590 Writing Workshop Part I: Interpretive Statements

590 Writing Workshop

Part I:

Interpretive Statements

© 2001 by V. Dennen; revised 2003, 2004 by M. J. Bober

Why do I need interpretive statements? Conclusions are interpretive

statements--drawn from results/findings You are broadly interpreting what the

data mean for the reader Your words need to be chosen carefully;

conclusive statements are likely to be quoted


About interpretive statements

address important or pervasive findings--somewhat like summaries … thus not trivial matters

the strongest possible statements about what was learned that can be supported by the data collected and presented

include qualifications that limit the scope of interpretation to the data collected and presented


Interpretive statements are NOT

Observations Assumptions Speculations Common knowledge


Writing interpretive statements

Avoid statements that suggest applicability in unstudied contexts.

Be careful to qualify as much as necessary (e.g., note the context or conditions under which the assertion holds, the unavailability of some data sources, limits of time and other resources).

Write down your assertions and, under each, list your supporting data--and then judge theits sufficiency.

Seek peer critique.


Cautions about interpretations

You don’t want to under-interpret– A statement of fact is an under-interpretation

You don’t want to over-interpret– A statement that is not well supported by the data

is over-interpreted You don’t want to over-generalize

– Depending on your sample size, you may or may not be able to apply your findings to a larger audience


Example: Under-interpretation

Seven out of ten people reported having problems with the installation procedure.

Problems with this statement:– One survey could generate several such

statements– It is a reporting of facts, not an explanation of what

they mean– It does not integrate different data sources


Example: Over-interpretation

Data: 7/10 had installation problems; 5 people are no longer using the product

People do not use the program because the installation procedure is too difficult

Problems with this statement:– The data do not support this statement– The data that are available are distorted


Example: Over-generalization

Scenario: An evaluation of how a particular district-wide reading program is working. Two classes were studied.

While students in the XYZ Reading Program have improved their reading skills, they lack the motivation to perform well on the standardized tests that serve as an indicator of reading gains.

Problems with this statement:

– Is not site-specific

– Suggests that two classes are indicators for an entire district


Let’s discuss

1. Although all four instructors were given positive ratings for professional presentation of the course material, there were very significant differences in the actual ratings between the instructors. Some received much higher ratings than others.

2. The video clips were rated as an effective portion of the design of the course. However, the ratings received for video clips differed between instructors.


Part II:

Triangulation and Report Structure


Triangulation

Triangulation involves supporting your story with multiple sources of evidence

Triangulation can be by data source, data collection method, theory

Sources need not literally match up– You do not have to ask the same question

of all participants to triangulate the data


Triangulation example(you try it!) Data:

– Survey– Observation– Interview


Triangulation example

Together the data support the assertion that …

Now--what’s your recommendation?


If the shoe doesn’t fit, still wear it

Some data disconfirm other data, but that does not make them irrelevant or important

Data that are not in agreement with overall findings should not be considered outliers and should not be ignored– They should be reported in the full context in which they

were collected– This is all part of demonstrating the certainty of your results– Your results are not worse or less important because the

data are not all in agreement


Structuring your results

By data collection method– Can be a bit disjointed in terms of

triangulation by method By evaluation question or objective

– Useful if your questions explore diverse aspects of the evaluand

By emergent theme or issue


Sample structure of results

Evaluand: Implementation of a computer-based workforce education program

Data: Extant learner performance data; Site coordinator interview; Learner survey

Concept: See if the program works in terms of learner gains

Report Structure:– Learner gains– Survey results (with int. data for context)– Discussion of gains in light of survey responses


Part III:

The Good, The Bad & The Ugly


Good news or bad news?

Remember that evaluation is a study of how someone or something is performing– It is not just a report of problems

Your report likely will cover some positive and some less-than-positive conclusions

Do not forget the positive parts!


Balancing statements

Sometimes an instrument yields negative or inconclusive results, while the evaluator has a gut sense of other knowledge of positive occurrences

It is appropriate to temper the negative results with this other, positive knowledge

Similarly, positive results may be tempered with cautionary statements


Giving bad news

Don’t just say something was negative, but explain why

Consider the political ramifications of how you are stating this finding– If a client is looking to lay blame in a way

you think is not fair, be sure to indicate that the blame does not lie there


Avoid generalities

It is rare that something is “always” or “never” the case

It is unlikely that you can speak for all members of a population


Keep suggestions manageable

You may have many suggestions to make, but getting a list of 25 fixes could be overwhelming to a client

Try to group or synthesize your recommendations– 25 small suggestions might become 5

“issue areas” covering the same exact content, but appearing less daunting


Part IV:

Tables, Charts & Graphs


When to use tables, charts & graphs When you find yourself listing data When you want readers to see the magnitude

of a difference– Example: if one group always outperformed

another, a bar chart can show it at a glance A picture is worth a thousand words

– Sometimes the table or graph says it much better than you ever could


When to not use them

To show results of EVERY survey item When the data are very simple and easily

understood in written form– Example: 2/3 of the people said “yes” and 1/3 said

“no” A picture is worth a thousand words: the flip

side– Too many visuals distract your reader from the

main point


When to use what

Bar chart: Shows the level of results, and the difference in magnitude between items

Pie chart: Shows the % of each item, adding up to 100%; not appropriate when a person fits more than one category


Labels and in-text references

All visuals should be labeled– Tables become Table 1: Mean Instructor Ratings;

Table 2: Session Reactions (Frequencies); etc.– Charts/graphs become Figure 1:_____; Figure 2:

_____; etc.. All visuals should have a title

– Descriptive, but not too wordy In text, you might say “Table 3 summarizes

…” or at the end of a related sentence write “(see Table 3).”


Title examples

Too brief: Table 2. Mean Ratings Too wordy: Table 2: Mean Ratings by

Students about Instructor Competence within the Writing Classroom

Better: Table 2: Mean Ratings of Instructor Competence


Example

What do you think of the example I’ve provided?


Part V: Editing


The editing process

Use the grammar check function in MS Word

Read your paper looking for– Spelling/grammar problems– Incomplete or run-on sentences– Illogical sentences– Unnecessary jargon– Wordy phrases


The 10% game

Read your paper and try to eliminate 10% of the words without changing the meaning

“The rate of turnover for instructors at XYZ Corporation...” becomes “XYZ Corporation’s instructor turnover rate...”


Common writing problems

Between (2)/Among (more than 2) While (during)/Whereas (contrast) That (things)/Who (people) Use (employ)/Utilize (make skilled use

of)/Usage (the act of use)


And more…

Personification (e.g. The evaluation asked students to complete a survey)

Dangling Modifiers (e.g. After writing all that material, the computer didn't save it)

Adverb abuse (firstly, secondly, thirdly)

Documents

590 Writing Workshop Part I: Interpretive Statements