Statistics in Psychology Using R and SPSS E-Book

Dieter Rasch

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 936

Veröffentlichungsjahr: 2011

Beschreibung

Statistics in Psychology covers all statistical methods needed in education and research in psychology. This book looks at research questions when planning data sampling, that is to design the intended study and to calculate the sample sizes in advance. In other words, no analysis applies if the minimum size is not determined in order to fulfil certain precision requirements. The book looks at the process of empirical research into the following seven stages: * Formulation of the problem * Stipulation of the precision requirements * Selecting the statistical model for the planning and analysis * The (optimal) design of the experiment or survey * Performing the experiment or the survey * Statistical analysis of the observed results * Interpretation of the results.

Leseprobe

Contents

Cover

Title Page

Copyright

Preface

Acknowledgments

Part I: INTRODUCTION

1: Concept of the book

2: Measuring in psychology

2.1 Types of psychological measurements

2.2 Measurement techniques in psychological assessment

2.3 Quality criteria in psychometrics

2.4 Additional psychological measurement techniques

2.5 Statistical models of measurement with psychological roots

3: Psychology – an empirical science

3.1 Gain of insight in psychology

3.2 Steps of empirical research

4: Definition – character, chance, experiment, and survey

4.1 Nominal scale

4.2 Ordinal scale

4.3 Interval scale

4.4 Ratio scale

4.5 Characters and factors

Part II: DESCRIPTIVE STATISTICS

5: Numerical and graphical data analysis

5.1 Introduction to data analysis

5.2 Frequencies and empirical distributions

5.3 Statistics

5.4 Frequency distribution for several characters

Part III: INFERENTIAL STATISTICS FOR ONE CHARACTER

6: Probability and distribution

6.1 Relative frequencies and probabilities

6.2 Random variable and theoretical distributions

6.3 Quantiles of theoretical distribution functions

6.4 Mean and variance of theoretical distributions

6.5 Estimation of unknown parameters

7: Assumptions – random sampling and randomization

7.1 Random sampling in surveys

7.2 Principles of random sampling and randomization

8: One sample from one population

8.1 Introduction

8.2 The parameter μ of a character modeled by a normally distributed random variable

8.3 Planning a study for hypothesis testing with respect to μ

8.4 Sequential tests for the unknown parameter μ

8.5 Estimation, hypothesis testing, planning the study, and sequential testing concerning other parameters

9: Two samples from two populations

9.1 Hypothesis testing, study planning, and sequential testing regarding the unknown parameters μ1 and μ2

9.2 Hypothesis testing, study planning, and sequential testing for other parameters

9.3 Equivalence testing

10: Samples from more than two populations

10.1 The various problem situations

10.2 Selection procedures

10.3 Multiple comparisons of means

10.4 Analysis of variance

Part IV: DESCRIPTIVE AND INFERENTIAL STATISTICS FOR TWO CHARACTERS

11: Regression and correlation

11.1 Introduction

11.2 Regression model

11.3 Correlation coefficients and measures of association

11.4 Hypothesis testing and planning the study concerning correlation coefficients

11.5 Correlation analysis in two samples

Part V: INFERENTIAL STATISTICS FOR MORE THAN TWO CHARACTERS

12: One sample from one population

12.1 Association between three or more characters

Summary

12.2 Hypothesis testing concerning a vector of means μ

12.3 Comparisons of means and ‘homological’ methods for matched observations

13: Samples from more than one population

13.1 General linear model

13.2 Analysis of covariance

13.3 Multivariate analysis of variance

13.4 Discriminant analysis

Part VI: MODEL GENERATION AND THEORY-GENERATING PROCEDURES

14: Model generation

14.1 Theoretical basics of model generation

14.2 Methods for determining the quality and excellence of a model

14.3 Simulation – non-analytical solutions to statistical problems

15: Theory-generating methods

15.1 Methods of descriptive statistics

15.2 Methods of inferential statistics

Appendix A: Data input

Data input in R

Data input in SPSS

Data list 1.1

Appendix B: Tables

Appendix C: Symbols and notation

References

Index

This edition first published 2011 © 2011 John Wiley & Sons, Ltd

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Library of Congress Cataloging-in-Publication Data

Statistics in psychology using R and SPSS / Dieter Rasch, Klaus D. Kubinger and Takuya Yanagida.

p. ; cm.

Includes bibliographical references and index.

ISBN 978-0-470-97124-6 (cloth) – ISBN 978-1-119-97964-7 (E-PDF) – ISBN 978-1-119-97963-0 (O-book) – ISBN 978-1-119-95202-2 (E-Pub) – ISBN 978-1-119-95203-9 (Mobi)

1. Psychometrics.2. SPSS (Computer file)I. Rasch, Dieter.II. Kubinger, Klaus D., 1949- III.Yanagida, Takuya.

[DNLM: 1. SPSS (Computer file)2. Psychometrics–methods.3. Statistics as Topic–methods.4. Software. BF 39]

BF39.S7863 2011

150.72′7–dc23

2011020660

A catalogue record for this book is available from the British Library.

Print ISBN: 978-0-470-97124-6

ePDF ISBN: 978-1-119-97964-7

oBook ISBN: 978-1-119-97963-0

ePub ISBN: 978-1-119-95202-2

Mobi ISBN: 978-1-119-95203-9

Preface

This textbook contains, on the one hand, everything that is needed for a freshman statistician. On the other hand, it can also be used in advanced courses and in particular it can be used for empirical research work.

Within the Bachelor's curriculum it is only possible to demonstrate the correct use of the most important techniques. For the Master's curriculum, however, a certain understanding of these methods is necessary. For doctoral studies, understanding alone is not enough: a willingness to reflect critically on the statistical methods must be developed.

Since even for doctoral students a repetition of the basics of statistics on an elementary level is often useful, with this book they can be picked up individually where their powers of recollection end – if necessary at the beginning of the Bachelor education. And in contrast, Bachelor's students are often interested in the contents of a Master's curriculum or where the textbook leads. They can get a taste of that now.

Even lecturers will find something new in this textbook; according to our experience, ‘statistics for psychologists’ is not taught by professional statisticians but by psychologists, mostly by those at the beginning of their academic careers; anecdotes may at least help them didactically. These casual reflections can of course also be academically amusing for students.

Accordingly, the three to four mentioned target groups are guided through the book using distinctive design elements.

All examples given in this textbook refer to psychology as an empirical science. However, the topics covered in psychology are similar to those of (other) social sciences, above all sociology and educational science. So, of course, this textbook suits their framework as well.

The statistical methods that are recommended in this book and which can be used for answering the research questions posed by psychology as a science are often only practicable when using a computer. Therefore we refer to two software packages in this book. The program package R is both freely accessible and very efficient; that is why we continuously use R here. However, since in psychology the program package IBM SPSS Statistics is still preferred for statistical analyses most of the time, it is also illustrated using the examples; here we use version 19.

We try to present statistical knowledge as simply as possible using these program packages, and avoid formulas wherever reasonable. However, we did not completely avoid formulas because we also wish to help those readers interested in the theoretical background. As a matter of fact, more important than formulas is the procurement of appropriate applications and interpretations of statistical methods. And that is actually the main focus of this book.

We have refrained from citing the exact sources for the practical, everyday methods given, reserving that for methods that are new or uncommon.

With the hope that the reader may easily gather from this textbook all information relevant to his/her individual academic level.

This book includes an accompanying website. Please visit www.wiley.com/go/statisticsinpsychology

Dieter Rasch, Klaus D. Kubinger, and Takuya Yanagida Rostock and Vienna

Acknowledgments

The authors wish to express their thanks to those who contributed in either the translation or in the programs for this book, especially:

Dr. Albrecht Gebhardt, Alpen Adria University of Klagenfurth, Austria,

who gave us access to the R-package OPDOE and assistance in programming the sequential triangular tests.

In translating the text from German into English we received help from (in alphabetical order):

Maximilian Alexander Hetzel, University of Vienna, Austria

Nina Heuberger, University of Vienna, Austria

Mag. Jürgen Grafeneder, University of Vienna, Austria

Mag. Bernhard Piskernik, University of Vienna, Austria

Sarah Treiber, University of Vienna, Austria

Mag. Alexander Uitz, University of Vienna, Austria

Because the authors and translators are not native English speakers, we are happy that we found help from (in alphabetical order):

Mag. Carrie Kovacs, University of Luxembourg

Mag. Renate Dosanj, University of Vienna, Austria

Sandra Almgren, Kremmling, Colorado, USA

Peter Loetscher, University of Vienna, Austria

We thank, for a lot of editorial work (in alphabetical order):

Bettina Hagenmüller, University of Vienna, Austria

Mag. Bernhard Piskernik, University of Vienna, Austria

We thank Prof. Dr. Rob Verdooren, Wageningen, The Netherlands for carefully reading many chapters and giving helpful remarks, Dr. Maciej Rosolowski from the University of Magdeburg, Germany for the R-programs for the principal component tests.

We further thank IBM SPSS STATISTICS for providing the most recent version 19 of the IBM SPSS STATISTICS program.

Last but not least, we thank Mag. Joachim Fritz Punter, Medical University of Vienna, Austria for producing all figures as a reproduction proof, but above all for editing the antecessor book.

Part I

INTRODUCTION

This textbook requires a multi-layered view of ‘statistics in psychology’. Within the Bachelor's curriculum it is only possible to demonstrate the correct use of the most important techniques. For the Master's curriculum, however, a certain understanding of these methods is necessary: for the Master's thesis, where usually a scientific question has to be worked on single-handed but under supervision, the student has to refer to statistical analyses in literature concerning the topic, and if necessary to improve the choice of the method used for analysis. For doctoral studies, understanding alone is not enough; a willingness to reflect critically on the statistical methods must be developed. The statistical methods used in the doctoral thesis, which means the entrance to a scientific career, have to be oriented on state-of-the-art methodological developments; the ability to follow these developments requires profound knowledge as well as the aptitude to evaluate new statistical methods regarding their shortcomings.

Since even for doctoral students a repetition of the basics of statistics on an elementary level is often useful, with this book they can be picked up individually where their powers of recollection end – if necessary at the beginning of the Bachelor's education. And in contrast, Bachelor's students are often interested in the contents of a Master's curriculum or where the textbook leads. They can get a taste of that now.

Finally, even lecturers will find something new in this textbook; according to our experience ‘statistics for psychologists’ is not taught by professional statisticians but by psychologists, mostly by those at the beginning of their academic careers; anecdotes may at least help them didactically. These casual reflections can of course also be academically amusing for students.

Accordingly, the three to four mentioned target groups are guided through the book using distinctive design elements.

The running text, without special accentuation, is directed at all target groups. It is information essential for the further study of the textbook and its practical use – as is this introduction before Chapter 1. Also the terminology used in the book has to be conveyed in a standardized way. Finally, some contents, which should be familiar to doctoral students, are nevertheless aimed at all target groups because we think that repetition is useful.

Moreover, special symbols and labels on the outer edge of some pages signal the target group that the information is aimed at. Target groups other than the ones indicated with the symbol can skip these passages without being in danger of missing the respective educational aim.

The symbol indicates that the material in these passages is aimed particularly at Bachelor's students since it deals only with the Ability to Use. The symbol on the outer edge indicates that here the reader finds an explanation of the underlying methods, without using a mathematical derivation that is too detailed; this is about Understanding. The symbol on the outer edge of the page announces that the shortcomings of the method will be discussed and that common misuses will be indicated; this is about Critical Reflection. Finally, the note For Lecturers signals didactically useful observations, entailing understanding of the respective topic in a very demonstrative way.

In order to bring all target groups together again, occasionally a Summary is given. At the beginning of every chapter a short description of its contents is given.

1

Concept of the book

In this chapter, the structure of the book and accordingly the didactic concept are presented to the reader. Moreover, we outline an example that will be used in several chapters in order to demonstrate the analytical methods described there.

In six sections this book conveys the methods of the scientific discipline of ‘statistics’ that are relevant for studies in psychology:

I. Introduction (Chapters 1 to 4)

II. Descriptive statistics (Chapter 5)

III. Inferential statistics for a single character (Chapters 6 to 10)

IV. Descriptive and inferential statistics for two characters (Chapter 11)

V. Inferential statistics for more than two characters (Chapters 12 and 13)

VI. Theory building statistical procedures (Chapters 14 and 15).

Chapter 1 explains the concept underlying our presentation of the methods. Furthermore an empirical example that will be used as an illustration in various parts of the book is provided.

Chapter 2 will demonstrate that quantifying and measuring in psychology is not only possible but also very useful. In addition we would like to give the reader an understanding of the strategy of gaining knowledge in psychology as a science; the approach however is similar to other scientific fields, which is why this book can be used in other fields too.

In Chapter 3 we will address the issue that empirical research is performed in several steps. For all scientific questions that are supposed to be answered by the study (as diverse as they might be regarding contents), exact planning, careful collecting of data, and adequate analysis are always needed.

Within this context we wish the reader to realize that a study does not always have to include all the people that the research question is directed at. Out of practical reasons, most of the time only part of the group of interest can be examined; this part is usually called sample, whereas the group of interest is called the population. Chance plays an important role here. It will be shown that we have to make probability statements for the results of the statistical analysis; the probability calculus used for this is only valid for events for whose occurrence (or non-occurrence) chance is responsible. For example, a certain event might be that a specific person is part of the study in question. We will treat this topic in Chapter 4, as well as in Chapter 7. Since ‘chance’ often has a different meaning in everyday use as opposed to its general meaning in statistics and therefore in this book, we will point out at this early stage that a random event is not necessarily a rare or unanticipated event.

Finally, if data concerning one or more person(s) or character(s) that are of interest have been gathered within the framework of the study, they have to be processed statistically. The data in their totality are too unmanageable to be able to draw conclusions from them that are relevant for answering the scientific question. Therefore, special methods of data compression are necessary. We will deal with this issue in Chapter 5. The decision of which one of these methods is applicable or most appropriate is substantially based on the type of data: for example, whether they have been derived from physical measurements or whether they can only express greater/less than and equal to relations. In the latter case it is important to use methods that have been specially developed for this type of data.

Mathematical-statistical concepts are needed, especially for the generalization of study results; these will be introduced in Chapter 6. For readers who are unpracticed in the use of formulas, this chapter is surely difficult, although we try to formulate as simply as possible.

If the generalization of the study results is the aim, then a prerequisite for the use of appropriate methods is that the collected samples are random samples; information on this topic can be found in Chapter 7.

In Chapter 8 an introduction to statistical inference, in particular the principle of hypothesis testing, will be given. Because of the fact that random samples are used, it is necessary to take random deviations of the sample data from the population into account. Through hypotheses that have been formulated before data collection we try to find out as to what extent these deviations are systematic or can/must be traced back to chance. The aim is to either accept or reject a hypothesis based on the empirical data.

Chapter 9 pursues a similar objective, but this time the focus is on two populations that are compared with each other.

The implied separation between planning, data collection, and analysis is true for the classic procedure for empirical studies. In this book, however, we also want to promote a sequential approach. Thereby the gradual collection of data is constantly interrupted by an analysis. This leads to a process that looks like this: observe–analyze–observe–analyze …; this goes on until a predetermined level of precision is reached. This procedure is also described in Chapters 8 and 9.

Special methods are needed in studies that examine a certain character of the research unit (which in psychology often is a person or a group of persons) not only under constant conditions but also under varying conditions or when the study includes more than two populations. In Chapter 10 we cover situations where there are three or more different conditions or two or three treatment factors, with at least two values of each (treatment or factor levels).

In psychological research hardly ever is only one character used. If more than one character per person is observed, then a certain connection between them may exist; we refer here to statistical relationships. If these relationships are of interest, then the statistical methods described primarily in Chapter 11 are needed.

If there really are relationships between several characters – or if there is reason to think so – then one needs very special methods for comparing several populations. Chapters 12 and 13 describe these.

Finally Chapters 14 and 15 give an introduction into theory-building techniques that establish or test models regarding content.

The appendix of the book is split into three parts: Part A lists the data of Example 1.1 which will be illustrated below, and in part B one can find tables, helpful for some analyses; often it is faster and more convenient to look up a value than to calculate it with the help of some software. Appendix C contains a summary of the symbols and abbreviations. A complete list of references and a subject index are given at the end of the book.

Summary

We assume that empirical studies always yield data regarding at least one character. Optimally, planning takes place prior to any study. Data are used to answer a specific question. Statistics as a scientific discipline provides the methods needed for this.

The diverse statistical methods that are recommended in this book and which can be used for answering the research questions posed by psychology as a science are often only practicable when using a computer. Therefore we refer to two software packages in this book. The program package R is both freely accessible and very efficient; that is why we continuously use R here. However, since in psychology the program package IBM SPSS Statistics is still preferred for statistical analyses most of the time, it will also be illustrated using the examples. The appropriate use of such packages is not trivial; that is why the necessary procedures will be demonstrated by the use of numerical examples. The reader can recalculate everything and practice their use.

The program package R can be used for the planning of a study, for the statistical analysis of the data and for graphical presentation. It is an adaptation of the programming language S that has been developed since 1976 by John Chambers and colleagues in the Bell Laboratories (belonging to Alcatel-Lucent). The functionality of R can be enhanced through freely available packages by everybody and at will, and also special statistical methods and some procedures of C and Fortran can be implemented. Packages that already exist are being made available in standardized archives (repositories). The most well-known archive to be mentioned here is CRAN (Comprehensive R Archive Network), a server network that is serviced by the R Development Core Team. With the distribution of R, the number of R packages has increased exponentially: whereas there were 110 packages available on CRAN in June 2001, there were 2496 in September 2010. R is available, free, for Windows, Linux and Apple. With few exceptions, there are implementations for all statistical methods in R. With the means of the recently built package OPDOE (see Rasch, Pilz, Verdooren & Gebhardt, 2011), it is possible, for the first time, to statistically plan studies or to calculate the optimal number of examination objects and also to successively collect and analyze data in R.

The program package R is available for free at http://cran.r-project.org/ for the operating systems Linux, MacOS X and Windows. The installation under Microsoft Windows is initiated via the link ‘Windows’, from where the link ‘base’, which leads to the installation website, must be chosen. The setup file can be downloaded under ‘Download R 2.X.X for Windows’ (where X stands for the current version number). After executing this file, one is lead through the installation by a setup assistant. For the uses described in this book all the standard settings can be applied. SPSS as a commercial product must be acquired by purchase; normally universities offer inexpensive licenses for students. More on R can be found under www.r-project.org, and on SPSS under www.spss.com. In order not to unnecessarily prolong the explanation of the operational sequence in R or SPSS, we always assume that the respective program package, as well as the file that will be used, are already at hand and open.

In R the input window opens after starting the program; the prompt is in red: ‘>’. Here commands can be entered and run by pressing the enter button. The output is displayed in blue right below the command line. If the command is incomplete, a red ‘+’ will appear in the next line in order to complete the command or to cancel the current command input by pressing the Esc button. An instruction sequence is displayed as in the following example:

> cbind(sub1_t1.tab, sub1_t1.per, sub1_t1.cum)

or also as

> cbind(sub1_t1.tab,+ sub1_t1.per,+ sub1_t1.cum)

or also as

> cbind(sub1_t1.tab,+ sub1_t1.per,+ sub1_t1.cum)

A special working environment in R is the Workspace. Several (calculation-) objects that have been created in the current session with R can be saved in there. These objects include results of calculations (single scores, tables, etc.) and also data sets. A workspace can be loaded with the sequence

File - Load Workspace...

For all the examples presented in this book the reader can download the Workspace ‘RaKuYa.RData’ from the website www.wiley.com/go/statisticsinpsychology.

Since there are more data sets in our Workspace, the scores of single research units/persons have to be accessed by specifying the data set with a ‘$’; for example: Example_1.1$native_language. A useful alternative for the access is the command attach(), which makes the desired data set generally available; for example: attach(Example_1.1). To minimize repetition, in the instruction sequences given throughout the book, we assume that the attach() command has already been run and therefore the relevant data set is active. For some examples we need special R packages; they must be installed once via the menu Packages - Install Package(s)… and then loaded for every session in R with the command library(). The installation of packages is done via the menu

Packages - Install Package(s)...

In SPSS the desired data frame can be opened via File – Open – Data… after starting the program. Then we write the instruction sequence as in SPSS handbooks; for example like this

Analyze Descriptive Statistics Frequencies…

For all examples in the book the reader can find the data in the SPSS folder ‘RaKuYa’ on the website www.wiley.com/go/statisticsinpsychology.

For figures that are shown as the results of the calculations for the examples, we use either the one from SPSS or the one from R. Only if the graphs differ between R and SPSS will we present both.

It is the concept of this textbook to present illustrative examples with content – that can be recalculated – from almost all subject areas concerning the planning and statistical analysis of psychological studies. A lot of the methods described in this book will be demonstrated using one single data set in order to not have to explain too many psychological problems. This will be introduced in Example 1.1.

Example 1.1 The goal is to test the fairness of a popular natural-language intelligence test battery with reference to children with Turkish native language1,2 (see Kubinger, 2009a3).

The following characters were observed per child (see Table 1.1 and the data sheet in Appendix A; then see, for R, the respective data structure in Figure 1.1, and for SPSS the screen shot shown in Figure 1.2).

Table 1.1 The characters and their names in R and SPSS (including coded values)4.5

Figure 1.1 Representation of the data structure of Example 1.1 in R.

In order to illustrate some statistical procedures we need other examples regarding content, but the data for these examples will not be found in Appendix A due to space limitations; however they are provided in the aforementioned Workspace and SPSS folders respectively. For the recalculation of the examples as well as for later calculations with the reader's own data, we will also provide the R instruction sequences, so that they don't have to be typed out. They can be found on the website www.Wiley.com. For beginners in R these are simply listed in order in a PDF file; for those readers already experienced in the use of R they are in a syntax editor for R; that is, Tinn-R (www.sciviews.org/Tinn-R/).

Figure 1.2 Part of the data view of Example 1.1 in SPSS.

References

Kleining, G. & Moore, H. (1968). Soziale Selbsteinstufung (SSE): Ein Instrument zur Messung sozialer Schichten [Social Self-esteem (SEE): An Instrumnet for Measuring the Social Status]. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 20, 502–552.

Kubinger, K. D. (2009a). Adaptives Intelligenz Diagnostikum - Version 2.2 (AID 2) samt AID 2-Türkisch [Adaptive Intelligence Diagnosticum, AID 2-Turkey Included]. Göttingen: Beltz.

Kubinger, K. D. (2009b). Psychologische Diagnostik – Theorie und Praxis psychologischen Diagnostizierens (2nd edn) [Psychological Assessment – Theory and Practice of Psychological Consulting]. Göttingen: Hogrefe.

Rasch, D., Pilz, J., Verdooren, R. L., & Gebhardt, A. (2011). Optimal Experimental Design with R. Boca Raton: Chapman & Hall/CRC.

1. Fairness is a specific quality criterion of psychological assessment methods (tests). A psychological test meets the requirement of fairness if the resulting test scores don't lead to a systematic discrimination of specific testees: for example because of sex, ethnic, or socio-cultural affiliation; see Kubinger, 2009b).

2. The data originally applied to German-speaking countries; however, there was no socio-political difference when the data in the following analyses were interpreted as relating to English-speaking countries and some ethnic-minority groups.

3. Due to copyright reasons the original data had to be slightly modified; therefore no deductions regarding content can be drawn from the data found in the data sheet in the appendix.

4. The gestational age is the age of the (unborn) child counted from the day of supposed fertilization.

5. Test scores are generally standardized to a certain scale; T-Scores are a very common method of standardization.

2

Measuring in psychology

This chapter deals with several methods of data acquisition that are used in psychology. The methods for psychological assessment and the methods primarily for answering research questions have to be distinguished.

Within the field of psychology, the claim of conducting measurements, e.g. to measure ‘psyche’ or psychological phenomena, is often adamantly refuted. The attempt to measure or to quantify would not allow for the specific, individual, and qualitative characteristics of a person. Instead, the assessment of the personality of a person should be performed in a qualitative way.

Psychology as a science demonstrates though that this approach to the assessment of a person, regarding a specific character (within psychology: trait/aptitude), is limited to a pre-scientific level. While it can lead to important assumptions on causal relations, it never allows for binding generalizations. On the contrary, measurements that are conducted under defined abstractions can relate a person's personality to an objective framework.

Statistical data calls for a useful bundling of what is to be measured. Not everything that is measurable regarding a certain character can be compared in depth, i.e. individually, but the whole essential part of the information has to be compressed. A factually acceptable abstraction of the available information has to be made. For example, this abstraction could be that all 35-year-old women are viewed equally regarding their age, irrespective of whether one of them has a biologically ‘young’ body caused by practicing competitive sports or another one has a biologically ‘old’ body because she lived in war zones for some years.

We can be sure that measuring in psychology is valuable for psychological case consulting as well as for research on the evaluation of psychological treatments, and especially for basic psychological research.

Although there are measurement techniques in psychology that follow the methods of natural science, measurements of psychic or mental phenomena are additionally based on specific scientific methods. One thing, however, is common to all natural sciences, psychology included: measuring means the ascertainment of the interesting character's value for the research unit (in psychology this is mostly a person). This happens as an assignment of numbers or signs in such a way that these assignments (measuring values), represent empirical factual relations. That is, the assignment relations must coincide with the empirical (obviously) given relationships of the research units (discussed in detail in Chapter 4).

Although important to note here but not explicitly a distinct measurement technique are the measurements of physiological psychology: its first sub-specialty, neuropsychology, studies the relationship between behavior and the activity of the central nervous system by the means of electrophysiological methods (e.g. EEG, electroencephalography). Its second sub-specialty, psycho-physiology, investigates the relationships between behavior and the activity of the vegetative nervous system by the means of physical methods (e.g. measurement of electro-dermal activity, EDA). Its third sub-specialty, chemical psychology, explores the relations between behavior and chemical substances, which are either brought into the organism from outside (pharmaco-psychology) or are built inside the organism (endocrine psychology, neuro-chemopsychology, psycho-genetics) by the means of chemical methods.