This document discusses content analysis methods for analyzing documents and textual data. It describes several types of documents that can be analyzed, including organized records and unorganized personal records. The document outlines the steps to conducting a content analysis, including selecting materials, defining coding units, establishing categories to measure, and developing a coding system. It also discusses issues of validity and reliability in content analysis and different methodological approaches like human coding, dictionary-based analysis, and supervised machine learning.

1. GEOG 420 / POL 420 / SOC 420
Spring 2018

4.  Examples: Documents, reports, manuscripts,
memoirs, speeches, etc.
 Issue ofTime (History) and Space (Location)
 Fewer ethical issues than observation,
interviewing, etc.

5. ADVANTAGES
 Access difficult subjects
 Raw data is nonreactive
 Analysis over time
 Increased sample size
 Cost
DISADVANTAGES
 Selective Survival
 Incomplete Nature
 Biased Content
 Unavailability of Records
 Lack of Standard Format

7.  Records NOT part of organized or systematic
record-keeping program.
 Produced and preserved in a casual, personal,
and sometimes accidental manner
 Examples: Diaries, memoirs, manuscripts,
correspondence, autobiographies,
“media of temporary existence”
(e.g. brochures, posters, etc.)

9.  Produced by organizations
 Carefully stored, easily accessed, and
available for long periods of time
 Examples: OpenSecrets,THOMAS, Almanac
of American Politics, University Crime Log

10.  Advantages
 Cost –Time and Money
 Accessibility
 More Extensive
 Disadvantages
 Control of record-keeping organization
 Unwillingness of organization to share data
 Finding out record-keeping organization’s practices
 How do we actually analyze the data that we collect?

12.  Step #1: Materials to Include in Analysis
 Selection guided by theory and existing research
 Examples:
 Speeches, newspapers, blogs, magazines, etc.
 Materials collectively make up sampling frame

13.  Step 2: Define Recording or Coding Units
 Units that are distinguished for description,
recording, and coding purposes in analysis
 Examples:
 Word or sentence fragment
 Sentence
 Paragraph
 Story
 Item asWhole

14.  Step 3: Categories of Content to Measure
 Variables that you want to focus on in study
 Can be most important part of content analysis
 Example: Viewing nightly news programs and
coding stories based on specific issues
(economy, health care, crime, education, etc.)

15.  Step 4: Devise System to Measure Content
 Presence or absence of given content category

17. VALIDITY
 Precise explanations of
procedures and categories
RELIABILITY
 Demonstrated through
intercoder reliability
 Two or more analysis,
using same procedures and
definitions, agree on
content categories
applied to material

19.  Reading
 Human Coding / Manual Content Analysis
 Dictionary-Based Approaches
 Supervised Learning

21.  Analysis process involves reading a text
 Advantages:
 Fundamental for inferring meaning from text
 Fewer assumptions that other methods
 Disadvantage: Cost

23.  Standard methodology for content analysis
and text coding with social science research
 Coders read text and attempt to assign one
of a set of categories to each unit

24. ADVANTAGES
 Less substantive
knowledge than deep
reading of the texts
 Less costly than reading
DISADVANTAGES
 Higher initial costs
 Arriving at categorization
scheme requires knowledge
of subject matter and
substantial time

26.  Analyst develops list of words and phrases
likely to be in a particular category
 Examples: LIWC, DICTION, JFREQ
 Computer tallies words in given categories

27. ADVANTAGES
 Analysis Costs
 Large number of texts
can be processed quickly
 Descriptive numerical
summaries are easily
generated
DISADVANTAGES
 High startup costs
 Building appropriate
dictionary requires good
deal of knowledge
 Trial and error

29.  Hand coding done to subset of texts
 “Training” Set: Evaluation tool for “test” set
 Algorithms used to attempt to infer mapping
from text features to hand-coded categories in
training set

30. ADVANTAGES
 Large amount of texts
due to automated process
DISADVANTAGES
 High startup costs due to
human construction of
“training” and “test” sets
of documents