Optometry Semester 2

OPTOMETRY · SEMESTER 2 Additional Study Notes: Screening Measures and Worked Data Examples Epidemiology and Biostatistics Additional Study Notes — newly authored explanations and examples. These sections supplement the supplied course material. START READING NOTES Contents of This Topic Learning objectives Prevalence and incidence An original screening table Avoiding data errors Learning objectives Calculate prevalence and basic screening-test measures; identify their denominators; and distinguish a screening result from a confirmed diagnosis. Prevalence and incidence Prevalence describes existing cases in a defined population at a specified time or during a stated period. Cumulative incidence describes new cases during follow-up among people initially at risk. An incidence rate uses person-time in the denominator. Always state the population, condition definition and time frame. Original example: if 60 of 300 examined students have the defined condition on the survey date, sample prevalence is 60/300 = 20%. This estimate does not automatically describe all students in the district; the sampling process, participation and measurement method affect generalisability. An original screening table Hypothetical results for 1,000 people with a reference assessment: 90 true positives, 10 false negatives, 180 false positives and 720 true negatives. These counts are invented for calculation practice. Sensitivity = TP/(TP+FN) = 90/100 = 90%. Specificity = TN/(TN+FP) = 720/900 = 80%. Positive predictive value = TP/(TP+FP) = 90/270 ≈ 33.3%. Negative predictive value = TN/(TN+FN) = 720/730 ≈ 98.6%. The disease prevalence in this example is 100/1,000 = 10%. Despite high sensitivity, only about one-third of positive screens are true positives. Predictive values depend on the population prevalence as well as test performance. A positive screening result needs the appropriate confirmatory pathway. Avoiding data errors Keep raw observations, units and coding definitions. Use a distinct code for missing data rather than silently entering zero. Check impossible values, duplicate records and inconsistent eye or patient identifiers. Define whether an analysis is per person or per eye: two eyes from one person are not automatically independent observations. Report the sample size and denominator with every percentage. For a small or skewed dataset, examine the distribution before choosing summary measures. A median describes the middle ordered observation; a mean uses all values and is sensitive to extreme observations. Study References Source notes: Concepts of Biostatistics; Data Collection; Introduction to Epidemiology CDC: Evaluating Public Health Surveillance Systems — predictive value External references checked 13 September 2026. Worked numerical examples and teaching activities are original. ← PREVIOUS TOPICVIEW MODULE NOTESVIEW SEMESTER NOTESALL OPTOMETRY NOTES Need These Notes as PDF? Request a formatted copy for offline study, printing or revision. GET PDF NOTES ON WHATSAPP

OPTOMETRY · SEMESTER 2 Employ Knowledge Of Statistics In Collecting Health Data Epidemiology and Biostatistics START READING NOTES Contents of This Topic Related Tasks Definition Main tools used in statistical data collection 1. QUESTIONNAIRE Advantages of Questionnaire Disadvantages of Questionnaires Qualities of a good Questionnaire 2. INTERVIEW How to conduct interview Advantages of Interview Disadvantages of Interview 3. OBSERVATION (Observation checklist) Advantages of Observation Disadvantages of Observation 4. FOCUS GROUP DISCUSSIONS (FGDs) Advantages of FGDs Disadvantages of FGDs 4. CENSUS Disadvantages of Census METHODS OF DATA COLLECTION A. Primary data collection methods Sources of primary data B. Secondary data collection method Sources of secondary data Unpublished sources Factors influencing the choice of data collection method HOW TO DEVELOP DATA COLLECTION TOOLS STEPS IN DEVELOPING DATA COLLECTION TOOLS Principles in Using data collection tools EMPLOY KNOWLEDGE OF STATISTICS IN COLLECTING HEALTH DATA Related Tasks Define the term data collection. Identify tools used in statistical data collection Describe main methods of collecting statistical data Develop tools for data collection Use tools for data collection Definition DATA COLLECTION is the systematic process of gathering, measuring, and recording information from individuals, groups, observations, or sources for the purpose of analysis, interpretation and decision making. It is an important step in statistics, biostatistics, epidemiology, and research because the quality of collected data directly affects the accuracy and reliability of study findings. Main tools used in statistical data collection Questionnaires Interviews Observation checklists Focus group discussions (FGDs) Rating scales (E.g. Likert scale) Medical and Administration Records Tests and Examinations census 1. QUESTIONNAIRE Is a written set of questions answered by respondents. TYPES OF QUESTIONNAIRE Structured questionnaires- fixed responses E.g. Yes/No Semi-structured questionnaires- Both fixed and open-ended questions Unstructured questionnaires- Mostly open-ended questions Advantages of Questionnaire Can collect large amounts of data Cost effective Time-saving Standardization of responses Easy to analyze No interview bias Disadvantages of Questionnaires Low response rate Misinterpretation of questions Limited depth of information Incomplete or missing data Not suitable for illiterate population Risk of dishonest or biased response Qualities of a good Questionnaire Clear instruction Demographics Grammar Proper space for response Simple questions As per objectives Pilot study 2. INTERVIEW Is a data collection tool which involves direct verbal interaction between the researcher and participant (interviewer and interviewee) It consists of a set of pre-written questions that are asked by an interviewer and filled in by the interviewer called Interview Schedule TYPES OF INTERVIEW Structured interview- standardized set of pre-determined questions in a fixed order. Semi-structured interview- Uses a pre-prepared set of guiding questions but allows the interviewer to ask additional probing questions. Unstructured (informal) interview- An interviewer asks open-ended questions. How to conduct interview Conducted as per objectives Plan for time, duration, place, mode of recording Respect not to answer right of subject Simple and appropriate language Positive body language Advantages of Interview Provides in-depth information Clarification of questions is possible High response rate Suitable for all literacy levels Allows probing and follow up questions Allows better control over data collection process Disadvantages of Interview Time-consuming Expensive Interview bias Requires skilled personnel Possibility of response bias Limited anonymity (respondents may feel uncomfortable sharing sensitive or personal information) 3. OBSERVATION (Observation checklist) In this tool a research observe and record behaviors, events or condition using a checklist. TYPES OF OBSERVATION Overt- subjects are aware Covert- subjects are not aware Participant Non-participant Advantages of Observation Provide direct information Captures the real-life behavior Useful when subjects can not communicate Minimizes response bias Provides contextual information Useful for verifying other data sources Disadvantages of Observation Observer bias Time-Consuming Limited scope of data Hawthorne effect Expensive in some cases Ethical concerns (observing people without their knowledge may raise privacy and consent issues) 4. FOCUS GROUP DISCUSSIONS (FGDs) A small group discussion guided by a moderator to obtain opinions and perception. Number of participants Minimum- 6 participants Optimal range- 6-8 participants (most commonly used in qualitative health research) Maximum- 10-12participants Advantages of FGDs Generates rich qualitative data Encourage interaction among participants Efficient data collection method (large amount of information can be collected from several participants at the same time) Explores diverse perspectives Helps in identifying new idea or issues Useful for understanding social norms and behavior Cost effective compared to individual interview Disadvantages of FGDs Dominance by certain participants Lack of confidentiality Group pressure (conformity bias)- Individuals change their opinions, attitudes, or responses to match those of a group) Requires skilled moderator Logistical challenges 4. CENSUS It involves the complete enumeration of all members of a defined population at a specific point in time It aims to gather detailed information about demographic, social , and economic characteristics. Advantages of census No sampling bias High accuracy and reliability Detailed information Benchmark data Disadvantages of Census Very expensive Time consuming Not suitable for frequent studies METHODS OF DATA COLLECTION Statistical data collection methods are systematic approaches used to gather information for analysis, interpretation, and decision making in research, epidemiology, public health and biostatistics. These methods are broadly divided into, Primary data collection methods Secondary data collection methods A. Primary data collection methods Observation method Interview method Questionnaire method Experimental method Focus group discussion Measurement method Sources of primary data Direct observation Experiments Focus group discussion Interview Survey and field studies Measurements and tests Case studies B. Secondary data collection method Secondary data are data already collected by others for another purpose. Sources of secondary data Published sources Government reports Journals Census data WHO reports Books Unpublished sources Hospital records Institutional data base Research theses Factors influencing the choice of data collection method Research objectives Nature of data needed Available time Cost and resources Literacy level of participants Population size Accuracy required HOW TO DEVELOP DATA COLLECTION TOOLS This is the systemic process of designing instruments used to gather accurate, reliable, and relevant information for research, epidemiology, biostatistics and public health studies. Data collection tools should be carefully developed to ensure the quality of data collected. STEPS IN DEVELOPING DATA COLLECTION TOOLS There are four steps in developing data collection

OPTOMETRY · SEMESTER 2 Describe Concepts Of Epidemiology In Disease Prevention Epidemiology and Biostatistics START READING NOTES Contents of This Topic Learning Objectives Explain factors related to health and disease Epidemiology Definition of epidemiology Describe Concepts Of Epidemiology In Disease Prevention Study of the distribution (pattern of disease i.e. time, place, person). Definition of terms used in epidemiology Definition of terms cont… Definition of Health Describe natural history of disease Stages of Pathogenesis Factors in Pre-Pathogenesis Factors in Pathogenesis Outline key applications and achievements of epidemiology Achievements of epidemiology The determinants of health The determinants of health include; Key Points Key points cont… aims/Uses/Applications of epidemiology reference Describe the concepts of epidemiology in disease prevention Learning Objectives By the end of this session, students are expected to be able to: Define terms used in epidemiology Define health and disease Describe Natural History of disease Outline key applications and achievements of epidemiology Explain the determinants of health and disease -INTRODUCTION TO EPIDEMIOLOGY Explain factors related to health and disease Distinguish factors related to health and disease -INTRODUCTION TO EPIDEMIOLOGY Epidemiology The word epidemiology comes from the Greek words epi = meaning on or upon demos = meaning people logos = meaning the study of or doctrine Thus the word “epidemiology” simply means the study of what is happening to people -INTRODUCTION TO EPIDEMIOLOGY Definition of epidemiology As per perkins 1873 it is that branch of science which treats epidemics As per frost 1927 it is the science of mass phenomenon of infection disease. As per greenwood 1934 the study of any disease as a mass phenomena -INTRODUCTION TO EPIDEMIOLOGY Describe Concepts Of Epidemiology In Disease Prevention Epidemiology is often more concerned with the well-being of society as a whole, than with the well-being of individuals. In these definitions, three components are common. Study of the disease frequency(frequency of disease, disability or death and summarizing in the form of rates and ratios). -INTRODUCTION TO EPIDEMIOLOGY Study of the distribution (pattern of disease i.e. time, place, person). Study of determinants (underlying cause or risk factors). -INTRODUCTION TO EPIDEMIOLOGY Definition of epidemiology « The study of the distribution and determinants of health related states or events in specified populations and the application of this study to the control of health problems. -INTRODUCTION TO EPIDEMIOLOGY Definition of terms used in epidemiology Distribution is concerned with the frequency and pattern of health events in a population Distribution: What, who, when, and where Frequency: number, rates, and risk Quantify diseases to determine magnitude Patterns: time, place, and person -INTRODUCTION TO EPIDEMIOLOGY Definition of terms cont… Frequency: refers to the number of health events e.g. Number of cases of meningitis, Number of cases of diabetes in a population, Number of people with mental disorder in a population, Number of children under one year of age vaccinated for measles. Pattern of disease refers to the occurance of health related events or disease by time, place and person. -INTRODUCTION TO EPIDEMIOLOGY Definition of Health Health: A state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity. (World Health Organization) Definition of Disease Disease: A disorder of structure or function in a human, especially one that produces specific symptoms or that affects a specific part. Definition of agent Is an microorganism/microbe that is cable of causing a disease -INTRODUCTION TO EPIDEMIOLOGY Describe natural history of disease Definition: Natural history of a disease the course of a disease over time, unaffected by human interventions. Pathogenesis: Step-by-step origination and development of a disease. -INTRODUCTION TO EPIDEMIOLOGY Stages of Pathogenesis Pre-Pathogenesis: Occurs before the onset of disease; interaction between host, agent, and environment. Pathogenesis: Interaction leads to discernible lesions or symptoms. Intervention can shorten or prevent this. Post-Pathogenesis: Residual effects persist; new diseases may arise from post-pathogenesis of others. Factors in Pre-Pathogenesis Host Factors: Age, sex, social class, personality, genetic makeup, etc. Agent Factors: Presence of an etiological factor necessary for disease. Risk Factors: Fixed (e.g., age) or modifiable (e.g., smoking). Environmental Factors: Climate, altitude, population density, sanitation, etc. Factors in Pathogenesis Host-Agent Interaction: Time between onset and discernible disease or symptoms. Outcome: May lead to recovery, disability, or death.Human Intervention can terminate or shorten this process. Outline key applications and achievements of epidemiology The applications of epidemiology include; Disease surveillance Disease investigation Risk analysis Design of prevention strategies Natural history Diagnostic testing -INTRODUCTION TO EPIDEMIOLOGY Achievements of epidemiology Community diagnosis Tools for data analysis Foundation for modern Public health Integration into various fields -INTRODUCTION TO EPIDEMIOLOGY The determinants of health Many factors combine together to affect the health of individuals and communities. Whether people are healthy or not, is determined by their circumstances and environment. -INTRODUCTION TO EPIDEMIOLOGY The determinants of health include; Income and social status Education Physical environment Social support networks Biology and genetic endowment Health services (availability and accessibility) Gender -INTRODUCTION TO EPIDEMIOLOGY Key Points As a discipline within public health, epidemiology includes the study of the frequency, patterns, and causes of health-related states or events in populations, and the application of the information gained to public health issues. In epidemiology, our “patient” is the public at large—the community—and in “treating” our patient we perform several tasks, including public health surveillance, disease investigation, analytic epidemiology, and evaluation. -INTRODUCTION TO EPIDEMIOLOGY Key points cont… Epidemiology provides us with a systematic approach for determining What, Who, Where, When, and Why/How. We use descriptive epidemiology to describe disease occurrence by person (Who), place (Where), and time (When). We use descriptive epidemiology to portray the characteristics and public health of a population or community. We use analytic epidemiology to sort out and quantify potential risk factors and causes (Why). -INTRODUCTION TO EPIDEMIOLOGY aims/Uses/Applications of epidemiology Describe the distribution of diseases and burden in a population Identification of etiological factors in pathogenesis Provide and analyze information for planning, implementation and evaluation of health status Study the natural history of disease -INTRODUCTION TO EPIDEMIOLOGY reference Bety R. K., & Jonathan A. C. S. (2003) Medical Statistics (2nd Ed.),

OPTOMETRY · SEMESTER 2 Concepts Of Biostatistics In Managing Health Data Epidemiology and Biostatistics START READING NOTES Contents of This Topic Sub-Enabling Outcome a) Define terminologies used in biostatistics Key Concepts of Biostatistics in Health Data Management b) Data Organization and Classification c) Data Summarization (Descriptive Statistics) d) Data Analysis (Inferential Statistics) e) Interpretation of Results f) Data Presentation g) Decision-Making and Policy Formulation Summary Table b) Explain importance of biostatistics 2. Helps in Health Research and Innovation 3. Enables Disease Surveillance and Control 4. Assists in Planning and Policy Formulation 5. Facilitates Evaluation of Health Programs 6. Improves Quality of Health Data 7. Helps Understand Risk Factors for Diseases 8. Aids in Predicting Health Trends 9. Enhances Communication of Scientific Findings 10. Promotes Rational Use of Resources c) Explain importance of data stratification Importance of Data Stratification b) Helps Identify Hidden Trends and Patterns c) Supports Targeted Interventions d) Enhances Equity and Resource Allocation e) Facilitates Monitoring and Evaluation f) Reduces Bias and Misleading Conclusions g) Supports Health Research and Policy Development h) Improves Quality Improvement in Health Facilities d) Explain different types of biostatistical data Main Types of Biostatistical Data i. Nominal Data ii. Ordinal Data B. Quantitative (Numerical) Data Examples: ii. Continuous Data 3. Summary Table Importance of Knowing Data Types In Summary References….. CONCEPTS OF BIOSTATISTICS IN MANAGING HEALTH DATA Sub-Enabling Outcome 1.1.1 Describe concepts of biostatistics in managing health data Related Tasks a) Define terminologies used in biostatistics b) Explain importance of biostatistics c) Explain importance of data stratification d) Explain different types of biostatistical data a) Define terminologies used in biostatistics Definition of Biostatistics Biostatistics is the branch of statistics that applies statistical methods to biological, medical, and health-related research. It involves collecting, analyzing, interpreting, and presenting health data to support evidence-based decision-making in healthcare. Key Concepts of Biostatistics in Health Data Management a) Data Collection This involves gathering health information from sources such as surveys, hospital records, laboratory results, or public health surveillance systems. Biostatistics provides methods for designing valid sampling techniques and data collection tools to ensure accuracy and representativeness. Example: Selecting a random sample of patients to estimate disease prevalence in a community. b) Data Organization and Classification Health data are organized into categories or groups for easier analysis. Data may be quantitative (numerical) or qualitative (categorical). Biostatistics helps classify data into nominal, ordinal, interval, or ratio scales depending on the type of variable. Example: Classifying patients by blood group (nominal) or by disease severity (ordinal). c) Data Summarization (Descriptive Statistics) Biostatistics uses measures such as mean, median, mode, standard deviation, and range to summarize and describe data patterns. Graphical tools like bar charts, histograms, and pie charts help visualize the distribution of health data. Example: Calculating the average age of malaria patients or showing TB cases by region in a bar chart. d) Data Analysis (Inferential Statistics) Involves drawing conclusions or inferences about a population from a sample. Uses techniques such as hypothesis testing, correlation, regression, t-tests, and chi-square tests. Helps identify associations and causes in health outcomes. Example: Testing whether smoking is significantly associated with lung cancer incidence. e) Interpretation of Results Statistical findings are interpreted in the context of health and medical knowledge. Biostatistics helps health professionals understand whether observed patterns are due to chance or real associations. Example: Interpreting that an increase in vaccination coverage is statistically linked to reduced measles outbreaks. f) Data Presentation Biostatistics emphasizes clear presentation of results through tables, graphs, charts, and reports. Proper visualization aids communication of findings to policymakers, researchers, and the public. Example: Presenting maternal mortality trends in a line graph to guide national health planning. g) Decision-Making and Policy Formulation Statistical evidence guides public health decisions, program evaluation, and policy development. Enables assessment of intervention effectiveness, disease trends, and health system performance. Example: Using biostatistical analysis to determine whether a malaria control program effectively reduced infection rates. Summary Table Concept Description Example Data Collection Gathering health data Hospital records, surveys Data Organization Classifying data Grouping patients by diagnosis Descriptive Statistics Summarizing data Mean, median, charts Inferential Statistics Drawing conclusions Hypothesis testing Interpretation Understanding meaning Relating statistics to health outcomes Data Presentation Displaying results Graphs and tables Decision-Making Using evidence for action Health policy formulation b) Explain importance of biostatistics Importance of Biostatistics 1. Supports Evidence-Based Decision Making Biostatistics provides the numerical foundation for making informed decisions in medicine and public health. Through statistical analysis, health professionals can identify what interventions work best, evaluate treatment outcomes, and make policies based on scientific evidence rather than assumptions. Example: Determining the effectiveness of a new vaccine by comparing infection rates between vaccinated and unvaccinated groups. 2. Helps in Health Research and Innovation Biostatistics is essential in designing, conducting, and analyzing medical and public health research. It guides sample selection, data collection methods, and the correct use of statistical tests to ensure research findings are valid and reliable. Example: In clinical trials, biostatistics helps determine whether a new drug is significantly better than existing treatments. 3. Enables Disease Surveillance and Control Biostatistics helps in monitoring patterns and trends of diseases in populations. By analyzing data over time, health authorities can detect outbreaks, measure disease burden, and evaluate control measures. Example: Using statistical trends to track and control the spread of malaria or COVID-19. 4. Assists in Planning and Policy Formulation Health planners use biostatistics to forecast future health needs, allocate resources efficiently, and set realistic health targets. It ensures that public health policies are guided by quantitative evidence. Example: Using birth and death rate data to plan maternal and child health services. 5. Facilitates Evaluation of Health Programs Biostatistics provides tools for monitoring and evaluating the impact and cost-effectiveness of health programs. This helps in improving performance and ensuring accountability. Example: Assessing whether a nutrition program reduces malnutrition rates in children under five. 6. Improves Quality of Health Data Statistical principles help ensure accuracy, reliability, and validity of collected health information. This reduces errors and enhances confidence in conclusions drawn from the

OPTOMETRY · SEMESTER 2 Basic statistical method in compiling health data Epidemiology and Biostatistics START READING NOTES Contents of This Topic Learning Objectives Methods of statistical Definition of a Variable. Types of Variables. Quantitative (Numerical) Variables Continuous variables take any value within meaningful extremes, can take decimals, for example: Levels of Measurement Example 1: Gender (male or female) is a common nominal variable used in epidemiologic studies. Ordinal Measurement Interval Measurement Basic statistical method in compiling health data Differences Between Nominal, Ordinal, Interval and Ratio Measurements Frequency and Relative Frequency Distribution – 1 Frequency and Relative Frequency Distribution – 2 Frequency and Relative Frequency Distribution – 4 Note: A frequency distribution can be displayed as a table, a bar chart, a histogram, or a frequency polygon. Use of Tallies in Making Frequency Distribution – 1 Use of Tallies in Making Frequency Distribution – 2 Use of Tallies in Making Frequency Distribution – 3 Sputum Examination for Possible TB Infection Sample Raw Data From the above data we can summarize as follows Can We Summarize it Further? A better summarization! Use of Diagrams Pie Charts Example: 2017 TTCIH Students’ Enrollment Data Bar Chart – 1 Bar Chart – 2 Example: Why not use a pie chart in the previous table? Two-Way Tables – 1 Two-Way Tables – 2 Example Pulmonary Tuberculosis by HIV Status Two-Way Table – 3 Take Home! Basic statistical method in compiling health data. Learning Objectives By the end of this session, students are expected to be able to: Explain methods of statistical data categorization Identify levels of statistical data measurement Compile health data using appropriate statistical methods Methods of statistical data categorization Data: The raw material of statistics. Data generally consists of numbers of measurement or counting of a population sample. The methods of summarizing data (methods of descriptive statistics) vary with different types of data that are generated from different types of variables. Definition of a Variable. Variable: A term for a characteristic that is different in different members of a population or sample, such as height. This measurement is not constant, so therefore it is variable. Variables can be qualitative or quantitative, continuous or discrete. Types of Variables. There two types of variables: Qualitative (categorical) variables Quantitative (numerical) variables Qualitative (Categorical) Variables Qualitative variables do not take numerical values (e.g. gender: male/female). Outcome of disease (recovery, chronic illness, death) Hair color (black, blonde) Marital status (single, married, widowed, separate, divorced) Quantitative (Numerical) Variables Quantitative variables take numerical values, for example: Age (years): 10, 19, 45, 60 Height (cms):140, 50.6, 200 Parity: 0, 1, 2, 3, 4, 5, 6, 10. Quantitative variables are of two types: continuous and discrete Continuous variables take any value within meaningful extremes, can take decimals, for example: Height (cm): 159, 25, 160.35 Weight (kg): 71.12, 80.56 Exact age like 21 yrs 6 months and 4 days Discrete variables take only fixed values, in most cases whole numbers, for example: Parity: 0, 1, 2, 3, 4, 5, 6, 10 Levels of Measurement Variables are measured on different levels/scales The term ‘measurement’ is used here in a broad sense These are nominal, ordinal, and ratio measurements Nominal Measurement The nominal scale classifies persons or things based on a qualitative assessment of the characteristic being assessed. It neither includes information on quantity or amount nor does it indicate ‘more than’ or ‘less than’ Example 1: Gender (male or female) is a common nominal variable used in epidemiologic studies. Other examples: These used for identifying various categories that make up a given variable e.g. Religion: 1 = Muslim, 2 = Christian, 3 = Ordinal Measurement The ordinal scale also classifies persons or things based on the characteristic being assessed but does indicate ‘more than’ or ‘less than’. In this sense, it provides more information than the nominal scale. Example: Rating students’ performance as being poor, average, good, or excellent indicates how well students perform and provides a basis for comparison. However, it does not indicate how much better an excellent performance is compared to a good one. Interval Measurement The interval scale has the same characteristics of the ordinal scale – classifying persons or things based on the characteristic assessed and indicating more than or less than – but the interval scale indicates how much more than or less than. The interval scale does not indicate a true zero point, meaning that there cannot be an absence of a characteristic being measured. Basic statistical method in compiling health data Example: Temperature is an interval in that different values can tell you how much more or less. However, there is no true zero point. The value of zero in temperature does not indicate absence of temperature. Ratio Measurement The ratio scale includes all the characteristics of the interval scale but does indicate a true zero point. Example: Height and weight measurements indicate how much more or less, but also have a true zero point. A weight of zero indicates an absence of weight. Differences Between Nominal, Ordinal, Interval and Ratio Measurements Nominal Ordinal Interval Ratio Classifies persons or things based on a qualitative assessment Similar or dissimilar but not more or less Can be numeric but there is no implication of more or less Classifies persons or things based on a qualitative assessment More or less but not how much more or less Indicates how much more or less Does not contain a true zero point Cannot create meaningful ratios of these two numbers Includes all the characteristics of the interval scale, but contains a true zero point. Descriptive Methods for Qualitative Data Frequency and Relative Frequency Distribution – 1 Frequency distribution A presentation of the number of times (or the frequency) that each value (or group of values) occurs in the study population. Frequency distribution helps to give a picture of the shape of the distribution of the data. Frequency and Relative Frequency Distribution – 2 Unimodal data: Data that only has one peak. Bimodal data: Data that has two peaks. Multimodal data: Data

OPTOMETRY · SEMESTER 2 Apply Biostatistical Methods In Analyzing Health Data Epidemiology and Biostatistics START READING NOTES Contents of This Topic APPLY BIOSTATISTICAL METHODS IN ANALYZING HEALTH DATA Learning tasks Definitions Data to be analyzed Methods for data analysis Data entry Data cleaning Data summarization Tools and applications for data analysis References APPLY BIOSTATISTICAL METHODS IN ANALYZING HEALTH DATA 1 Learning tasks At the end of this session, students are expected to be able to: Define data analysis Identify data to be analyzed Identify methods for data analysis Describe data entry process Explain data cleaning Explain data summarization List tools for data analysis Analyze health data 2 Definitions Data Analysis Is the systematic process of inspecting, cleaning, transforming, and modeling raw data to discover useful information, identify patterns, and support decision-making. 3 Data to be analyzed Qualitative Data: Non-numerical data from sources like interviews, open-ended survey questions, and observations Quantitative Data: Numerical data that represents quantities or measurements, such as sales figures, age, or height. 4 Methods for data analysis Data analysis has four main methods or techniques. Descriptive; Summarizes historical data to show what has happened by looking for causes and correlations. Predictive; uses historical data and models to forecast what is likely to happen in future. Diagnostic; Examines data to understand why something happened by looking for causes and correlations. Prescriptive; Recommends specific actions to be taken based on the predictions. 5 Data entry This is the manual or automated process of inputting or transferring data from physical or digital sources into an electronic format for an organization. 6 Data cleaning Is the process of finding and correcting errors, inconsistencies, and inaccuracies in a dataset to improve its quality. The goal is to ensure the data is reliable, complete, and consistent before analysis. 7 Data summarization Is the process of reducing large datasets into a more manageable and understandable form. This helps to highlight key aspects of data, such as patterns and trends without losing critical information. Techniques for summarization include calculating measures of central tendency(mean, median) and creating visual summaries like charts and graphs. 8 Tools and applications for data analysis Microsoft excel Stata R SPSS Python 9 References Kothar, C.R. (2004). Research Methodology: Methods and Techniques (2nd ed.). India: New Age International (P) Limited 10 ← PREVIOUS TOPICNEXT TOPIC →VIEW MODULE NOTESVIEW SEMESTER NOTESALL OPTOMETRY NOTES Need These Notes as PDF? Request a formatted copy for offline study, printing or revision. GET PDF NOTES ON WHATSAPP