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Data, Tables and Graphs Presentation

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Types of data Qualitative and quantitative Qualitative is descriptive (nominal, categories), labels or words Quantitative involves numbers Data: information to be analyzed

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Types of data Discrete and continuous Discrete: takes on only whole number values Continuous: can take on decimal (fractional) values

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Coding schemes Coding schemes are numbers assigned to characteristics of the data to be analyzed Best to use numeric coding schemes

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Example: age, race and gender, coding scheme Age: recorded as a two digit number Race: Coded as a single digit number using a coding scheme: 1. African American 2. Hispanic 3. White 4. Asian 5. Other

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Example: continued Gender 1. male 2. female Andy is a 22 year old white male Age: 22, Race: 3, Gender: 1 Coded as: 2231

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Data file Usually rectangular Variable values recorded for the unit of analysis We will use SPSS as an example: Statistical Package for the Social Sciences

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Data file: example IDAgeSexRaceIQHandMS 1221310211 2342111012 3602111213 454139212 5391112021

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Data file Each row is the unit of analysis (usually a subject) Each column is a variable Every variable should be given a label (name) If it is a nominal variable, each value should have a value label

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Example of value label Unit of analysis: subject Variable: marital status Values might include: single, married, divorced, widowed Each value should be coded as a number, and the label provided

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Missing value Data is often incompletethere will be missing information There should be a code to indicate if a piece of data (a variable) is missing for a particular subject (often 0 is used) Example: no IQ score available, coded as a 0, indicated in the data file

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Simple descriptive statistics Frequency: number of times a value occurs If there are 48 females and 52 males in a sample, f = 48 for females and 52 for males Proportion = f/N, P = 48/100 for females, or.48 Percent: % = f/N * 100

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Qualitative (nominal) Frequency distributions Tables and graphs Always label tables and graphs

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Table 1. Gender of Sample FrequencyProportionPercent Male52.5252% Female48.4848%

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Pictorial representations Pie charts Bar charts

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Displaying two variables in a table Crosstabs Race and gender, as an example

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Quantitative data Tables and graphs Ungrouped data Each value is displayed Count: each value Frequency: number of times each value occurs

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Quantitative Frequency: number of times each value occurs Cumulative frequency: arrange the numbers in ascending (or descending), and sum the frequencies going down the table Indicates how many scores are less than a given score (cf)

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Quantitative: tables Proportion, cumulative proportion Percent, cumulative percent

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Graphs, quantitative, ungrouped Histogram Bar graphs Line graphs: frequency Cumulative

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Quantitative, grouped data Sometimes cumbersome to list each value too many values Example: agecould be 0 to 90+ Set up group intervals, i.e., 0-5, 6-10, etc. Rules: 1. first and last interval should not have a 0 frequency

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Grouped data Mutually exclusive and exhaustive All intervals should be the same width Important rule, not in the book: when collecting data, do not group (collapse) information is lost. You can always group later

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Interval width No hard and fast ruleswhat seems to be most meaningful Appearance also a consideration As a start, use the formula, width = range of scores (highest-lowest), divided by the number of intervals

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Continuous data If data is continuous, actually decimal values are possible Must develop a rule for handling this For example, use a rounding rule

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Organizing Data Section 2.1.

Organizing Data Section 2.1.

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