BIOSTATICS, Lecture notes of Biostatistics

Download BIOSTATICS and more Lecture notes Biostatistics in PDF only on Docsity! ACKNOWLEDGMENTS TEAM MEMBERS YAZEED ALKHAYAL SHAHAD ALDUMKH REVIEWER ASEEL BADUKHON KSU COLLEGE OF MEDICINE 2019 - 2020 17 BIOSTATICS Special thanks to SARAH ALENEZY & 436 TEAM PLAGIARISM PAGE 02 TABLE OF CONTENTS Original, refrased. Our notes. Doctors notes. Golden notes. LECTURE OBJECTIVES By the end of this lecture, I am able to: Q U IZ Definition of statistics and biostatistics To understand different Levels of measurements To understand different Types of data To use these concepts appropriately BIOSTATISTICS THIS LECTURE IS EXTREMELYIMPORTANT!!!! BIOSTATICS PAGE 05 ● When collecting or gathering data we collect data from individuals cases on particular variables. ● A variable is a unit of data collection whose value can vary. (from someone to another) ● Variables can be defined into types according to the level of mathematical scaling that can be carried out on the data. ● There are four types of data or levels of measurements: ○ 1. Nominal 2. Ordinal 3. Interval 4. Ratio Types of variables & data Scales of Measurement Nominal scale variables Ordinal scale variables Interval scale variables Ratio scale variables A type of categorical data in which objects fall into unordered categories Studies measuring nominal data must ensure that each category is mutually exclusive and the system of measurement needs to be exhaustive Variables that have only two responses i.e. Yes or No, are known as Dichotomies. Ordinal data is data that comprises of categories that can be rank ordered. Similarly with nominal data, the distance between each category cannot be calculated but the categories can be ranked above or below each other. Fahrenheit temperature scale -zero is arbitrary- 40 degrees is not twice as hot as 20 degrees. IQ tests. No such thing as zero IQ. 120 IQ is not twice as intelligent as 60 IQ. Can we assume that attitudinal data represents real, quantifiable measured categories? (i,e, Very happy is twice as happy as plain “Happy”, or “Very Unhappy” means no happiness at all). “Statisticians are not in agreement with this. The distance between any two adjacent units of measurement (intervals) is the same and there is a meaningful zero point. Income; someone earning SAR20,000 earns twice as much as someone who earns SAR10,000. Height Age Weight. (names) Its mean I can’t mix between the data. For example: females and males data, smoker and non smoker data. Mathematically not strong, zero value isn’t fixed (we can’t compare) Mathematically strong, zero value is fixed (we can compare) Remember the highlighted words! Examples of ordinal: grades, cancer stages. BIOSTATICS PAGE 06 ● These levels of measurement can be placed in a hierarchical order: Ratio > Interval > Ordinal > Nominal. ● Nominal data is the least complex and give a simple measure of whether objects are the same or different. ● Ordinal data maintains the principles of nominal data but adds a measure of order to what is being observed. ● Interval data builds on ordinal by adding more information on the range between each observation by allowing us to measure the distance between objects. ● Ratio data adds to interval with including an absolute zero. Hierarchical Data Order Categorical Data ● The objects being studied are grouped into categories based on some qualitative trait. ● The resulting data are merely labels or categories. ● Nominal and Ordinal scales will be used for categorical data or qualitative data. Categorical Data Nominal Data Ordinal Data Examples of Nominal Data: ● Type of car: ○ Mercedes, BMW, Lexus, Toyota, etc. ● Ethnicity: ○ White British, Afro-Caribbean, Asian, Arab, Chinese, other, etc. ● Smoking status: ○ Smoker, non-smoker. Examples of Ordinal Data: ● Grades in an exam: A+, A, B+, B, C+, C, D+, D, and fail. ● Degree of illness; none, mild, moderate, acute, chronic. ● Opinion of students about stat classes; Very unhappy, unhappy, neutral, happy, ecstatic! Examples of Binary Data: A type of categorical data in which there are only two categories. (yes or no, A or B and nothing between) E.g. Smoking status; smoker, non-smoker. Attendance; present, absent. Result of exam; pass, fail. Status of student; undergraduate, postgraduate. (Qualitative data) Remember this! BIOSTATICS PAGE 07 Examples of categorical (nominal & ordinal) data: Eye color: (Nominal) Blue, brown, black, green, etc. Smoking status: (Nominal) Smoker, non-smoker Attitudes towards the death penalty: (Ordinal) Strongly disagree, disagree, neutral, agree, strongly agree. Nominal data (Binary) & Ordinal data: What is your gender? ☐ Male ☐ Female Did you enjoy the teaching session? ☐ Yes ☐ No What is the level of satisfaction with the new curriculum at a medical school received? ☐ Very satisfied ☐ Somewhat satisfied ☐ Neutral ☐ Somewhat dissatisfied ☐ Very dissatisfied Quantitative Data ● The objects being studied are ‘measured’ based on some quantitative trait. ● The resulting data are a set of numbers. ● Interval & Ratio scales will be used to measure quantitative data. Examples: Pulse rate Exam marks Height Time to complete a biostatistics exam Age Number of cigarettes smoked Quantitative Data Discrete Continuous Discrete Data: (Whole numbers) Only certain values are possible (there are gaps between the possible values). Implies counting. Continuous Data: ( Decimal points) Theoretically, with a fine enough measuring device. Implies measuring. What is difference between Discrete and Continuous Quantitative data? Discrete: can take on only integer (target) values (counted data). For example: the number of students in a hall (you can't have half a student). Continuous: can take on any value (measured data) For example: heights, weight..etc (you can have half data)