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7-9 - Data Analysis

7-9 - Data Analysis - Data Analysis An Introduction to...

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Data Analysis An Introduction to Descriptive Statistics Measurement Measurement is fundamental to the research process Survey questionnaires aim to measure particular concepts and phenomena They allow us to convert an ambiguous concept into a precise empirical measurement It’s the researcher’s task to convert the questions and answers in a survey into precise measurements Example: the question “How old are you?” gets converted for statistical purposes into a variable named ‘age’ – all survey responses to the age question get coded and stored under the age variable. The age variable and its data becomes an empirical measurement and is stored in a database for statistical purposes. Age Variable Age is categorized into different sections (0-3yrs; 4-5yrs, etc) The frequency is accounted (the number of people that fall into the category) The percentage of total sample is calculated Levels of Measurement The level of measurement will predict the particular type of test conducted Each variable within the NPHS database has different categories or levels of measurement: o Nominal level o Ordinal level o Interval-ratio level Nominal Level Nominal level categories simply name the different attributes in variable These categories are mutually exclusive or dichotomous There is no overlap; the categories are completely separate from each other Examples: gender, religion, and political party Nominal Variable If the level of measurement in a variable is nominal, then the variable is often referred to as a nominal variable Nominal-level variables are used for specific statistical tests that look at differences between the categories They are the lowest level of measurement Example: men vs women on the hours of weekly exercise Gender is the most often used nominal variable Ordinal Level Ordinal level measurements are nominal categories that are ranked Mutually exclusive, but also organized in some special order Example: ranked from high to low; from small to large; worst to best Ordinal Variable If the variable of measurement in a variable is ordinal, then the variable is often referred to as an ordinal variable Ordinal variables are used for specific statistical tests that look at differences between the quality of rank ordering Example: differences between the very religious, somewhat religious, and not very religious Interval-ratio Level Highest level of measurement because it’s a numerical value Interval-ratio level categories share all the same qualities associated with nominal and ordinal variables, but also allows us to measure the distance between the categories The distances between the categories are numerical, incremental, and precise – ranging from 0 – infinity Interval-ratio Variable If the level of measurement in a variable is interval-ratio, then the variable is often referred to as an interval ratio variable
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