – if S≤K = Null hypothesis (H₀) is rejected
Sign Z Test for Large Sample (>30)
– Find out no of ties with less frequent sign(X)
– Z = (X – np) / √ np (1-p) here X= no. + Sign
• Inference – if Z>2 = Null hypothesis is rejected
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237. Step-7
Inferences
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238. Steps in Statistical Inference
Generating NULL and ALTERNATIVE
hypothesis
Testing the hypothesis using appropriate
statistical tests
Obtaining „p‟ value
Concluding from the p value.
Obtaining Level of Significance
Comparing „p‟ value with CI.
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239. ‘P’ Value and Inferences
with Normal Curve
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240. Rejection Zone Acceptance Zone Rejection Zone
Mean 1SD =68% values - Confidence Limit 68% - P Value = >0.05 - NS
Mean 2SD =95% values - Confidence Limit 95% - P Value = 0.05 - S
Mean 3SD =99% values - Confidence Limit 99% - P Value = 0.001 - HS
241. Rejection Zone Acceptance Zone Rejection Zone
Mean 1SD =68% values - Confidence Limit 68% - P Value =/>0.05 - NS
Mean 2SD =95% values - Confidence Limit 95% - P Value < 0.05 – S
Mean 3SD =99% values - Confidence Limit 99% P Value < 0.001 - HS
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242. Conventionally Accepted
Significance Level
P Value > 0.05 LS=Not Significant
P Value < 0.05 LS=Significant
P Value < 0.001 LS=Highly Significant
243. Step-8
Reporting
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244. Steps of Report Writing
Title of Project
Abstract
Introduction
Aims & Objectives
Methodology
Observations-Compilation, Classification &
Presentation of data with analysis and inferences
Discussion
Conclusions
Recommendations
Limitations
Acknowledgment
Bibliography
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245. Discussion
Explanation of findings
Logic and reasoning for the results as it
appears
Compare and contrast with findings of other
researchers
Based on objectives of the study
Should answer the research question
Scope & limitations of the study
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246. Recommendations & conclusions
• Based on our findings
• Limited to objectives of the study
• Policy implications
• Relevance should be emphasized
• Should be exclusively limited to
observations
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247. Managerial and financial aspects
Protocol development
Time line/Gantt chart
Peer review
Development of tools
Training in data collection
Budget/ financial accounting
Quality control
Monitoring & Evaluation
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248. Time Line/Gant chart/log Fram
Activities 1.1.12- 16.1- 1.2.12- 1.3.12- 16.5.12- 16.6.12- 16.7.12-
15.1.12 31.1 15.2.12 15.5.12 15.6.12 15.7.12 31.7.12
Planning
Officials
Que. Dev
Training
Poilet Survey
Corrections
Re-training
Resource Proc
Survey
Analysis
Report Writing
Dissemination
of Report
250. Web sites related to Statistics
• http://stattrek.com
• http://vassarstat.net
• http://www.scribd.com
• http://www.statistixl.com
• http://statistics calculators.com
• http://stat.ubc.ca/~rollin/stats/ssize/
• ………………………………………………………
……
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251. Computer Softwares in Statistics
• Microsoft Excel
• SPSS
• Epi info
• Epi tab
• Mini tab
• Graph Pad
• Primer
• Medcal
• ……………..
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252. Always there is room for improvement
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