Data, Tables and Graphs Presentation. Types of data Qualitative and quantitative Qualitative is...

Data, Tables and Graphs

Presentation

Types of data

Qualitative and quantitative Qualitative is descriptive (nominal,

categories), labels or words Quantitative involves numbers Data: information to be analyzed

Types of data

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

values

Coding schemes

Coding schemes are numbers assigned to characteristics of the data to be analyzed

Best to use numeric coding schemes

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

Example: continued

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

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

Data file: example

ID Age Sex Race IQ Hand MS

1 22 1 3 102 1 1

2 34 2 1 110 1 2

3 60 2 1 112 1 3

4 54 1 3 92 1 2

5 39 1 1 120 2 1

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

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

Missing value

Data is often incomplete—there 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

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

Qualitative (nominal)

Frequency distributions Tables and graphs

Always label tables and graphs

Table 1. Gender of Sample

Frequency Proportion Percent

Male 52 .52 52%

Female 48 .48 48%

Pictorial representations

Pie charts Bar charts

Displaying two variables in a table

Crosstabs Race and gender, as an example

Quantitative data

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

occurs

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)

Quantitative: tables

Proportion, cumulative proportion Percent, cumulative percent

Graphs, quantitative, ungrouped

Histogram Bar graphs Line graphs: frequency Cumulative

Quantitative, grouped data

Sometimes cumbersome to list each value—too many values

Example: age—could 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

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

Interval width

No hard and fast rules—what 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

Continuous data

If data is continuous, actually decimal values are possible

Must develop a rule for handling this For example, use a rounding rule