OIM In-Class Notes.pdf - Sept 5 I DCOVA helps answer the...

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Sept 5 I. DCOVA - helps answer the experiment 1)Define Data 2)Collect Data - Data should always be available, online or physically 3)Organize Data - "Clean Data" to make usable for hypothesis and analysis 4)Visualize Data - Make some graphs to understand the data easily, special field: data visualization, descriptive data analysis* 5)Analyze Data - Inferential data analysis, able to test your hypothesis and answer your problem* * Visualization and Analyzation can be mixed up sometimes II. Defining Data: a.Defining data = defining variables i.Variable: any characteristic that can be measured or counted b.Types/Measurement Scale of Variables i.Categorical - set by topic (first two are categorical variable scales) 1.Nominal - defined characteristics 2.Ordinal - ordered characteristics, has meaning a.ex. Freshman, Sophomore, Junior, Senior 3.ex. Location of Travel and Sports Team 4.Ex. How you feel on 1-5 scale, major ii.Numerical - set by numbers (first two are types of numerical variables) 1.Dichotomic/Discrete - counted items 2.Continuous - measured characteristics a.Ex. How much time spent on course III. Collecting Data a.Population vs. Sample i.Population: all the items (observations) that you are interested in (𝝁) ii.Sample: A subset of the population (that is available to you) - main objective to study (n) 1.Measures estimates/statistics 2.M - population mean 3.- sample meanXiii.Parameters (population) and statistics/estimate (sample) b.Primary Data vs. Secondary Data i.Primary Data - You collect data by yourself ii.Secondary Data - Some already collected for you c.Sampling Method i.Way we collect samples from the population - sampling ii.(Data) Frame: a complete or partial listings of the items in population, part of the population chosen iii.Sampling: a process of choosing items (observations) from population iv.Non-Probability Sampling 1.Judgement Sample 1.Pre-selected expert opinions in the subject 2.Political issues
2.Convenience Sample 1.Items are selected based only on the fact that they are easy, inexpensive, or convenient 2.More widely used v.Probability Sampling 1.Simple Random Sample 1.Choose anyone from the population to generate random numbers and choose specific samples - ex. include or exclude based on choice for random sampling 2.Random - everyone has the same prob to be chosen 2.Systematic Sample 1.Choose by a certain set of rules - ex. Choose one of every 5 people 3.Stratified Sample 1.Define strata (group of items that are similar) and choose one person from each strata - ex. One from male, female, infant groups 2.More systematic than the systematic sample 4.Cluster Sample 1.Create clusters/groups (don’t need to be similar) and choose that certain cluster - ex. Choosing cluster 3 of 1-4I. DCOVA - helps answer the experiment 1)Define Data 2)Collect Data - Data should always be available, online or physically 3)Organize Data - "Clean Data" to make usable for hypothesis and analysis 4)Visualize Data - Make some graphs to understand the data easily, special field: data visualization, descriptive data analysis* 5)

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