SAMPLING METHODS Simple Random – Everyone in the population has an equal chance of selection. Example: Picking older cardiac patients in Toronto via randomization algorithm after contacting local GPs. Voluntary & Convenience – Participants are the most accessible or self-selected. Example: Recruiting the first 40 patients entering a hospital’s emergency department. Stratified Random – Population divided into subgroups, then sampled randomly within each. Example: Studying newcomers’ housing experiences by sampling within nationality groups. Cluster Random – Random sampling within naturally occurring groups. Example: Surveying patient satisfaction across different hospital departments. DATA CASE=ROW // VARIABLE NAMES=HEADERS // VARIABLE=COLUMN // RELATIONAL DB= links db’s cases through id# (like merging by ping_id in powerquery) Primary Data = Data we collect ex: from survey Secondary Data = Data collected by another party ex: stats canada VARIABLES Qualitative Var / Categorical Var = answers how ?s Quantitative Var = Measures numerical values with units Some variables can be both categorical and quantitative How data are classified depends on Why we are collecting the data Identifier Variable = unique identified linked to individual or item in group (ID# or PRODUCT#) Nominal Variable = Qualitative vvars used only to name categories Ordinal Variable = When data values can be ordered EX:employees can be ranked according to the number of days worked in the company. Time Series = Variables that are measured at regular intervals over time EX:the share price of the Royal Bank of Canada at the end of each day for the past year. Cross-Sectional Data – Multiple variables measured at the same point in time. Ex:Tracking sales revenue, customer count, and expenses for each Starbucks location in the past month. UNITS Some quantitative units indicate how value was measured, the corresponding scale of measurement, how much of something we have, how far apart two values are Other quantitative variables have no units, such as: Number of visits to a web site, or the number of shares of a company traded in Toronto Stock Exchange Variables can be qualitative and quantitative, depending on the purpose of data collection. For example: - Age as a quantitative variable – Measured in years to calculate the average age of customers. - Age as a categorical variable – Grouped (Child, Teen, Adult, Senior) to tailor music offers (folk, jazz, hip-hop, reggae). Counts = 1. Summarize qualitative var 2. To measure the amount of things Quantitative variables differ based on whether zero has a defined value: Interval Scale = No true zero (e.g., 80°F isn’t twice as hot as 40°F). Ratio Scale = Has a true zero, allowing meaningful comparisons. Statistics Process = 0. Determine analysis objectives/goals 1.Data Collection 2.Data Organizing 3.Data Analysis 4.Results Interpretation 5.Results Presentation Data Collection Process = 1.Review the analysis 2.Data Identification(What question type) 3.Data Sources (primary or secondary) 4.Tools 5.Budget+Time Population = whole group Sample = selected group to study Subgroup = subsection of a group Sample Unit = Sample units are the members of the population from which measurements are taken during sampling Creating Data Structure amd Data Input Template = 1.Developing surveys/forms 2.Determining Sample Size 3.Collecting a pilot sample 4. Finalize surveys / forms / tools 5.Distributing surveys / forms / tools 6. Receiving Data, Input and start organizing Survey Development = 1.Intro (purpose, who, confidentiality, assurance blah blah blah), 2. Question Section (multiple choice, open-ended questions, closed ended questions.) 3.Ending section(thank you / incentives?) Sampling Errors = Sample does not represent population (wrong respondent) 1.Non sampling errors = right respondent - wrong question / time / organization
