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