PRACTICAL SKILL DEVELOPMENT IN DISECTION AND CRITICAL ANALYSIS OF THE MESSA...Mohammad Aslam Shaiekh
PRACTICAL SKILL DEVELOPMENT IN DISECTION AND CRITICAL ANALYSIS OF THE MESSAGE OF COMPLEMENTARY FEEDING AND ASSESSMENT OF FAVORABLE AND UNFAVORABLE BEHAVIOR ON MATERNAL NUTRITION
Practicum presentation on Safe Motherhood Program (SMP) and Maternal and Peri...Mohammad Aslam Shaiekh
Practicum presentation on Safe Motherhood Program (SMP) and Maternal and Perinatal Death Surveillance and Response (MPDSR) Program in MNH Section of Family Welfare Division
NHS reforms – opportunities and challenges for MS CareMS Trust
This presentation by Karen Middleton CBE, Chief Allied Health Professions Officer, explores the narrative for the NHS reforms, the key structures that clinicians need to be aware of and some of the main challenges and opportunities they present for MS care.
It was presented at the MS Trust Annual Conference in November 2013.
PRACTICAL SKILL DEVELOPMENT IN DISECTION AND CRITICAL ANALYSIS OF THE MESSA...Mohammad Aslam Shaiekh
PRACTICAL SKILL DEVELOPMENT IN DISECTION AND CRITICAL ANALYSIS OF THE MESSAGE OF COMPLEMENTARY FEEDING AND ASSESSMENT OF FAVORABLE AND UNFAVORABLE BEHAVIOR ON MATERNAL NUTRITION
Practicum presentation on Safe Motherhood Program (SMP) and Maternal and Peri...Mohammad Aslam Shaiekh
Practicum presentation on Safe Motherhood Program (SMP) and Maternal and Perinatal Death Surveillance and Response (MPDSR) Program in MNH Section of Family Welfare Division
NHS reforms – opportunities and challenges for MS CareMS Trust
This presentation by Karen Middleton CBE, Chief Allied Health Professions Officer, explores the narrative for the NHS reforms, the key structures that clinicians need to be aware of and some of the main challenges and opportunities they present for MS care.
It was presented at the MS Trust Annual Conference in November 2013.
Community health Nursing is the synthesis of nursing and public health practice applied to promote and protect the health of population. It combines all the basic elements of professional, clinical nursing with public health and community practice.
Buniyaad is a three year project (April 2012 to March 2015). The project aims at reaching out to 400,000 women with BCC messages on the three tenets of IYCF (immediate and exclusive breastfeeding and complementary feeding) in its lifetime. These messages are rolled out dedicated grassroot workers-Peer Educators (PEs) and Cluster Coordinators (CCs). Their main job is to counsel mothers and caregivers about recommended IYCF practices and help them overcome the barriers to the same.The BCC efforts under the project are expected to bring about an improvement in the knowledge levels (output) of the primary beneficiaries (pregnant women and mothers of children under two) as well as the secondary target population (health and nutrition functionaries) before resulting in a change in practice (outcome).
The main purpose of this midterm evaluation was to review the progress attained in the project to-date in relation to expected outcomes, highlight what works well which can be scaled up, and what necessary changes (both in strategy as well as action plans) can be made to achieve the desired project objectives. Specifically, the main objectives of the midterm evaluation are:
• To assess progress to-date in terms of achieving key milestones, outputs and early outcomes
• Identify lessons learned, areas to strengthen, modify and refocus to enhance the project’s implementation and sustainability
The project internally generates lot of data which quantifies the progress achieved under key components of the project. However, it was felt that it is important to capture the qualitative response of project beneficiaries and project staff with respect to the project interventions, as well as strengths and weaknesses of specific processes and activities that are affecting, and may further affect the outcomes under the project. Getting a subjective overview of the project will help in assessing the current strategy and help in identifying the need for any further change or modifications.With this view, this mid-term evaluation study has adopted a mixed method approach consisting of both qualitative and quantitative methods of data collection and analysis, aiming at addressing the research questions.
Nursing Education programs can include one or two practicum courses in nursing education and leadership. In a typical practicum, you might be expected to work with a nurse educator or administrator in an educational setting and help design, implement, and evaluate nursing education programs. Ed.D. practicums are built to accommodate working nurses.
Care in hospital settings is all about the care which is provided in the hospital to the patient. The word patient comes from the Latin word patiens, originally meant “one who suffers”. Care in hospital is the attention or watchful oversight and attentive assistance or treatment for the increasing proportion of population and with the shift in disease patterns from acute illnesses to chronic...
Challenges before Nursing Educators An OverviewYogeshIJTSRD
Trends in health care suggest changes in nursing practice and implications for nursing education. Changing demographics, emphasis on health promotion, health care costs, movement toward community based care, and expanding technology are factors that shape the health care system of the future and educational preparation of nurses. This article examines these trends and implications for nursing education. Faculty are faced with preparing students for future practice that will be more complex and specialized than it now is will be provided in multiple settings and will require extensive knowledge, critical thinking and other cognitive skills, technologic and psychomotor skills, and a valve system for making ethical decisions. Other outcomes of nursing education program include learning to learn, handling ambiguity, thinking like a professional, and accepting responsibility for decisions made in practice. For nursing to assume a central role in the health care system of tomorrow, reform in nursing education is needed today. Mr. Manu Chacko "Challenges before Nursing Educators: An Overview" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41234.pdf Paper URL: https://www.ijtsrd.commedicine/nursing/41234/challenges-before-nursing-educators-an-overview/mr-manu-chacko
Community health Nursing is the synthesis of nursing and public health practice applied to promote and protect the health of population. It combines all the basic elements of professional, clinical nursing with public health and community practice.
Buniyaad is a three year project (April 2012 to March 2015). The project aims at reaching out to 400,000 women with BCC messages on the three tenets of IYCF (immediate and exclusive breastfeeding and complementary feeding) in its lifetime. These messages are rolled out dedicated grassroot workers-Peer Educators (PEs) and Cluster Coordinators (CCs). Their main job is to counsel mothers and caregivers about recommended IYCF practices and help them overcome the barriers to the same.The BCC efforts under the project are expected to bring about an improvement in the knowledge levels (output) of the primary beneficiaries (pregnant women and mothers of children under two) as well as the secondary target population (health and nutrition functionaries) before resulting in a change in practice (outcome).
The main purpose of this midterm evaluation was to review the progress attained in the project to-date in relation to expected outcomes, highlight what works well which can be scaled up, and what necessary changes (both in strategy as well as action plans) can be made to achieve the desired project objectives. Specifically, the main objectives of the midterm evaluation are:
• To assess progress to-date in terms of achieving key milestones, outputs and early outcomes
• Identify lessons learned, areas to strengthen, modify and refocus to enhance the project’s implementation and sustainability
The project internally generates lot of data which quantifies the progress achieved under key components of the project. However, it was felt that it is important to capture the qualitative response of project beneficiaries and project staff with respect to the project interventions, as well as strengths and weaknesses of specific processes and activities that are affecting, and may further affect the outcomes under the project. Getting a subjective overview of the project will help in assessing the current strategy and help in identifying the need for any further change or modifications.With this view, this mid-term evaluation study has adopted a mixed method approach consisting of both qualitative and quantitative methods of data collection and analysis, aiming at addressing the research questions.
Nursing Education programs can include one or two practicum courses in nursing education and leadership. In a typical practicum, you might be expected to work with a nurse educator or administrator in an educational setting and help design, implement, and evaluate nursing education programs. Ed.D. practicums are built to accommodate working nurses.
Care in hospital settings is all about the care which is provided in the hospital to the patient. The word patient comes from the Latin word patiens, originally meant “one who suffers”. Care in hospital is the attention or watchful oversight and attentive assistance or treatment for the increasing proportion of population and with the shift in disease patterns from acute illnesses to chronic...
Challenges before Nursing Educators An OverviewYogeshIJTSRD
Trends in health care suggest changes in nursing practice and implications for nursing education. Changing demographics, emphasis on health promotion, health care costs, movement toward community based care, and expanding technology are factors that shape the health care system of the future and educational preparation of nurses. This article examines these trends and implications for nursing education. Faculty are faced with preparing students for future practice that will be more complex and specialized than it now is will be provided in multiple settings and will require extensive knowledge, critical thinking and other cognitive skills, technologic and psychomotor skills, and a valve system for making ethical decisions. Other outcomes of nursing education program include learning to learn, handling ambiguity, thinking like a professional, and accepting responsibility for decisions made in practice. For nursing to assume a central role in the health care system of tomorrow, reform in nursing education is needed today. Mr. Manu Chacko "Challenges before Nursing Educators: An Overview" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41234.pdf Paper URL: https://www.ijtsrd.commedicine/nursing/41234/challenges-before-nursing-educators-an-overview/mr-manu-chacko
Valid and Reliable ToolsThe goal of an evaluation is to determin.docxnealwaters20034
Valid and Reliable Tools
The goal of an evaluation is to determine the success of an intervention, a new process, the launch of a new technology, patient satisfaction, or any number of things. Surveys are a popular tool for gathering this type of information. For the results of the evaluation to be meaningful, however, the survey used must be both reliable and valid. What does that entail? A reliable instrument is one that would yield similar results when given to different groups under identical circumstances. For example, if a survey was given to nurses on the use of a certain piece of technology, all respondents would understand the phrasing of the questions the same way. Validity refers to how well the instrument actually measures what it is intended to measure. Determining the reliability and validity of a survey instrument can be complicated and involves the use of statistics. For this reason, many researchers opt to use instruments that are already developed and tested.
For this Discussion, you consider survey instruments that would be appropriate to use in specific situations.
The following scenarios will be used for this week’s Discussion:
Scenario 1:
A large hospital intends to implement a computerized provider order entry (CPOE) system. In order to get a good idea of its effects, the hospital’s leadership has asked for an evaluation of the CPOE system’s impact 90 days after its initial implementation.
Scenario 2:
Years ago, the primary hospital for a large, rural county distributed personal data assistants (PDAs) to all of its physicians in an attempt to modernize. After looking at many other more up-to-date mobile systems, physicians and hospital leaders are curious about how their current PDA-based system performs.
Scenario 3:
The informatics department of one of North America’s largest hospitals is conducting an internal review of its health information technology systems. This review will evaluate the need for any changes to its systems and may serve as justification for different budgetary allocations. Because of its sheer size and the number of personnel it affects, the hospital’s electronic health record system will be a pivotal point of the review.
To prepare:
Review this week’s Learning Resources on reliability and validity.
Review the AHRQ Evaluation Survey Compendium.
Review the scenarios presented above.
Using the “Locate a Survey for your Project” tool available on the AHRQ website, identify a survey tool that would be appropriate for use for each scenario.
Reflect on the specific characteristics of a valid, reliable survey tool.
By tomorrow Tuesday 1/17/17, post a minimum of 550 words in APA format with 3 references that include
the unique survey tool you identified for
each scenario
and a
justification
for your selections
Required Readings
Friedman, C. P., & Wyatt, J. C. (2010). Evaluation methods in biomedical informatics (2nd ed.). New York, NY: Springer Science+Business Media, Inc.
Chapter 5, “Meas.
Assignment 2 FAQsQuestion 1I am eager to start my next piece o.docxrock73
Assignment 2 FAQs
Question 1
I am eager to start my next piece of assessment, my understanding is we have to identify and explain our understanding of process and outcome data in delivery of safe, high quality care.
The example I would like to use is the five moments of hand hygiene, with promotes infection control, my data would be the auditing process of the five moments and how this is acheived. The outcomes would be improved patient outcomes or adversely, death due to nosocomial infection or readmission. Am I on the right track or does the example have to be based on an a specific ILLNESS such as CA or pneumothorax for example using the Donabedian model of measuring health care
such as treatment process, stages of treatment, appropriateness of treatment and outcomes such as death, adverse events etc..
And do we have to go into depth about what the illness. or is it just simply demonstrating our understanding of the processes and outcomes data for an illness?
Thank you for your email.
Yes you are on the right track as you are discussing safety and quality of nursing care NOT the process or outcome of a disease.
And do we have to go into depth about what the illness. or is it just simply demonstrating our understanding of the processes and outcomes data for an illness?
If you were discussing infection you would need to discuss the impact of the infection.For example,
1. What would be the impact on the client/patient? eg prolonging of illness, complications, longer hospital stay, financial costs, psychological costs,
2. What would be the impact on the health organisation/hospital? eg Inefficiency, increased length of patient stay, bed blocking, increased costs of hospitalisation, possible court claim
3. What would be the impact on the state? eg Decreased productivity of the individual if they remain hospitalised and are unable to work, increased costs if client/patient a pensioner
Question 2
In regards to the second assessment piece, it says:
"Use the scholarly literature to identify and explain the use of process and outcome data in the delivery of safe, quality nursing care in health institutions.
The use 1 example of process and outcome data to demonstrate your understanding of their use in providing safe, high quality care in health institutions."
Did you want us to use the 1 example for the whole paper in identifying and explaining the use of process and outcome data in the delivery of safe, quality nursing care in health institutions to demonstrate our understanding of it. Or did you want us to address both points separately as they are explaining what it is first, then explaining it again using the example we choose?
Thank you for your email.
How you go about addressing the question is up to you. What you have been asked to do is:
1. Explain the use of process and outcome data in the delivery of safe, quality nursing care in health institutions.
2. Demonstrate your understanding of these two phenomena by providing examples of ...
FOCUSING YOUR RESEARCH EFFORTS Planning Your Research ShainaBoling829
FOCUSING YOUR RESEARCH
EFFORTS
Planning Your Research Project Chapter Four
What is the Research Design?
The research design is the general strategy that
provides the overall structures for the procedures
used in the research project. It is the planning
guide.
The Basic Format of the Research
Design
The question
The question converted to a research problem
A temporary hypothesis
Literature search
Data collection
Organization of the data
Analysis of the data
Interpretation of the data
The data either support or do not support the
hypothesis
Planning vs. Methodology
The general approach
to planning research is
similar across all
disciplines
The strategies used to
collect and analyze
data may be specific
to a particular
academic discipline
Research Planning Research Methodology
General Criteria for a Research Project
Universality (can be carried out by any competent
researcher)
Replication
Control (important for replication)
Measurement
The Nature and Role of Data
Data (plural) ‘data are’
Data ARE NOT absolute reality
Data are transient and ever changing
Primary Data are closest to truth
No researcher can glimpse ABSOLUTE TRUTH
Criteria for the Admissibility of Data
Any research effort should be replicable
Restrictions we identify are the criteria for the
admissibility of data
Standardize the data
Planning for Data Collection
What data are needed?
Where is the data located?
How will data be obtained?
How will data be interpreted?
Defining Measurement
Measurement is limiting the data of any
phenomenon – substantial or insubstantial – so that
those data may be interpreted and ultimately
compared to a particular qualitative or quantitative
standard
Measurement is ultimately a comparison: a think or
concept measured against a point of limitation
Types of Measurement Scales
Nominal Scales
Ordinal Scales
Interval Scales
Ratio Scales
Nominal Scales
A nominal scale limits the data
Nominal measurement is simplistic, but it does divide
data into discrete categories that can be compared
to one another.
Only a few statistical procedures are appropriate
for analyzing nominal data (a) mode, (b)
percentage, and (c) chi-square test
Ordinal Scales
Ordinal scales allow us to rank-order data
In addition to using statistics we can use with
nominal data, we can also use statistical procedures
to determine (a) the median, (b) the percentile rank,
and (c) Spearman’s rank order correlation
Interval Scales
An interval scale is characterized by two features:
(a) it has equal units of measurement, and (b) its
zero point has been established arbitrarily
Interval scales allow statistical analyses that are not
possible with nominal and ordinal data
Because an interval scale reflects equal distances ...
ABSTRACT This cultural analysis will examine the catalyst .docxransayo
ABSTRACT
This cultural analysis will examine the catalyst that led to the cultural phenomenon
allowing adults to openly read literature classified as belonging to the younger literature genres.
J. K. Rowling’s Harry Potter series release sparked a change in the way children’s and Young
Adult literature was written as well as opened the door for a wider range of readership (including
adults) for children’s and Young Adult literature. The works of John Granger, Seth Lerer,
Jacqueline Rose, Timothy Morris, Perry Nodelman, Mavis Reimer, Maria Tatar, and Rachel
Falconer will be used to frame Rowling’s series within the context of our post-modern culture,
inviting in an older audience to this younger literature. Following this chapter, there will also be
a similar analysis of Suzanne Collins’ Hunger Games trilogy and Rick Riordan’s Percy Jackson
and the Olympians series.
Using these three current, popular children’s and Young Adult series, the reasons behind
why adults are returning to this younger literature will become clear.
Assignment 2 FAQs
Question 1
I am eager to start my next piece of assessment, my understanding is we have to identify and explain our understanding of process and outcome data in delivery of safe, high quality care.
The example I would like to use is the five moments of hand hygiene, with promotes infection control, my data would be the auditing process of the five moments and how this is acheived. The outcomes would be improved patient outcomes or adversely, death due to nosocomial infection or readmission. Am I on the right track or does the example have to be based on an a specific ILLNESS such as CA or pneumothorax for example using the Donabedian model of measuring health care
such as treatment process, stages of treatment, appropriateness of treatment and outcomes such as death, adverse events etc..
And do we have to go into depth about what the illness. or is it just simply demonstrating our understanding of the processes and outcomes data for an illness?
Thank you for your email.
Yes you are on the right track as you are discussing safety and quality of nursing care NOT the process or outcome of a disease.
And do we have to go into depth about what the illness. or is it just simply demonstrating our understanding of the processes and outcomes data for an illness?
If you were discussing infection you would need to discuss the impact of the infection.For example,
1. What would be the impact on the client/patient? eg prolonging of illness, complications, longer hospital stay, financial costs, psychological costs,
2. What would be the impact on the health organisation/hospital? eg Inefficiency, increased length of patient stay, bed blocking, increased costs of hospitalisation, possible court claim
3. What would be the impact on the state? eg Decreased productivity of the individual if they remain hospitalised and are unable to work, increased costs if client/patient a pensioner
Questi.
Running Head INFERENTIAL STATISTICS 1INFERENTIAL STATISTICS .docxjeanettehully
Running Head: INFERENTIAL STATISTICS 1
INFERENTIAL STATISTICS 3
Inferential statistics in health care
Name
Institution
Inferential statistics are important as they are used for comparing the differences between treatment groups. Various inferential statistics are applicable in healthcare; however, each is appropriate for research. I intend to look at the regression analysis throughout this paper.
The study was conducted to predict the emergency patient volume at the Indianapolis 500 miles race. Therefore, the researcher conducted the research and expected to have some numbers at the end of the study. The research question addresses the problem by trying to find out the population of patients during the race. Additionally, the research held weather conditions into consideration during the investigation. For the methodology, data was collected from the National Oceanic and Atmospheric Administration (Bowdish, Cordell, Bock, & Vukov, 1992). The data was from the patients that were treated between 1983 to 1989 Race Days. This data was collected from the facility hospital. Also, the regression analysis was done using the weather factors and race characteristics as independent variables, and the number of patients used as the independent variable. To test the validity of the model, data from 1990 was used.
` The regressive analysis is beneficial as it shows the relationship between the dependent and the independent variable. Moreover, I found out that regression analysis is ideal for giving forecast and predictions (Gunst, 2018). I looked at the results of the study, and they were as follows, there was a substantial relationship between the dew point and the patient load. Unfortunately, from the result, I found no correlation between wind, sunshine, humidity, number of patients, and race characteristics.
I believe the results of the study were accurate and presents the real picture. Therefore, I recommend that researchers who aim to compare variable always to use the regression analysis as it is useful. Additionally, it gives predictions on the future or likely happenings.
References
Bowdish, G. E., Cordell, W. H., Bock, H. C., & Vukov, L. F. (1992). Using regression analysis to predict emergency patient volume at the Indianapolis 500 mile race. Annals of emergency medicine, 21(10), 1200-1203.
Gunst, R. F. (2018). Regression analysis and its application: a data-oriented approach. Routledge.
COLLECTIVE BARGAINING AGREEMENT
BETWEEN
DISTRICT BOARD OF TRUSTEES OF
FLORIDA STATE COLLEGE AT JACKSONVILLE
AND
UNITED FACULTY OF FLORIDA -
FLORIDA STATE COLLEGE AT JACKSONVILLE
EFFECTIVE AUGUST 16, 2016
Florida State College at Jacksonville is a member of the Florida College System and is not affiliated with any other public
or private university or college in Florida or elsewhere.
Florida State College at Jacksonville is accredited by the Southern Association of Colleges and Scho ...
How to develop and evaluate a pro instrument - pubricaPubrica
PRO or Patient-reported outcome is a relatively novel method to evaluate the outcome of any treatment by clinician or researcher (in clinical trial) purely based on the inputs got from the patient alone.
Full Information : https://bit.ly/2VPA4uX
Reference : https://pubrica.com/services/data-analytics-machine-learning/
Why pubrica?
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A PROPOSAL ON WORKPLACE HEALTH PROMOTION, EDUCATION AND COMMUNICATION PROGRAM...Mohammad Aslam Shaiekh
A PROPOSAL ON
WORKPLACE HEALTH PROMOTION, EDUCATION AND COMMUNICATION PROGRAM AMONG THE MUNICIPAL SOLID WASTE MANAGEMENT WORKERS OF POKHARA METROPOLITAN CITY
Proposal Development on Organizing Health Promotion Education Communication T...Mohammad Aslam Shaiekh
Proposal Development on Organizing Health Promotion Education Communication Training Program on Maternal Infant and Young Child Nutrition Practices in Pumdi, Pokhara Municipality 22.
A PROPOSAL ON HEALTH PROMOTION, EDUCATION AND COMMUNICATION PROGRAM ON SCHOOL...Mohammad Aslam Shaiekh
A PROPOSAL ON
HEALTH PROMOTION, EDUCATION AND COMMUNICATION PROGRAM ON SCHOOL HEALTH NUTRITION AMONG THE PRIMARY LEVEL STUDENTS OF POKHARA METROPOLITAN-30, KASKI
Practicum presentation on Safe Motherhood Program (SMP) and Maternal and Peri...Mohammad Aslam Shaiekh
Practicum presentation on Safe Motherhood Program (SMP) and Maternal and Perinatal Death Surveillance and Response (MPDSR) Program in MNH Section of Family Welfare Division..
Practicum presentation on Safe Motherhood Program (SMP) and Maternal and Peri...Mohammad Aslam Shaiekh
Practicum presentation on Safe Motherhood Program (SMP) and Maternal and Perinatal Death Surveillance and Response (MPDSR) Program in MNH Section of Family Welfare Division
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
ABDOMINAL TRAUMA in pediatrics part one.drhasanrajab
Abdominal trauma in pediatrics refers to injuries or damage to the abdominal organs in children. It can occur due to various causes such as falls, motor vehicle accidents, sports-related injuries, and physical abuse. Children are more vulnerable to abdominal trauma due to their unique anatomical and physiological characteristics. Signs and symptoms include abdominal pain, tenderness, distension, vomiting, and signs of shock. Diagnosis involves physical examination, imaging studies, and laboratory tests. Management depends on the severity and may involve conservative treatment or surgical intervention. Prevention is crucial in reducing the incidence of abdominal trauma in children.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Tests for analysis of different pharmaceutical.pptx
PRACTICAL SKILL DEVELOPMENT (PSD) ON PRACTICE OF DATA MANAGEMENT AND ANALYSIS USING DIFFERENT SOFTWARES.
1. PRACTICAL SKILL DEVELOPMENT (PSD)
ON
PRACTICE OF DATA MANAGEMENT AND ANALYSIS USING DIFFERENT
SOFTWARES
Mohammad Aslam Shaiekh
Master of Public Health Program
School of Health and Allied Sciences
Faculty of Health Sciences
Pokhara University
Kaski, Nepal
May 2019
2. PRACTICAL SKILL DEVELOPMENT (PSD)
ON
PRACTICE OF DATA MANAGEMENT AND ANALYSIS USING DIFFERENT
SOFTWARES
Mohammad Aslam Shaiekh, 18700003
A PSD report
Submitted
(In Partial Fulfillment of the Requirements for the Degree of MPH 2nd
semester, Advanced Public
Health Research, PSD 524)
Master of Public Health Program
School of Health and Allied Sciences
Faculty of Health Sciences
Pokhara University
Kaski, Nepal
May 2019
3. i
APPROVAL
Mr. Mohammad Aslam Shaiekh has prepared the PSD report entitled “Practice of data management
and analysis using different software.” The PSD report has been prepared and presented for the
partial fulfillment of the requirement for the degree of Master of Public Health (MPH) and
forwarded for final evaluation.
_____________________
(Dr.Tulsi Ram Bhandari)
Date:
Master of Public Health (MPH) Program, School of Health and Allied Sciences, Faculty of
Health Sciences, Pokhara University, Pokhara Metropolitan-30, Kaski, Nepal.
This report has been reviewed and accepted
Accepted with condition
Not accepted
External Examiners
1.Name: _____________________ Signature: __________________ Date: _________
2. Name: _____________________ Signature: __________________ Date: __________
_______________________ ______________________________
(Mr. Chiranjivi Adhikari) (Dr. Damaru Prasad Paneru) School Seal
Program Coordinator Director
4. ii
DECLARATION
To the best of my knowledge and belief I declare that this PSD report entitled “Practice of data
management and analysis using different software.” is the result of my own practical skill
development work and contains no material previously published by any other person except where
due acknowledgement has been made. This PSD report contains no material, which has been
accepted for the award of any other degree or diploma in any university.
Signature:
Mohammad Aslam Shaiekh
Roll No: 18700003
PU Regd No: 2018-4-70-0003
Date: 28th
May 2019
5. iii
APPROVAL.......................................................................................................................................................i
DECLARATION................................................................................................................................................ii
2. Create a Google Form & Spreadsheet to collect information.................................................................. 7
3. Create data entry form: Excel, SPSS, Epidata ......................................................................................... 15
Data export to text ..................................................................................................................................... 22
5. Data cleaning.......................................................................................................................................... 23
6. Normality test of data (in SPSS)/Methods of normality test................................................................. 29
7. Merge and split of data set/s in SPSS and epidata................................................................................. 32
8. Select cases in SPSS/filter option/create subsets................................................................................... 33
Creating a subset :...................................................................................................................................... 33
.................................................................................................................................................................... 34
9. Preparation of data analysis plan........................................................................................................... 34
10. Data analysis using Excel/SPSS/SAS...................................................................................................... 34
11. Type of Analysis: univariate, bivariate, and multivariate..................................................................... 36
12. Create, tables/Academic tables and figures in excel, SPSS.................................................................. 36
Creating figures in SPSS.............................................................................................................................. 36
.................................................................................................................................................................... 37
13. Sample size calculation......................................................................................................................... 37
14. Organization of reviewed literature using referencing software: Zotero............................................ 39
6. 1
1. Create/Adopt Interview Schedule/Guideline/Checklist
1.1 Define variable/item/construct:
A variable is a characteristics or attributes that can vary from person to person, from time to time or from
place to place. A variable is something whose magnitude can be changed and can takes on different values.
Variable is an empirical property that can take two or more values. These may be in the form of
numbers or non-numerical characteristics. For example weight, height, temperature, blood pressure etc
are the quantitative variables. Likewise, age, sex, education, occupation etc are the qualitative variables.
Variables can be classified in several ways. Some of the commonly used variables are as followings:
1. Discrete and Continuous variable:
2. Dependent and Independent Variable
3. Qualitative and Quantitative variables
4. Extraneous and Intervening variables
On the basis of characteristics variables are of two types i.e. Quantitative and qualitative.
Qualitative variables: Characteristics that is not measurable but expressed in description.For
example Sex, nationality, honesty etc. That have labels or names rather than numbers. Qualitative
variable can be categorize under Nominal and Ordinal.
i. Nominal: Only names the variable value. It classify the cases such as gender, caste, religion,
blood group. Each category can be assigned to a unique numeral. These variables are
distinct, exhaustive and mutually exclusive. There are two types of nominal variable i.e
dichotomous and multi/ polychotomous.
ii. Ordinal: Ordinal variable involves classification and magnitude. Those types of variables
with an ordered in series, mutually inclusive. For example Nutrition status: Mild, Moderate
and severe
Quantitative variables: Characteristics that is measurable and can be expressed in numerical form
e.g. height, weight, temperature etc. Arithmetic functions can be done. Quantitative variables further
classified into two types.
i. Continuous variable: Expressed in fractions or decimals eg. Body temperature, height,
weight etc.
7. 2
ii. Discrete (Numerical) variables: These variables can be expressed in whole numerical
value. Eg. number of death in a hospital per year, number of family member.
On the basis of relationship with each other
i. Dependent variables: Describe or measure the problems, depends upon independent
variables
ii. Independent variables Describe or measure the factors that are assume to cause or least
influence on dependent variables
iii. Confounding variables: An extraneous variable associated with the problem and possible
cause of the problem, may either strengthen or weaken the apparent relationship between the
problem and possible cause.
1. 2 Generation of items pool
Generation of items pool should reflect the focus of the scale. Generation of items pool is a
developing a series of statements relating to the variable being measured by using general criteria
for statement.
1.3 Reliability and validity measurement
Validity means that scientific observations actually measure what they intend to measure. Validity
refers to the soundness of the observations and to the accurateness of the data collected by research
methods/instruments.
Types of validity
8. 3
a. Face and content validity: An instrument is measuring what it supposed to is primarily based
upon the link between questions and objectives of study. Each question or item on the research
instrument must be in logical link with objective. Establishment of this link is face validity.
Item and question covers full range of issue or attitude being measured. Assessment of the items of
an instrument in this respect is called content validity.
b. Concurrent and Predictive validity is judged by how well an instrument compares with a
second assessment concurrently. Predictive validity is judged by the degree to which an instrument
can forecast outcome
c. Construct validity: this type of validity is a more sophisticated technique for establishing the
validity of an instrument. It is based upon statistical procedures. It is determined by ascertaining the
contribution of each construct to the total variance observed in a phenomenon.
Reliability: A result is said to be reliable if the same result is obtained when the study is repeated in
same conditions.
Test/retest method
Parallel forms of same test
The split half technique
1.4 Psychometric analysis of the scale
In order for any scientific instrument to provide measurements that can be trusted, it must be both
reliable and valid. These psychometrics are crucial for the interpretability and the generalizability of
the constructs being measured.
Reliability is the degree to which an instrument consistently measures a construct -- both across
items (e.g., internal consistency, split-half reliability) and time points (e.g., test-retest reliability).
One of the most common assessments of reliability is Cronchbach alpha a statistical index of
internal consistency that also provides an estimate of the ratio of true score to error in Classical Test
Theory. A general rule of thumb is that solid scientific instruments should have a Cronbach‟s Alpha
of at least 0.7.
http://blog.motivemetrics.com/psychometrics-101-scale-reliability-and-validity
Test Procedure in SPSS Statistics
a. Click Analyze > Scale > Reliability Analysis
9. 4
b. Transfer the variable Q1 to Q10 into the items box. You can do this by drag-and-dropping the
variables into their respective boxes or by using the arrow. You will be presented with the
following screen:
c. Leave the Model set as "Alpha", which represents Cronbach's alpha in SPSS Statistics. If you
want to provide a name for the scale, enter it in the Scale box. Since this only prints the name
you enter at the top of the SPSS Statistics output, it is certainly not essential that you do (in
our example, we leave it blank).
d. Click on the statistics button, which will open the Reliability Analysis: Statistics dialogue
box, as shown below:
10. 5
e. Select item, Scale and Scale if item deleted options in Descriptives for area and Corelation item
in Inter item and click on Continue as shown below:
f. Click on Ok to generate Output .
g. SPSS Statistics produces many different tables. The first important table is the Reliability
Statistics table that provides the actual value for Cronchbach alpha, as shown below:
From our example, we can see that
11. 6
Cronbach's alpha is 0.805, which indicates a high level of internal consistency for our scale with this
specific sample
This column presents the value that Cronbach's alpha would be if that particular item was deleted
from the scale. We can see that removal of any question, except question 8, would result in a lower
Cronbach's alpha. Therefore, we would not want to remove these questions. Removal of question 8
would lead to a small improvement in Cronbach's alpha, and we can also see that the "Corrected
Item-Total Correlation" value was low (0.128) for this item. This might lead us to consider whether
we should remove this item.
Cronbach's alpha simply provides you with an overall reliability coefficient for a set of variables
(e.g., questions).
12. 7
2. Create a Google Form & Spreadsheet to collect information
2.1 Create form
Go to google page > Goggle apps > Drive > New > More > Google form
20. 15
3. Create data entry form: Excel, SPSS, Epidata
3.1 Create data entry form in Excel
3.1.1 Leveling and form creation
21. 16
3.1.2 Data entry using form
3.1.3 Filter option
Select the row to activate filter .
Go to Data > Click on Filter as shown
3.1.4 Validation option (Range, dropdown menu)
For data validation,
Goto > Data > Select Data validation.
22. 17
A dialog box with data validation appears.
Go to setting > Select list among various option in Allow > Enter the variables in Source as shown
For a whole number : Select whole number in Allow > Select between in data and enter the
maximun and minimum value > Click ok
Conditional formatting
a. Open excel > Select the column
b. Select conditional formatting > Select (Format only cells that contains)
c. Enter the cell value less than and the 25000 as shown below > Click on format
23. 18
d. Select yellow colour to fill yellow colour who has salary less than 25000
e. Click on Ok
24. 19
3.1.5 Input message
Input messege is used to describe the variable
3.2 SPSS
3.2.1 Creating three files
Creating three files in SPSS. These are given below.
3.2.2 Data file, syntax file and output files
Data file: Open SPSS > File > New > Data.
Data file has two view one is variable view and another is data view. In variable view questionnaire
are develop and data are entered in data view.
Syntax file:
New syntax file are developed by using following command.
Open SPSS > File > New> Syntax file
Syntax file auto opens whenever a command is given.
Output file:
Any result of the command given is shown on output file.
25. 20
3.3 Epidata (overview of epidata)
3.3.1 Overview of work process tool bar
The Epidata interface
.
When a new form is opened you will see several toolbar options below the work process tools.
3.3.2 Creating three files
1. Creating new Qes file:
Go to Define data > New Qes files
2. Creating data files from Qes files:
Go to make data files > field pick list
ID Number: <idnum>
For string values of values: Shift_
Defining attributes of variables: #
Defining Numeric value: #
3. Check file:
Go to checks > select file
Range label for no. of attributes for specific variable (eg 1 Yes, 2 No )
Input values or attributes > Accept and close
Click edit > Type comment > Accept and close > save
Jump file: Define jump – Eg: if answer is 2 (No) > q2
26. 21
4. Enter data:
Go to enter data > select file > open > data entry and save at last in appropriate folder.
4. Export data:
Go to export data > select file > define what you want to export in.
4. Data export/transform: Excel to SPSS, SPSS to Excel, Epidata to
Excel/SPSS/SAS/Text
4.1 Data transform from Excel to SPSS
a. Open SPSS > File > Open > Data> Select folder of data > Click on File types > Click on Excel
file in file name > Open > Tick on read variable names from the first row of data > ok.
4.2 Data export from SPSS to Excel
Open SPSS main file > then go to file > Save as > select SPSS file in file name> save as type >
Excel 2007 through 2010 XLSX save.
4.3 Data export from Epidata to Excel/SPSS/SAS/Text
1. Open Epidata > Export data > all the options can be selected as shown below
Data export to excel
2. Open epidata > Export data > Excel
27. 22
3. Select the rec file and Click on Open. A dialog box appears, it gives the option of exporting to a
specific place in the computer. You can also select records which ever you want to export and also
the variables and click on Ok.
5. You can find the excel file in the given destination.
Data Export to SPSS
1. Open epidata > Export data > SPSS
All the process will be same as the excel file but in the file name will be saved as .sps which means
it is a syntax file. While exporting to spss it will give you two file one will be syntax file and
another will be .txt file.
We need to open syntax file and run the command as per the instruction given .
Data export to SAS
1. Open epidata > Export data > SAS
All the process will be same as the excel file but in the file name will be saved as .sas .While
exporting to SAS it will give you two file one will be syntax file and another will be .txt file.
We need to open syntax file and run the command as per the instruction given.
Data export to text
Open epidata > Export data > Text
28. 23
Filename will be saved as .txt . Other steps are similar
5. Data cleaning
Data cleaning is preparing data for the analysis.
5.1 Check frequency (Find missing value and addressing it/replace missing value by mean)
Missing value
The reasons for missing data could be :
Participants forgetting to answer the question
Incorrectly answering the questionnaire
Data entry error
Step 1
Check frequency of that variables
Analyze > Descriptive Statistics > Frequencies
Find the variable you need, click on arrow, and Ok
Output view is shown.
The frequency table above shows that there are two items missing DM item 1 and DM Item 2.
Check those two items to understand the reason of missing.
29. 24
In case of data entry error go back to your data sheet and re- enter. If participant left item blank you
need to use replace value functions.
1. Go to Transform > Replace missing value function
2. Select series mean in method
30. 25
Missing values has been replaced.
To make the data set clean the new values can be copied to the original items .
5.2 Find range/outliers and treating the outliers
To check for outliers in SPSS:
1. Analyze > Descriptive Statistics > Explore...
2. Select variable (items) > move to Dependent box.
3. Click Statistics... > tick Outliers > Continue... > OK.
4. In Output window: Go to Boxplot > Look at circles and *. These are potential outliers. If
there's none, then there is no potential outlier in your dataset. If there are circles or *, then
there are potential outliers in your dataset.
5. To check if the outliers affect your data:
In the output window: Look at Descriptive table > Compare 5% trim mean and mean values.
If there's a large difference between these values, then there's huge possibility that your
further analyses, e.g. correlation and regression, will be affected. 5% trimmed mean is the
mean that slashes out 5% of the extreme ends (both lower and higher ends) of your dataset.
31. 26
Treating the outliers
Leave it if it is a legitamate outliers – use a non parametric test for skewed dataset
Correct data entry error in case of error
Winsorize the data (it means to trim the data )
Remove the data from the dataset in case of illegimate outliers, it is necessary to explain
your reasons for removal of the data such as multivariate outlier
5.3 Reverse coding (Negative statement of Likert scale)
Questionnaires that use a Likert scale (eg. strongly disagree, disagree, neutral, agree, strongly agree)
for answering questions often contain some items which are to be reverse scored.
For example, in a self-esteem questionnaire we may have some positively worded questions (eg. I
take a positive attitude toward myself), but also some negatively worded questions (eg. At times, I
think I am no good at all).
In the above example, we might attribute an answer of strongly disagree with a score of 1, disagree
= 2, neutral =3, agree = 4 and strongly disagree =5 for each question. This would be fine for the
positively worded questions, as this would give people with high self-esteem a high score, however,
we can‟t use the same scoring for the negatively worded questions.
Instead what we do is reverse score the negatively worded questions. Reverse scoring means that the
numerical scoring scale runs in the opposite direction. So, in the above example strongly disagree
would attract a score of 5, disagree would be 4, neutral still equals 3, agree becomes 2 and strongly
agree = 1.
The same principle applies regardless of the length or wording of Likert Scale being used. For
example, we might have the following 7 point scale:
Disgusting Horrible Unpleasant Neutral Pleasant Lovely Adorable
1 2 3 4 5 6 7
...which for reverse scored questions becomes:
Disgusting Horrible Unpleasant Neutral Pleasant Lovely Adorable
7 6 5 4 3 2 1
32. 27
After you have reverse scored the necessary items in your scale, you can then calculate the total
score for your questionnaire.
Recoding into different variable
Current value labels
Go to Transform > Recode into different variable
In this example I want to reverse score a 5 point Likert Scale, so 1 becomes 5, 2=4, 3=3, 4=2 and
5=1. In the „Old Value‟ box on the left, enter 1 and in the „New Value‟ box on the right enter 5.
Then click Add to move the values into the „Old-->New‟ box. Repeat this process for all the values,
including those that will be staying the same (eg. 3 still stays as 3 in this example)
33. 28
A new variable has been added . Put the label and values
Remember to use these new re-coded columns in any total score calculations or analyses, not the
original columns.
Recode into same variable
You can also go to Transform – Recode into Same Variables to recode data, however this will
overwrite the original data, so if you are not confident with recoding data it is safer to use the
Recode into Different Variables option.
34. 29
6. Normality test of data (in SPSS)/Methods of normality test
6.1 Skewness and kurtosis
Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data
set, is symmetric if it looks the same to the left and right of the center point.
Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal
distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with
low kurtosis tend to have light tails, or lack of outliers. A uniform distribution would be the extreme
case.
The histogram is an effective graphical technique for showing both the skewness and kurtosis of
data set.
6.2 Normality test
Step 1
Select "Analyze -> Descriptive Statistics -> Explore".
Step 2
From the list on the left, select the variable "Data" to the "Dependent List".
Click "Plots" on the right. A new window pops out. Check "None" for boxplot, uncheck everything
for descriptive and make sure the box "Normality plots with tests" is checked.
35. 30
Click on Ok
Step 3
The results now pop out in the "Output" window.
Step 4
We can now interpret the result.
The skewness and Kurtosis should be as close to zero as possible. In reality however the data are
skewed and kurtotic. A small departure from zero is therefore no problem, as long as the measures
are not too large as compared to their standard error.
We must divide the measures by its standard error. This will give the z value which is in between
+1.96 to – 1.96 .From above output,
Skewness= 0.374/0.365= 0.374
Kurtosis= 0.584/0.717= 0.81
36. 31
The value are in between +1.96 to -1.96. Hence the data are little skewed and kurtotic but they don‟t
differ significantly from normality.
6.2.1 Shapiro Wilk
The Shapiro wilk test p- value must be above 0.05 to reject alternative hypothesis and conclude that
the data comes from a normal distribution.
6.2.1 Kolmogorov-Sminov
Reject the null hypothesis if p> 0.05
6.3 Probability test plot
6.3.1 Q-Q Plot
The dots should be along the line for a variable to be normally distributed.
6.3.2 Box plot
37. 32
7. Merge and split of data set/s in SPSS and epidata
Merging file in SPSS
Open SPSS > go to file > open > data> example employee dataset 1 > merge file > add cases> An
external SPSS statistics file > browse > add cases > employee data set 2 > open > continue> Paste >
syntax > run > merge file > save as > concerned folder
Split of data in SPSS
Go to Data > Split file > Select compare groups > Select the variable (eg: educational level) > Click
on Ok
On the data viewer you can see split file by educational level on lower right corner. Further analysis
can be done for educational level.
In split file the option of output by groups can also be used instead of compare group it gives the
same result but only output view is changed.
38. 33
8. Select cases in SPSS/filter option/create subsets
Open SPSS data file then go to Data > Select cases > Select If condition satisfied > Click on If >
Select the variable > Example Gender> Write Gender =3 > Continue > Paste > syntax run > output
window
Filter on Option is shown on lower right. Repeat the same procedure and click on reset to turn the filter off.
Creating a subset :
Repeat the procedure of select cases > enter the option in if condition and then select in Output > Copy
selected cases to a new dataset > enter the name of the data set > Click ok
A new data set is shown in data viewer > Save the data file
39. 34
9. Preparation of data analysis plan
A detail plan is to be made on how to analyze the collected information to meet the desired
objectives of the study based on study variables. For this, study variables should be identified,
measurement scale of variables and univariate/bivariate/multivariate analysis plan should be
declared. For example:-
Variables : Dependent (Outcome ) and Independent (Explanatory) Variables
Measurement scales : Nominal, Ordinal, Discrete and Continuous
Univariate analysis: Percentages, IQR, Mean, Median, Mode etc.
Bivariate analysis: Chi-square tests, Binary logistic regression etc.
Multivariate analysis : Multiple logistic regression
10. Data analysis using Excel/SPSS/SAS
10.1 Data analysis using excel
Analysis of data by using following command
40. 35
Open the Microsoft excel data file > click on data > go to Data analysis > then select data analysis
tool like descriptive statistics ( mean, median, mode, range, standard deviation),confidence interval,
t- test, Simple linear regression and scatter diagram etc as per need.
Analysis of descriptive statistics
Open the Microsoft excel data>then click on data> data analysis > analysis tool > select descriptive
statistics > Ok > Select column to calculate a mean median mode standard deviation and range >
input range > select all value of calculating column >Tick on label in first row > Output range
(click on empty box below the given column) > click on summary statistics then Ok.
After calculating the value of standard deviation and mean we can calculate coefficient of variation
through manually by applying the following formula. i.e = (Standard deviation/Mean)*100.
Analysis of confidence interval
Open the Microsoft excel data > select variable to calculate CI. For example we select age > then go
to data > data analysis > descriptive statistics > Input range > select all value of defined column >
tick on label on first row> Output range (click on empty box below the given column) > click on
summary statistics > Confidence label for mean 95% then Ok.
t-test analysis: two sample assuming equal variances
Open the Microsoft excel data file > select a variable having two options > go to data > Sort > sort
by > select variable which you prefer > then go to order click on smallest to largest > Ok> then go
to ms excel > Type select variables of option 1 in one column and option 2 in another column >
then copy and paste a given value in select variable then go to data > data analysis> t test two
sample assuming equal variances > ok > then go to variable first range > select all value of first
range then go to second column of select variables. > here as also select all value of second column
> type 0 on hypothesis main differences > tick on labels> Alpha value 0.05 > click on output range
at empty box of excel > ok
To compare t stat value with t critical two value if t stat value is less than t critical value we cannot
reject null hypothesis. Therefore we cannot conclude that there is any differences between the
values of X.
41. 36
10.2 Data analysis using SPSS
Open SPSS file>Analyze>Descriptive>
Frequencies
Cross tabulation
Explore
11. Type of Analysis: univariate, bivariate, and multivariate
Univariate analysis: Go to analyze>Mean, mode, median, frequency
Bivariate analysis: Go to analyze> Crosstab Eg. Chi square test
Multivariate analysis: Go to Analyze > Descriptive statistics/Correlation/Regression
12. Create, tables/Academic tables and figures in excel, SPSS
Creating figures in SPSS
Go to Analyze > Descriptive > Frequency > Select the variable > Click on Chart > Select the
required figures > Click on Ok
The out put is shown.
The colour of the figure can be changed by double clicking on charts > Select the required function
in Chart editir to modify charts.
Another way ,
Go to Graphs > Legacy dialog > select the required figure.
Creating figures in Excel
Enter the data > Select the data > Click on Insert > Select the option of figures > The diagram
appears > Diagram can be modified by clicking on it.
42. 37
Creating table in SPSS
13. Sample size calculation
13.1 Manual calculation of sample size
Using mean
i) n = Z2
/2
2
( Where Z /2 = 1.96, 2
= variance )for infinite population
d
ii) n = Z2
/2
2
In Case of finite population(when N is known)
d2
+ Z2
/2
2
N
Using Proportion
i) n = Z2
/2pq(Where Z /2 = 1.96) In case of infinite population
d2
43. 38
ii) For finite population of size ‘N’
n = no , no = Z2
/2pq
1+ no/N d2
(P = Probability of success, q = Probability of failure)
13.2 Using open epi
Open open epi through google search and choose sample size for proportion, mean difference etc.
Then enter new data, give population size, expected percentage, confidence level and design effect
Openepi>sample size>proportion>enter new data> calculate
44. 39
14. Organization of reviewed literature using referencing software: Zotero
Zotero: Zotero offers users a variety of ways to capture, import and archive item information and
fles. Zotero automatically captures bibliographic information from web. Zotero‟s book icon will
appear in Firefox‟s location bar (at the top of the browser window, where the current web address,
or URL, appears), like so: Simply click on the book icon and Zotero will save all of the citation
information about that book into your library.
Open Zotero > click on new collection > rename > save
If we are looking at a group of items (e.g., a list of search results from Google Scholar), a folder
will appear. Clicking on the folder will produce a list of items with check boxes next to them;
choose the ones you want to save and Zotero will do the rest.
Go to Google Scholar>Search “title”>click on save to Zotero (Zotero item selector)>choose &OK
45. 40
For citation when we would like to cite something from our collection click the first button, “Zotero
Insert Citation” ( ). If this is the first citation we have added to the document the Document
Preferences window will open. Chose the bibliographic format we would like to use from the list
and click OK.
46. 41
Eg. Maternal Anemia, as determined by low hemoglobin or hematocrit, is common among
women in their reproductive years in particular if the women are poor, pregnant, and members
of an ethnic minority. (Scholl, 2005)
47. 42
REFERENCES: Scholl, T.O., 2005. Iron status during pregnancy: setting the stage for
mother and infant. Am. J. Clin. Nutr. 81, 1218S–1222S.
48. 43
15. Basic format of academic research proposal.
A. Preliminaries
i. Front Cover Page
ii. Approval Page
iii. Table of Contents
iv. List of Abbreviations
B. Body
CHAPTER I: INTRODUCTION
1.1. Background
1.2. Justification of the study
1.3. Research Questions
1.4. Objectives of the study
1.5 Conceptual Framework
1.6 Operational definitions
1.7 Expected Outcome
CHAPTER II: LITERATURE REVIEW
2.1 Introduction
2.2 Literature search methods and strategies
2.3 Literature review
CHAPTER III: METHODOLOGY
3.1. Study Method
3.2. Study Type
3.3. Study Population
3.4. Study Area
3.5. Study Period
3.6. Sample Size
3.7. Sampling Technique
3.8. Selection Criteria: Inclusion and Exclusion Criteria
49. 44
3.9. Data Collection Technique
3.10. Data Collection Tools
3.11. Pretesting of the Tools
3.12. Data Management and Analysis
3.13. Quality Control and Quality Assurance
3.14. Ethical Consideration
C. Supplementary Section
References
Appendixes
a. Informed Consent
b. Data Collection Tools
c. Gantt Chart
d. Map of study area
e. Budget