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VALIDITY AND RELIABILITY IN EXPERIMENTS 155<br />

interviewer. Powerful interviewees are usually busy<br />

people and will expect the interviewer to have read<br />

the material that is in the public domain.<br />

The issues of reliability do not reside solely<br />

in the preparations for and conduct of the<br />

interview; they extend to the ways in which<br />

interviews are analysed. For example, Lee (1993)<br />

and Kvale (1996: 163) comment on the issue<br />

of ‘transcriber selectivity’. Here transcripts of<br />

interviews, however detailed and full they<br />

might be, remain selective, since they are<br />

interpretations of social situations. They become<br />

decontextualized, abstracted, even if they record<br />

silences, intonation, non-verbal behaviour etc.<br />

The issue, then, is how useful they are to<br />

researchers overall rather than whether they are<br />

completely reliable.<br />

One of the problems that has to be considered<br />

when open-ended questions are used in the<br />

interview is that of developing a satisfactory<br />

method of recording replies. One way is to<br />

summarize responses in the course of the interview.<br />

This has the disadvantage of breaking the<br />

continuity of the interview and may result in<br />

bias because the interviewer may unconsciously<br />

emphasize responses that agree with his or her<br />

expectations and fail to note those that do not. It<br />

is sometimes possible to summarize an individual’s<br />

responses at the end of the interview. Although<br />

this preserves the continuity of the interview, it<br />

is likely to induce greater bias because the delay<br />

may lead to the interviewer forgetting some of<br />

the details. It is these forgotten details that are<br />

most likely to be the ones that disagree with the<br />

interviewer’s own expectations.<br />

Validity and reliability in experiments<br />

As we have seen, the fundamental purpose of<br />

experimental design is to impose control over<br />

conditions that would otherwise cloud the true<br />

effects of the independent variables upon the<br />

dependent variables.<br />

Clouding conditions that threaten to jeopardize<br />

the validity of experiments have been identified<br />

by Campbell and Stanley (1963), Bracht and<br />

Glass (1968) and Lewis-Beck (1993), conditions<br />

that are of greater consequence to the validity<br />

of quasi-experiments (more typical in educational<br />

research) than to true experiments in which random<br />

assignment to treatments occurs and where<br />

both treatment and measurement can be more<br />

adequately controlled by the researcher. The following<br />

summaries adapted from Campbell and<br />

Stanley (1963), Bracht and Glass (1968) and<br />

Lewis-Beck (1993) distinguish between ‘internal<br />

validity’ and ‘external validity’. Internal validity<br />

is concerned with the question, ‘Do the<br />

experimental treatments, in fact, make a difference<br />

in the specific experiments under scrutiny’.<br />

External validity, on the other hand, asks the<br />

question, ‘Given these demonstrable effects, to<br />

what populations or settings can they be generalized’<br />

(see http://www.routledge.com/textbo<strong>ok</strong>s/<br />

9780415368780 – Chapter 6, file 6.8. ppt).<br />

Threats to internal validity<br />

<br />

<br />

<br />

History: Frequently in educational research,<br />

events other than the experimental treatments<br />

occur during the time between pretest and<br />

post-test observations. Such events produce<br />

effects that can mistakenly be attributed to<br />

differences in treatment.<br />

Maturation: Between any two observations<br />

subjects change in a variety of ways. Such<br />

changes can produce differences that are<br />

independent of the experimental treatments.<br />

The problem of maturation is more acute in<br />

protracted educational studies than in brief<br />

laboratory experiments.<br />

Statistical regression: Like maturation effects,<br />

regression effects increase systematically with<br />

the time interval between pretests and<br />

post-tests. Statistical regression occurs in<br />

educational (and other) research due to the<br />

unreliability of measuring instruments and to<br />

extraneous factors unique to each experimental<br />

group. Regression means, simply, that subjects<br />

scoring highest on a pretest are likely to score<br />

relatively lower on a post-test; conversely,<br />

those scoring lowest on a pretest are likely<br />

to score relatively higher on a post-test. In<br />

short, in pretest-post-test situations, there is<br />

Chapter 6

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