Accepting (or Rejecting) Hypotheses: Objective Criteria

At this stage, the experiment has yet to be conducted and the experimenter has yet to decide which of the two hypotheses he should favor. But the “favoring” is not a matter of fancy; it can­not be decided arbitrarily. The consequences of accepting one or the other of the possible hypotheses should be logically

Procedures for Planning the Experiments

With minor variations, the following are the steps for designing simple comparative experiments: Identify whether O is known for the responses rep­resented in μ0. If it is not known, more calcula­tions will be required, as we will see later in the procedure. State the null and the alternate hypotheses to represent the experimental conditions.

Other Situation Sets of Experimental Research

Thus far we have dealt in detail with only, perhaps, the two most used situation sets. We have seen the difference of requiring the additional steps in the procedure for situations in which a is not known. This difference applies to all other situation sets as well. We will now visit a few more

Operating Characteristic Curve

A given set of experimental situations can be represented in a graphical form, referred to as the operating characteristic curve for that set. To show how it is derived, we will revisit the design for experimental Situation Set 1. H0: μ1 = μ0 (average pressure, 250 psi) (δ = 10; enhanced pressure, 260 psi)

Sequential Experimenting

As the reader should have noticed, the two key steps in designing the experiment in all the variations so far presented are (1) find­ing the number of items in the sample N, and (2) computing the criterion value, X, to compare it with the mean of the response output, X1. If the sample items

The Way to Inference from Experimental Data

During the life span of an experiment, following the stages of conducting the experiment and collecting data, there comes a time when the experimenter, with his logbooks loaded with mea­surements, meter readings, and various counts of other kinds, will have to sit and ponder how reliable each bunch of observa­tions (measurements, readings, or counts)

Estimation (From Sample Mean to Population Mean) of Experimental Data

As pointed out in Chapter 18, the mean is the most significant statistic of a given set. The mean of the sample set A—56.4, 57.2, 55.9, 56.2, 55.6, 57.1, and 56.6—is 56.43. This is called the sample mean, X, and when it is used as an estimator of the popula­tion mean, fl, it is

Testing of Hypothesis in Experimental Research

Testing of statistical hypothesis has several applications, almost wherever statistics is applicable. In this book, however, we shall confine the discussion to experimental research, considering only one obvious and hypothetical case of application; this will show the way to other possible applications with minor modifications as required. Let us imagine that an experiment has

Regression and Correlation of Experimental Data

When two sets of variables are associated with any kind of rela­tion, their relation can be represented on a graph as a set of points, each point determined by a pair of corresponding coordi­nates, one from each set. When there is a cause-effect relation, the values of the independent variable are shown on the

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Multiple Regression of Experimental Data

Thus far, we have dealt with simple linear regression, which is adequate for most lab experiments required in college course work. For research work, one-factor-at-a-time experiments are inadequate. It is now time for us to reflect on how close—or how far away—the reality of the experimental situation is to the results obtained by simple

Introduction to observation

Observation has one overpowering claim to validity: it deals not with what people say they do but what they actually do – to the extent that their behaviour is open to observation, and insofar as observation is as objective as it seems to be. Two questions follow from this. In the first place why

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Observation and self-report: similarities and Differences

Both self-report (as in questionnaires and interviews) and observation techniques can differ in their degree of struc­ture. The first of these may involve short, prescriptive question-and-answer formats that are easily analysed in quantitative terms. Using structured observation schedules which involve the recording and counting of pre-determined categories of behaviour is an almost exact analogy.

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The uses of observation as research method

The most general use of observation in research is exploratory, as it is in real life. When we take up a new job, enter university, move to a different part of the country (or to a different country), or even just go on holiday to a new region, we look around us. In that

Observation in multi-method research

In the same way that surveys can give the bigger picture within which more in-depth interviews are nested, so can observation provide a wider descriptive framework. In a school, for example, it would make only limited sense to interview teachers about their views on the official dis­ciplinary policy without some account, by direct observation,

Structured observation in practice

For the moment the terms structured, detached and non­participant are used almost interchangeably: that is not quite correct (see page 19) but sufficiently so to equate their meaning. All introductory classifications simplify reality. Structured observation (specifying exact behaviours and recording their frequency over short, usually intermittent, periods of time) is something that figures regularly

Event sampling in Structured observation

1. Event sampling Event sampling is used when the behaviour of interest is discontinuous and low frequency, where you would miss the events if you didn’t observe continuously. The behaviour has to be specified exactly (making a monetary donation, engaging in conversation) so that it can be recorded on an observation schedule in a

Time sampling in Structured Observation

This is also known as interval sampling because you observe for a specified duration at specified intervals – like taking a succession of snapshots, an analogy which is pretty close. Sampling means estimating the frequency of events in a continuous time sequence from a much shorter period (or periods) of time. The main practical

Validating observation sampling

In survey research a sample of the population (however defined) is taken because it is much more economical to do so. A ‘valid’ – i.e. representative – sample is achieved in various ways (not our concern here but see the book in the present series dealing with social surveys (Gillham, 2008)). Validating interval sampling

Semi-structured Observation Method

If observation is a primary technique of real-world social research one can be forgiven for thinking that structured observation, with its ‘count’ emphasis and fragmented character, is not the best way of doing it. Sometimes that level of specificity is necessary and useful and it is to be hoped that we have done justice

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Semi-structured Observation in training, teaching and learning

1. Observation in training In a sense we are all ‘disabled’ when it comes to operating an unfamiliar and relatively complicated piece of equipment: the controls in a new car, the sequence of operations in a new piece of software or a washing machine with a more elaborate choice of programmes – all of