Relación entre la comercialización digital y el desarrollo sostenible

INTRODUCTION

Many statistical techniques are available to analyse the results of organizational research. In Chapters 5 and 6 these techniques are introduced and instructions are provided on how to carry them out, and how to interpret and report the results they produce. However, before discussing these techniques, it is important to draw attention to a fundamentally important principle: both the design of a research study, and the statistical method used to analyse the data it produces, should be dictated, or at least highly influenced, by the research question or questions to which the researcher requires an answer. The good researcher should be clear about what he or she is trying to find out, should design research around that question (or questions), and should choose a particular statistical technique because it can help to provide an answer to that question (or questions).

This needs to be stressed because it is not uncommon for people to generate research data without a tightly defined research question, and then to say ‘Okay, what statistical test should I use to analyse my data?’. This approach to research and data analysis invariably leads to dis-appointment. Before statistical analysis is even considered and, indeed, before a research study is designed, it is very important that the researcher clarifies his or her research questions, and is absolutely clear and focused about what he or she is trying to find out. When the research question(s) is absolutely clear, and the research is carefully designed in such a way that it is able to provide numerical data relevant to that question, the choice of statistical method is usually quite clear and straightforward. But when the research question is unfocused or even absent, the choice of statistical technique will be almost arbitrary; and in these circumstances it is unlikely that anything of much use will be found however complex and sophisticated the statistical analysis.

Having made this point, let’s have a look at the nature of statistical methods used in organ-izational research. These methods generally take the form either of a specific statistical test of significance (such as a t-test), or of a more general technique in which tests of significance are usually involved (such as multiple regression). Although a large number of inferential statistical techniques have been developed over the years, those commonly used by organizational researchers fall into four basic categories. These are as follows.

1111

1 Techniques for examining differences: it is possible to examine differences in the proportion of cases that fall into different categories. An example would be:

Is the proportion of men who are accepted for jobs in engineering greater than the proportion of women accepted for jobs in engineering?

It is also possible to examine differences in the central tendency of continuous variables. An example would be:

Is the job commitment of part-time staff less than the job commitment of full-time staff?

2 Techniques for examining associations between two variables: you can use these techniques to examine the strength and direction of the association between two continuous variables. An example of the sort of association you may wish to examine is:

Is there an association between salary levels and job commitment and, if so, how strong is this association?

3 Techniques for examining how well scores on a variable can be predicted from scores on one or more other variables, and how much variation in a variable can be explained with one or more other variables. These techniques are based on associations between variables. Examples are:

How well can you predict the job performance of employees if you know how conscientious they are and how committed they are?

How much of the variation in the job performance of employees is explained by variation in their job commitment?

4 Techniques for identifying a small number of core theoretical variables underlying the rela-tions between a larger number of directly measured variables, and examining whether specific constructs are being successfully measured by a questionnaire. Again, these techniques are based on associations. Examples include:

Is there a set of core personality dimensions which underlie all of the specific ways in which people behave differently to each other?

If you have developed a questionnaire to measure conscientiousness and commitment, do all items in the questionnaire clearly measure one of these two constructs?

Within each of these four categories, a very large number of techniques for use in specific circum-stances have been developed. However, the aim here is only to cover those that you are most likely to use in your own research or find in journal articles. Chapter 5 will concentrate on the first of these four categories, differences. Chapter 6 will deal with associations, prediction and ways of identifying core theoretical variables and examining whether specific constructs are being successfully measured by a questionnaire.

As well as providing an introduction to a variety of techniques for analysing data, Chapters 5 and 6 also explain how to use SPSS to carry out these analyses, how to interpret the SPSS output and how to write up the results. For each statistical method there is a worked example.

If you have not carried out a particular analysis before, you may find it helpful to work through the example provided, using SPSS on your computer to reproduce the tables of data set out in this book.

When each type of statistical analysis is introduced, a table showing the size of the sample that will be required if it is to be used effectively is provided. The sample sizes have been computed from the information set out in the texts by Cohen (1988) and Bausell and Li (2002). These books, together with commercial software such as nQuery Advisor (www.statsol.ie/nquery/

nquery.htm) and Power and Precision (www.power-analysis.com) make it possible to compute very exact sample sizes for a variety of statistical tests and methods, usually using information about three things: the required level of statistical significance (e.g. .05 or .01), the required power (e.g. 80 per cent or 90 per cent), and the effect size expected in the data to be produced by the research study. In practice, sample size calculations can be difficult, because while it is a relatively straightforward matter to choose the desired significance level and power for a study, determining the expected effect size is often problematic (see the discussion of how to estimate effect sizes on pages 49–50). Commonly, the researcher will have no more than an approximate estimate of the effect size that he or she expects to find and, as a consequence, it will only be possible to provide a rough estimate of the required sample size. In the sample size tables presented in the following two chapters, the somewhat rough and ready nature of most effect size estimates is reflected in the sample size figures given. The intention has not been to present exact sample sizes (e.g. 983 people will be required), and they have generally been rounded up slightly (e.g. from 983 to 1,000). With sample size analysis, as with other aspects of statistics, it is important to distinguish between accuracy and precision in presenting statistical information, and very precise sample sizes often suggest a misleadingly high level of accuracy in the estimated effect size. The reason for this is that, as explained above, most researchers do not have an exact and dependable estimate of the effect size they expect to find in the study they are about to carry out. If you do have a fairly precise and dependable estimate of the expected effect size, and obtaining a very accurate estimate of the sample size you will require in your study is important, refer to the texts by Bausell and Li (2002), or Cohen (1988), or to software such as Nquery Advisor, or Power and Precision. These provide detailed and precise sample size estimates (as well as making it possible to work out the power of a study with a known sample size). However, for those without very accurate and dependable effect size estimates, the tables presented here should prove helpful. Certainly, as you will see, they make it abundantly clear that with large effect sizes quite small sample sizes of 50 cases or less will often suffice, but that when small effects are present it is very likely that large samples of several hundred cases will be necessary.

They also give a reasonable guide to a sensible sample size to use if you have no idea of the effect size you expect to find in your research study.

1111

INTRODUCTION TO PART II

Chapter 5

Statistical methods for