This is a relationship in which a uniform change in one characteristic is combined with an uneven change in another. This situation is typical for psychology. Correlation coefficient formulas: When comparing ordinal data, apply rank correlation coefficient according to Ch. Spearman (ρ): ρ = 6Σd 2 / N (N 2 - 1), where: d is the difference in ranks (ordinal places) of two quantities, N is the number of compared pairs of values ​​of two variables (X and Y). When comparing metric data, use product correlation coefficient according to K. Pearson (r): r = Σ xy / Nσ x σ y where: x is the deviation of an individual value of X from the sample average (M x), y is the same for Y, O x is the standard deviation for X, a - the same for Y, N - the number of pairs of values ​​of X and Y. The introduction of computer technology into scientific research makes it possible to quickly and accurately determine any quantitative characteristics of any data arrays. Various computer programs have been developed that can be used to carry out appropriate statistical analysis of almost any sample. Of the mass of statistical techniques in psychology, the most widely used are the following: 1) complex calculation of statistics; 2) correlation analysis; 3) analysis of variance; 4) regression analysis; 5) factor analysis; 6) taxonomic (cluster) analysis; 7) scaling.

2.1.2.2. Comprehensive statistics calculation

Using standard programs, both the main sets of statistics presented above and additional ones not included in our review are calculated. Sometimes the researcher is limited to obtaining these characteristics, but more often the totality of these statistics represents only a block included in a wider set of indicators of the sample being studied, obtained using more complex programs. Including programs that implement the methods of statistical analysis given below.

2.1.2.3. Correlation analysis

Reduces to calculating correlation coefficients in a wide variety of relationships between variables. The relationships are set by the researcher, and the variables are equivalent, i.e., what is the cause and what is the effect cannot be established through correlation. In addition to the closeness and direction of connections, the method allows you to establish the form of connection (linearity, nonlinearity). It should be noted that nonlinear connections cannot be analyzed using mathematical and statistical methods generally accepted in psychology. Data related to nonlinear zones (for example, at points where connections are broken, in places of abrupt changes) are characterized through meaningful descriptions, refraining from their formal quantitative presentation. Sometimes it is possible to use nonparametric mathematical and statistical methods and models to describe nonlinear phenomena in psychology. For example, the mathematical theory of disaster is used.

2.1.2.4. Analysis of variance

Unlike correlation analysis, this method allows us to identify not only the relationship, but also the dependencies between variables, i.e., the influence of various factors on the characteristic being studied. This influence is assessed through dispersion relations. Changes in the characteristic being studied (variability) can be caused by the action of individual factors known to the researcher, their interaction and the effects of unknown factors. Analysis of variance makes it possible to detect and evaluate the contribution of each of these influences to the overall variability of the trait under study. The method allows you to quickly narrow the field of conditions influencing the phenomenon under study, highlighting the most significant of them. Thus, analysis of variance is “the study of the influence of variable factors on the variable being studied by variance.” Depending on the number of influencing variables, one-, two-, and multivariate analysis is distinguished, and depending on the nature of these variables - analysis with fixed, random or mixed effects. Analysis of variance is widely used in experimental design.

2.1.2.5. Factor analysis

The method makes it possible to reduce the dimension of the data space, i.e., to reasonably reduce the number of measured characteristics (variables) by combining them into certain aggregates that act as integral units characterizing the object being studied. In this case, these composite units are called factors, from which it is necessary to distinguish the factors of variance analysis, representing which are individual characteristics (variables). It is believed that it is the totality of signs in certain combinations that can characterize a mental phenomenon or the pattern of its development, while individually or in other combinations these signs do not provide information. As a rule, factors are not visible to the eye, hidden from direct observation. Factor analysis is especially productive in preliminary research, when it is necessary to identify, to a first approximation, hidden patterns in the area under study. The basis of the analysis is the correlation matrix, i.e. tables of correlation coefficients of each characteristic with all the others (the “all with all” principle). Depending on the number of factors in the correlation matrix, there are single-factor(according to Spearman), bi-factor(according to Holzinger) and multifactorial(according to Thurston) analyses. Based on the nature of the relationship between factors, the method is divided into analysis with orthogonal(independent) and with oblique(dependent) factors. There are other varieties of the method. The very complex mathematical and logical apparatus of factor analysis often makes it difficult to choose a method option that is adequate to the research tasks. Nevertheless, its popularity in the scientific world is growing every year.

2.1.2.6. Regression analysis

The method allows you to study the dependence of the average value of one quantity on variations of another (other) quantity. The specificity of the method lies in the fact that the quantities under consideration (or at least one of them) are random in nature. Then the description of the dependence is divided into two tasks: 1) identifying the general type of dependence and 2) clarifying this type by calculating estimates of the parameters of the dependence. There are no standard methods for solving the first problem, and here a visual analysis of the correlation matrix is ​​carried out in combination with a qualitative analysis of the nature of the quantities (variables) being studied. This requires high qualifications and erudition from the researcher. The second task is essentially finding an approximating curve. Most often this approximation is done using the mathematical method of least squares. The idea of ​​the method belongs to F. Galto- well, who noticed that very tall parents had children that were somewhat shorter, and very short parents had taller children. He called this pattern regression.

2.1.2.7. Taxonomic analysis

The method is a mathematical technique for grouping data into classes (taxa, clusters) in such a way that objects included in one class are more homogeneous in some respect compared to objects included in other classes. As a result, it becomes possible to determine in one metric or another the distance between the objects being studied and to give an orderly description of their relationships at a quantitative level. Due to the insufficient development of the criterion for the effectiveness and admissibility of cluster procedures, this method is usually used in combination with other methods of quantitative data analysis. On the other hand, taxonomic analysis itself is used as additional insurance for the reliability of results obtained using other quantitative methods, in particular, factor analysis. The essence of cluster analysis allows us to consider it as a method that explicitly combines quantitative processing data from their qualitative analysis. Therefore, it is apparently not legitimate to classify it unambiguously as a quantitative method. But since the procedure of the method is predominantly mathematical and the results can be presented numerically, then the method as a whole will be classified as quantitative.

2.1.2.8. Scaling

Scaling, to an even greater extent than taxonomic analysis, combines the features of quantitative and qualitative study of reality. Quantitative aspect scaling is that its procedure in the vast majority of cases includes measurement and numerical representation of data. Qualitative aspect scaling is expressed in the fact that, firstly, it allows you to manipulate not only quantitative data, but also data that does not have common units of measurement, and secondly, includes elements of qualitative methods (classification, typology, systematization). Another fundamental feature of scaling, which makes it difficult to determine its place in the general system of scientific methods, is combining procedures for data collection and processing. We can even talk about the unity of empirical and analytical procedures when scaling. Not only in a specific study is it difficult to indicate the sequence and separation of these procedures (they are often performed simultaneously and jointly), but also in theoretical terms it is not possible to detect a staged hierarchy (it is impossible to say what is primary and what is secondary). The third point that does not allow scaling to be unambiguously attributed to one or another group of methods is its organic “growth” into specific areas of knowledge and its acquisition along with signs general scientific method signs highly specific. If other methods of general scientific significance (for example, observation or experiment) can be quite easily presented both in general form and in specific modifications, then scaling at the level of the general without losing the necessary information is very difficult to characterize. The reason for this is obvious: the combination of empirical procedures with data processing in scaling. Empirics is concrete, mathematics is abstract, therefore the fusion of general principles of mathematical analysis with specific methods of data collection gives the indicated effect. For the same reason, the scientific origins of scaling have not been precisely defined: several sciences lay claim to the title of its “parent”. Among them is psychology, where such outstanding scientists as L. Thurston, S. Stevens, V. Torgerson, A. Pieron worked on the theory and practice of scaling. Having realized all these factors, we still place scaling in the category quantitative methods data processing, since in the practice of psychological research scaling occurs in two situations. The first one is construction scales, and the second - their usage. In the case of construction, all the mentioned features of scaling are fully manifested. When used, they fade into the background, since the use of ready-made scales (for example, “standard” scales for testing) simply involves comparison. Comparison with them of indicators obtained at the data collection stage. Thus, here the psychologist only uses the fruits of scaling, and at the stages following the collection of data. This situation is a common phenomenon in psychology. In addition, the formal construction of scales, as a rule, is carried out beyond the scope of direct measurements and collection of data about an object, i.e., the main scale-forming actions of a mathematical nature are carried out after the collection of data, which is comparable to the stage of their processing. In the most general sense scaling is a way of understanding the world through modeling reality using formal (primarily numerical) systems. This method is used in almost all areas of scientific knowledge (in natural, exact, humanities, social, technical sciences) and has wide applied significance. The most rigorous definition seems to be the following: scaling is the process of mapping empirical sets into formal ones according to given rules. Under empirical set refers to any set of real objects (people, animals, phenomena, properties, processes, events) that are in certain relationships with each other. These relations can be represented by four types (empirical operations): 1) equality (equal - not equal); 2) rank order (more - less); 3) equality of intervals; 4) equality of relations. By In the nature of the empirical set, scaling is divided into two types: physical And psychological. IN In the first case, objective (physical) characteristics of objects are subject to scaling, in the second - subjective (psychological). Under formal set is understood as an arbitrary set of symbols (signs, numbers) interconnected by certain relations, which, according to empirical relations, are described by four types of formal (mathematical) operations: 1) “equal - not equal” (= ≠); 2) “more - less” (><); 3) «сло-жение - вычитание» (+ -); 4) «умножение - деление» (* :). При шкалировании обязательным условием является one-to-one correspondence between the elements of the empirical and formal sets. This means that each element of the first multiplicity Only one element of the second must correspond to each other, and vice versa. In this case, a one-to-one correspondence of the types of relations between the elements of both sets (isomorphism of structures) is not necessary. If these structures are isomorphic, the so-called direct (subjective) scaling, in the absence of isomorphism, is carried out indirect (objective) scaling. The result of scaling is the construction scales(lat. scala - “ladder”), i.e. some sign (numerical) models of the reality under study, with the help of which this reality can be measured. Thus, scales are measuring instruments. A general idea of ​​the whole variety of scales can be obtained from works where their classification system is given and brief descriptions of each type of scale are given. The relationships between the elements of the empirical set and the corresponding admissible mathematical operations (admissible transformations) determine the level of scaling and the type of the resulting scale (according to the classification of S. Stevens). The first, simplest type of relationship (= ≠) corresponds to the least informative name scales, second (><) - order scales, third (+ -) - interval scales, fourth (*:) - the most informative relationship scales. Process psychological scaling can be conditionally divided into two main stages: empirical, at which data is collected about the empirical set (in this case, about the set of psychological characteristics of the objects or phenomena being studied), and the stage formalization, i.e. mathematical and statistical processing of data at the first stage. The features of each stage determine the methodological techniques for a specific implementation of scaling. Depending on the objects of study, psychological scaling comes in two varieties: psychophysical or psychometric. Psychophysical scaling consists in constructing scales for measuring the subjective (psychological) characteristics of objects (phenomena) that have physical correlates with the corresponding physical units of measurement. For example, the subjective characteristics of sound (loudness, pitch, timbre) correspond to physical parameters of sound vibrations: amplitude (in decibels), frequency (in hertz), spectrum (in terms of component tones and envelope). Thus, psychophysical scaling makes it possible to identify the relationship between the values ​​of physical stimulation and mental reaction, as well as to express this reaction in objective units of measurement. As a result, any types of indirect and direct scales of all levels of measurement are obtained: scales of names, order, intervals and ratios. Psychometric scaling consists in constructing scales for measuring the subjective characteristics of objects (phenomena) that do not have physical correlates. For example, personality characteristics, the popularity of artists, team cohesion, expressiveness of images, etc. Psychometric scaling is implemented using some indirect (objective) scaling methods. As a result, judgment scales are obtained that, according to the typology of permissible transformations, usually belong to order scales, less often to interval scales. In the latter case, the units of measurement are indicators of the variability of judgments (answers, assessments) of respondents. The most characteristic and common psychometric scales are rating scales and attitude scales based on them. Psychometric scaling underlies the development of most psychological tests, as well as measurement methods in social psychology (sociometric methods) and in applied psychological disciplines. Since the judgments underlying the psychometric scaling procedure can also be applied to physical sensory stimulation, these procedures are also applicable to identify psychophysical dependencies, but in this case the resulting scales will not have objective units of measurement . Both physical and psychological scaling can be unidimensional or multidimensional. One-dimensional scaling is the process of mapping an empirical set into a formal set according to one criterion. The resulting one-dimensional scales reflect either relationships between one-dimensional empirical objects (or the same properties of multidimensional objects), or changes in one property of a multidimensional object. One-dimensional scaling is implemented using both direct (subjective) and indirect (objective) scaling methods. Under multidimensional scaling the process of mapping an empirical set into a formal set simultaneously according to several criteria is understood. Multidimensional scales reflect either relationships between multidimensional objects, or simultaneous changes in several characteristics of one object. The process of multidimensional scaling, in contrast to one-dimensional scaling, is characterized by a greater labor intensity of the second stage, i.e., data formalization. In this regard, a powerful statistical and mathematical apparatus is used, for example, cluster or factor analysis, which is an integral part of multidimensional scaling methods. The study of multidimensional scaling problems is related to With named after Richardson and Torgerson, who proposed his first models. Shepard started the development of non-metric multidimensional scaling methods. The most widespread and first theoretically substantiated multidimensional scaling algorithm was proposed by Kruskal. M. Davison summarized information on multidimensional scaling. The specifics of multidimensional scaling in psychology are reflected in the work of G.V. Paramei. Let us expand on the previously mentioned concepts of “indirect” and “direct” scaling. Indirect, or objective, scaling is the process of mapping an empirical set into a formal one with mutual inconsistency (lack of isomorphism) between the structures of these sets. In psychology, this discrepancy is based on Fechner’s first postulate about the impossibility of direct subjective assessment of the magnitude of one’s sensations. To quantify sensations, external (indirect) units of measurement are used, based on various assessments of the subjects: barely noticeable differences, reaction time (RT), variance of discrimination, spread of categorical assessments. Indirect psychological scales, according to the methods of their construction, initial assumptions and units of measurement, form several groups, the main of which are the following: 1) accumulation scales or log-rhythmic scales; 2) scales based on the measurement of BP; 3) judgment scales(comparative and categorical). The analytical expressions of these scales are given the status of laws, the names of which are associated with the names of their authors: 1) Weber-Fechner logarithmic law; 2) for- Pieron's con (for a simple sensorimotor reaction); 3) Thurston’s law of comparative judgments and 4) Tor-gerson’s law of categorical judgments. Judgment scales have the greatest applied potential. They allow you to measure any mental phenomena, implement both psychophysical and psychometric scaling, and provide the possibility of multidimensional scaling. According to the typology of permissible transformations, indirect scales are mainly represented by scales of order and intervals. Direct, or subjective, scaling is the process of mapping an empirical set into a formal one with a one-to-one correspondence (isomorphism) of the structures of these sets. In psychology, this correspondence is based on the assumption of the possibility of direct subjective assessment of the magnitude of one’s sensations (the denial of Fechner’s first postulate). Subjective scaling is implemented using procedures that determine how many times (or by how much) the sensation caused by one stimulus is greater or less than the sensation caused by another stimulus. If such a comparison is made for sensations of different modalities, then we talk about cross-modal subjective scaling. Direct scales, according to the method of their construction, form two main groups: 1) scales based on the definition sensory relationships; 2) scales based on definition magnitudes of incentives. The second option opens the way to multidimensional scaling. A significant part of direct scales is well approximated by a power function, which was proved by S. Stevens, using a large amount of empirical material, after whom the analytical expression of direct scales is named - Stevens' power law. To quantify sensations during subjective scaling, psychological units of measurement are used, specialized for specific modalities and experimental conditions. Many of these units have generally accepted names: “sons” for loudness, “brils” for brightness, “gusts” for taste, “vegs” for heaviness, etc. According to the typology of permissible transformations, direct scales are represented mainly by scales intervals and relations. In conclusion of the review of the scaling method, it is necessary to point out the problem of its relationship with measurement. In our opinion, this problem is due to the scaling features noted above: 1) combined the introduction of empirical procedures for data collection and analytical procedures for data processing; 2) the unity of the quantitative and qualitative aspects of the scaling process; 3) a combination of general science and narrow profile, i.e., the “fusion” of general principles of scaling with specific procedures of specific techniques. Some researchers explicitly or implicitly equate the concepts of “scaling” and “measurement”. This point of view is supported especially strongly by the authority of S. Stevens, who defined measurement as “the attribution of numerical forms to objects or events in accordance with certain rules” and immediately pointed out that such a procedure leads to the construction of scales. But since the process of developing a scale is a process of scaling, we end up with the result that measurement and scaling are one and the same thing. The opposite position is that only metric scaling associated with the construction of interval and proportional scales is compared with measurement. It seems that the second position is stricter, since measurement presupposes the quantitative expression of what is being measured, and therefore, the presence of a metric. The severity of the discussion can be removed if measurement is understood not as a research method, but as instrumental support for one or another method, including scaling. By the way, metrology (the science of measurements) includes the concept of “measurement” as a mandatory attribute of the measuring instrument. For scaling (at least for non-metric scaling), measuring instruments are not necessary. True, metrology is interested mainly in the physical parameters of objects, and not in the psychological ones. Psychology, on the contrary, is primarily concerned with subjective characteristics (large, heavy, bright, pleasant, etc.). This allows some authors to take the person himself as a means of measurement. This means not so much the use of parts of the human body as units of measurement (elbow, arshin, fathom, stade, foot, inch, etc.), but rather its ability to subjectively quantify any phenomena. But the infinite variability of individual differences in humans, including the variability of evaluative abilities, cannot provide information commonly used units of measurement at the stage of collecting data about the object. In other words, in the empirical part of scaling the subject cannot be considered as a measuring instrument. This role can, with great stretch, be attributed to him only after manipulations no longer with empirical, but with formal sets. Then a subjective metric is artificially obtained, most often in the form of interval values. G.V. Sukhodolsky points to these facts when he says that ordering (and this is what the subject does at the stage of “evaluation” of empirical objects) “is a preparatory, but not a measuring operation.” And only then, at the stage of processing primary subjective data, the corresponding scale-forming actions (for Sukhodolsky, ranking) “metrize the one-dimensional topological space of ordered objects, and. therefore, they measure the "magnitude" of objects." The ambiguity of the relationship between the concepts of "scaling" and "measurement" in psychology increases when they are compared with the concepts of "test" and "testing." There is no doubt that tests are classified as measuring instruments, however their application in psychology has two aspects. The first is the use of the test in the testing process, i.e., examination (psychodiagnostics) of specific psychological objects. The second is the development, or construction of the test. In the first case, with some reason we can say about measurement, since a reference measure - a standard scale - is “applied” to the examined object (test person).In the second case, it is obviously more correct to talk about scaling, since the quintessence of test construction is the process of constructing a standard scale and associated These are the operations of defining empirical and formal sets, the reliability and isomorphism of which are not least ensured by the standardization of the procedure for collecting empirical data and the collection of reliable “statistics”. Another aspect of the problem arises from the fact that the test as a measuring instrument consists of two parts: 1) a set of tasks (questions) with which the subject directly deals at the stage of collecting data about him and 2) a standard scale with which the test is compared. Empirical data are collected at the interpretation stage. Where should we talk about measurement, where about scaling, if they are not the same thing? It seems to us that the empirical part of the testing process, i.e., the test subject’s performance of the test task, is not a purely measurement procedure, but it is necessary for scaling. The argument is as follows: the actions performed by the subject themselves are not a measure of the severity of the qualities being diagnosed. Only the result of these actions (time spent, number of errors, type of answers, etc.), determined not by the test subject, but by the diagnostician, represents a “raw” scale value, which is subsequently compared with standard values. The indicators of the results of the subject’s actions are called “raw” here for two reasons. First of all, they. As a rule, they are subject to translation into other units of expression. Often - into “faceless”, abstract points, walls, etc. And secondly, a common thing in testing is the multidimensionality of the mental phenomenon being studied, which presupposes for its assessment the registration of several changing parameters, which are subsequently synthesized into a single indicator. Thus, only the stages of data processing and interpretation of test results, where “raw” empirical data are translated into comparable ones and the latter are applied to a “measuring ruler,” i.e., a standard scale, can be referred to as measurement without reservations. This problematic knot is being tightened even more tightly due to the isolation and development of such scientific sections as “Psychometry” and “Mathematical Psychology” into independent disciplines. Each of them considers the concepts we are discussing as their own key categories. Psychometry can be considered psychological metrology, covering “the whole range of issues related to measurement in psychology.” Therefore, it is not surprising that scaling is included in this “range of issues”. But psychometry does not clarify its relationship with measurement. Moreover, the matter is confused by the variety of interpretations of psychometric science itself and its subject. For example, psychometry is considered in the context of psychodiagnostics. “Often the terms “psychometry” and “psychological experiment” are used as synonyms... It is a very popular opinion that psychometry is mathematical statistics taking into account the specifics of psychology... A stable understanding of psychometry: the mathematical apparatus of psychodiagnostics. .. Psychometry is the science of using mathematical models in the study of mental phenomena.” As for mathematical psychology, its status is even more vague. “The content and structure of mathematical psychology have not yet acquired a generally accepted form; the choice and systematization of mathematical-psychological models and methods are to some extent arbitrary.” Nevertheless, there is already a tendency to absorb psychometry into mathematical psychology. It is still difficult to say whether this will affect the discussed problem of the relationship between scaling and measurement and whether their place in the general system of psychological methods will become clearer.

2.2. Qualitative methods

Qualitative methods (QM) make it possible to identify the most essential aspects of the objects being studied, which makes it possible to generalize and systematize knowledge about them, as well as to comprehend their essence. Very often, CMs rely on quantitative information. The most common techniques are: classification, typologization, systematization, periodization, casuistry.

2.2.1. Classification

2.2.2. Typology

2.2.3. Systematization

2.2.4. Periodization

2.2.5. Psychological casuistry

Psychological casuistry is a description and analysis of both the most typical and exceptional cases for the reality under study. This technique is typical for research in the field of differential psychology. An individual approach in psychological work with people also predetermines the widespread use of casuistry in practical psychology. A clear example of the use of psychological casuistry can be the incident method used in professional studies. [ 44]

3. INTERPRETATION METHODS

Even more than organizational ones, these methods deserve the name approaches, since they are, first of all, explanatory principles that predetermine the direction of interpretation of the research results. In scientific practice they have developed genetic, structural, functional, complex And systemic approaches. Using one method or another does not mean cutting out others. On the contrary, a combination of approaches is common in psychology. And this applies not only to research practice, but also to psychodiagnostics, psychological counseling and psychocorrection.

3.1. Genetic method

The genetic method is a way of studying and explaining phenomena (including mental ones), based on the analysis of their development both in ontogenetic and phylogenetic plans. This requires establishing: I) the initial conditions for the occurrence of the phenomenon, 2) the main stages and 3) the main trends in its development. The purpose of the method is to identify the connection of the phenomena being studied over time, to trace the transition from lower to higher forms. So wherever it is necessary to identify the time dynamics of mental phenomena, the genetic method is an integral research tool for the psychologist. Even when the research is aimed at studying the structural and functional characteristics of a phenomenon, the effective use of the method cannot be ruled out. Thus, the developers of the well-known theory of perceptual actions under microstructures In a new analysis of perception, they noted that “the genetic research method turned out to be the most suitable.” Naturally, the genetic method is especially characteristic of various branches of developmental psychology: comparative, developmental, historical psychology. It is clear that any longitudinal study presupposes the use of the method in question. The genetic approach can generally be considered as a methodological implementation of one of the basic principles of psychology, namely development principle. With this vision, other options for implementing the principle of development can be considered as modifications of the genetic approach. For example, historical And evolutionary approaches.

3.2. Structural method

3.3. Functional method

3.4. Complex method

3.5. System method

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1. Tutorial. St. Petersburg: Rech Publishing House, 2003. 480 p. BBC88

   Tutorial

   In the textbook, experimental psychology is considered as an independent scientific discipline that develops the theory and practice of psychological research and has a system of psychological methods as the main subject of study.

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   The first edition of this book was published in 1978 (G. M. Andreeva, N. N. Bogomolova, L. A. Petrovskaya “Social psychology in the West”). If we consider that at that time the “publishing path” was very long, it becomes clear that the manuscript

4. State exam program in pedagogy and psychology of education direction

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   The standard period for mastering the main educational program for master's training in the direction 050700.68 Pedagogy for full-time study is 6 years.

5. Psychology of the 21st century volume 2

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   Members of the Organizing Committee: Akopov G.V., Bazarov T.Yu., Zhuravlev A.L., Znakov V.V., Erina S.I., Kashapov S.M., Klyueva N.V., Lvov V.M. , Manuilov G.M., Marchenko V.

Data processing is aimed at solving the following tasks:

1) organizing the source material, transforming a set of data into a holistic system of information, on the basis of which further description and explanation of the object and subject being studied is possible;

2) detection and elimination of errors, shortcomings, and gaps in information; 3) identifying trends, patterns and connections hidden from direct perception; 4) discovery of new facts that were not expected and were not noticed during the empirical process; 5) determining the level of reliability, reliability and accuracy of the collected data and obtaining scientifically based results on their basis.

Data processing has quantitative and qualitative aspects. Quantitative processing there is a manipulation with the measured characteristics of the object (objects) being studied, with its properties “objectified” in external manifestation. High-quality processing- this is a method of preliminary penetration into the essence of an object by identifying its unmeasurable properties on the basis of quantitative data.

Quantitative processing is aimed mainly at a formal, external study of an object, while qualitative processing is mainly aimed at a meaningful, internal study of it. In quantitative research, the analytical component of cognition dominates, which is reflected in the names of quantitative methods for processing empirical material, which contain the category “analysis”: correlation analysis, factor analysis, etc. The main result of quantitative processing is an ordered set of “external” indicators of the object (objects) ). Quantitative processing is carried out using mathematical and statistical methods.

In qualitative processing, the synthetic component of cognition dominates, and in this synthesis the unification component prevails and the generalization component is present to a lesser extent. Generalization is the prerogative of the next stage of the research process - interpretive. In the phase of qualitative data processing, the main thing is not to reveal the essence of the phenomenon being studied, but for now only in the appropriate presentation of information about it, ensuring its further theoretical study. Typically, the result of qualitative processing is an integrated representation of the set of properties of an object or set of objects in the form of classifications and typologies. Qualitative processing largely appeals to the methods of logic.

The contrast between qualitative and quantitative processing (and, consequently, the corresponding methods) is rather arbitrary. They form an organic whole. Quantitative analysis without subsequent qualitative processing is meaningless, since by itself it is not able to transform empirical data into a system of knowledge. And a qualitative study of an object without basic quantitative data in scientific knowledge is unthinkable. Without quantitative data, qualitative knowledge is a purely speculative procedure, not characteristic of modern science. In philosophy, the categories “quality” and “quantity,” as is known, are combined into the category “measure.” The unity of quantitative and qualitative understanding of empirical material clearly appears in many methods of data processing: factor and taxonomic analysis, scaling, classification, etc. But since traditionally in science it is accepted to divide into quantitative and qualitative characteristics, quantitative and qualitative methods, quantitative and qualitative descriptions, let us accept quantitative and qualitative aspects of data processing as independent phases of one research stage, to which certain quantitative and qualitative methods correspond.

Quality processing naturally results in description And explanation phenomena being studied, which constitutes the next level of their study, carried out at the stage interpretations results. Quantitative processing refers entirely to the data processing stage.

Quantitative and qualitative data in experiment and other research methods.

Qualitative data– text, description in natural science language. Can be obtained through the use of qualitative methods (observation, survey, etc.)

Quantitative data– the next step in organizing qualitative data.

Distinguish between quantitative processing of results and measurement of variables.

Quality – eg. observation. The postulate of immediacy of observation data is the presentation of psychological reality to observation. The activity of the observer in organizing the observation process and the involvement of the observer in the interpretation of the facts obtained.

Different approaches to the essence of psychological measurement:

1. Presentation of the problem assigning numbers on a scale of a psychological variable for the purpose of ordering psychological objects and perceived psychological properties. Assumption that The properties of the measuring scale correspond to the empirically obtained measurement results . It is also assumed that the presented statistical criteria for data processing are adequate to researchers’ understanding of different types of scales , but the documents are lowered.

2. Goes back to the traditions of psychophysical experiment, where the measurement procedure has the ultimate goal of describing phenomenal properties in terms of changes in objective (stimulus_h-k. Merit of Stevens)

He introduced a distinction between types of scales:

names, order (fulfillment of the monotonicity condition, ranking is possible here), intervals (for example, IQ indicators, here the answer to the question “how much” is possible), ratios (here the answer to the question “how much”, absolute zero and units of measurement - psychophysics)

Thanks to this, psi measurement began to act not only as an establishment of quantitative psychophysical dependencies, but also in the broader context of measuring psi variables.

Qualitative description– 2 types: description in a natural language dictionary and development of systems of symbols, signs, units of observation. Categorical observation – reduction of units into categories – generalization. An example is Bales's standardized observation procedure for describing the interaction of small group members in solving a problem. Category system(in a narrow sense) – a set of categories that covers all theoretically permissible manifestations of the process being studied.

Quantitative assessment): 1) event-sampling– complete verbal description of behavioral events, their subsequent reading and psychological reconstruction. Narrow meaning of the term: the observer's precise temporal or frequency reflection of the "units" of description. 2) time-sampling– the observer records certain time intervals, i.e. determines the duration of events. Time sampling technique. Also specially developed for quantitative assessment subjective scales(Example: Sheldon, somatotype temperaments).