Introduction to Data Analysis Handbook - AED Center for Early Care ...

V. Data Analysis Using
Content Area Examples
Introduction to Data Analysis
Working with data in real life situations always involves some combination of good news
and bad news (of course, not necessarily in equal amounts).
in Head Start programs, data is everywhere. Every enrollment form, developmental
history and child screening is a potential source of data, as are lesson plans, teachers’
observations of children and even the comments made by bus drivers. Likewise, every
pre-service evaluation form is also a source of data, as is every statement made by a
parent, whether or not it is written down.
As we have mentioned previously, in Head Start programs, “data” come from many
sources and are present in large amounts. Therefore, the good news is that we have plenty
to work with! Second, all staff in MSHS already possess many skills in analyzing data.
Consider the following example:
Q: Which size raise would you prefer?
a) 0.5%
b) 1.5%
c) 5%
d) 15%
e) 50%?
Congratulations! Everyone got the correct answer! (Remember, the question asked what
raise you would prefer….not what you should expect).
Some of the “bad” news connected with data analysis includes: 1) getting used to the idea
that there are different types of data; 2) keeping track of the many “ways” of working
with data; and 3) becoming comfortable with the reality that there is lots to learn if you
are new to this. Actually, the related fields of data analysis are so broad and continuously
developing that even people with decades of experience can always learn more – if they
want to. (PS- there’s a lot more bad news but we have space limitations)
Developing data analysis skills means using the skills that you already have and practicing
the use of new skills… until they become familiar. Accordingly, we use this section to
guide participants through some of the basic procedures and methods available for
the analysis of data. Since there is a basic distinction between quantitative data (think,
numbers) and qualitative data (think, no numbers) there is also a difference in what “data
analysis” means in practice, depending upon the type(s) of data you are (or soon will be)
working with.
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