The concepts of quantitative and qualitative methods in psychology

Defining methods as ways of cognition, S.L. Rubinstein noted that the methodology should be conscious and not turn into a form mechanically imposed on the specific content of science. Consider the question of how cognizant paths in psychology are and how researchers understand and define quantitative and qualitative methods.

As the main psychological methods S.L. Rubinstein in " Fundamentals of General Psychology" names observation, experiment, methods of studying the products of activity. This list does not include quantitative methods.

In the 1970s, the second classification of methods of psychological research, created by B.G. Ananiev.

He distinguishes the following groups of methods:

1. Organizational;
2. empirical;
3. Data processing methods;
4. Interpretation methods.

Quantitative and qualitative methods were classified as data processing methods. He defines quantitative methods as mathematical and statistical methods of processing psychological information, and qualitative methods are a description of those cases that most fully reflect the types and variants of mental phenomena and are an exception to the general rules.

Classification B.G. Ananiev was criticized by the representative of the Yaroslavl school V.N. Druzhinin, offering his own classification.

By analogy with other sciences, he distinguishes three classes of methods in psychology:

1. empirical;
2. Theoretical;
3. Interpretive.

Qualitative and quantitative methods are also not specified separately in the classification, but it is assumed that they are placed in the empirical methods section, which differs from the classification of B.G. Ananiev. Significantly supplemented the classification of B.G. Ananyeva, a representative of the Leningrad school of psychologists V.V. Nikandrov. He classifies quantitative and qualitative methods as non-empirical methods in accordance with the criterion of "staged psychological process". The author understands non-empirical methods as “research methods of psychological work outside the contact of the researcher and the individual.

In addition to the remaining differences in the classifications of S.L. Rubinstein and B.G. Ananiev, there are terminological discrepancies in the understanding of quantitative and qualitative methods.

An exact definition of these methods is not given in the works of V.V. Nikandrov. He defines qualitative methods functionally, from the point of view of the result, and calls them:

1. Classification;
2. Typology;
3. Systematization;
4. periodization;
5. Psychological casuistry.

He replaces the quantitative method with the definition of quantitative processing, which is mainly aimed at a formal, external study of the object. As synonyms for V.V. Nikandrov uses such expressions as quantitative methods, quantitative processing, quantitative research. The author refers to the main quantitative methods methods of primary and secondary processing.

Thus, the problem of terminological inaccuracy is quite relevant and takes on a new meaning when researchers seek to attribute quantitative methods to the new scientific sections "Psychometry" and "Mathematical Psychology".

Reasons for terminological discrepancies

There are a number of reasons why there is no strict definition of quantitative and qualitative methods in psychology:

• Quantitative methods within the framework of the domestic tradition have not received an unambiguously strict definition and classification, and this speaks of methodological pluralism;
• Quantitative and qualitative methods in the tradition of the Leningrad school are considered as a non-empirical stage of research. The Moscow school interprets these methods as empirical and elevates them to the status of a methodological approach;
• In the terminological confusion of the concepts of quantitative, formal, quantative, mathematical and statistical, there is a conventionalism that has developed in the psychological society regarding the definition of these quantitative and qualitative methods;
• Borrowing from the American tradition of dividing all methods into quantitative and qualitative methods. Quantitative methods, more precisely research, involve the expression and measurement of results in quantitative terms. Qualitative methods are seen as "humanitarian" research;
• The definition of an unambiguous place and the ratio of quantitative and qualitative methods most likely leads to the fact that quantitative methods are subordinate to qualitative methods;
• The modern theory of method moves away from the classification of methods only on one basis and a strict definition of the procedure of the method. Methodologists distinguish three directions in the theory:
  1. Improvement of the traditional empirical model;
  2. Criticism of the empirical quantitative model;
  3. Analysis and testing of alternative research models.
• Different directions in the development of the theory of method reveal a tendency for researchers to gravitate toward qualitative methods.

Quantitative Methods

The purpose of practical psychology is not to establish patterns, but to understand and describe problems, so it uses both qualitative and quantitative methods.

Quantitative methods are techniques for processing digital information, because they are mathematical in nature. Quantitative methods such as categorized observation, testing, document analysis, and even experiment provide information to diagnose a problem. The efficiency of work is determined at the final stage. The main part of the work - conversations, trainings, games, discussions - is carried out using qualitative methods. Of the quantitative methods, testing is the most popular.

Quantitative methods are widely used in scientific research and in the social sciences, for example, in testing statistical hypotheses. Quantitative methods are used to process the results of mass public opinion polls. To create tests, psychologists use the apparatus of mathematical statistics.

Methods of quantitative analysis are divided into two groups:

1. Methods of statistical description. As a rule, they are aimed at obtaining quantitative characteristics;
2. Methods of statistical inference. They make it possible to correctly extend the obtained results to the entire phenomenon, to draw a conclusion of a general nature.

With the help of quantitative methods, stable trends are identified and their explanations are built.

The disadvantages of the quantitative control method are related to its limitations. These methods of assessing knowledge in the field of teaching psychology can only be used for intermediate control, checking knowledge of terminology, textbook experimental research or theoretical concepts.

Qualitative Methods

Increased interest and popularity, qualitative methods are gaining only recently, which is associated with the demands of practice. In applied psychology, the scope of qualitative methods is very wide:

• Social psychology carries out humanitarian expertise of social programs - pension reform, reform of education, health care - using qualitative methods;
• Political psychology. Qualitative methods are necessary here to build an adequate and effective election campaign, to form a positive image of politicians, parties, and the entire system of public administration. Important here will be not only quantitative indicators of the trust rating, but also the reasons for this rating, ways to change it, etc.
• With the help of qualitative methods, the psychology of the mass media explores the degree of trust in one or another print publication, specific journalists, and programs.

The decisive role in the development of qualitative methods in psychology, therefore, was played by the need for a dialogue between psychological science and various fields of practical activity.

Qualitative methods are focused on the analysis of information, which is mainly presented in verbal form, so there is a need to compress this verbal information, i.e. obtain it in a more compact form. In this case, coding acts as the main compression technique.

Coding involves the selection of semantic segments of the text, their categorization and reorganization.

Examples of information compression are schemes, tables, diagrams. Thus, coding and visual representation of information are the main methods of qualitative analysis.

To conduct a quantitative analysis of the models, we will use the following indicators:

1. The number of blocks on the diagram is N;

2. The level of decomposition of the diagram - L;

3. Chart balance – B;

4. The number of arrows connected to the block - A.

This set of indicators refers to each diagram in the model, then using the coefficients (formula 1, 2), which can be used to determine the quantitative characteristics of the model as a whole. To increase the understandability of the model, it is necessary to strive to ensure that the number of blocks (N) on the lower-level diagrams is less than the number of blocks on the parent diagrams, that is, with an increase in the decomposition level (L), the decomposition coefficient d decreases: d = N / L

Thus, a decrease in this coefficient indicates that as the model is decomposed, the functions should be simplified, therefore, the number of blocks should decrease.

Charts must be balanced. This means that the number of arrows entering and leaving the block should be equally distributed, i.e. the number of arrows should not vary much. It should be noted that this recommendation may not be followed for processes that involve obtaining a finished product from a large number of components (production of a machine assembly, production of a food product, and others). The chart balance factor is calculated using the following formula:

It is desirable that the balance factor be minimal for the diagram, and constant in the model.

In addition to assessing the quality of the diagrams in the model and the model itself in general by the balance and decomposition coefficients, it is possible to analyze and optimize the described processes. The physical meaning of the balance coefficient is determined by the number of arrows connected to the block, and, accordingly, it can be interpreted as an estimated coefficient for the amount of processed and received information. Thus, on the graphs of the dependence of the balance coefficient on the level of decomposition, the existing peaks relative to the average value show the overload and underload of the information system subsystems in the enterprise, since different levels of decomposition describe the activities of various subsystems. Accordingly, if there are peaks on the graphs, then a number of recommendations can be made for optimizing the described processes automated by the information system.

Analysis of the context diagram "A-0 Information system of a construction organization"

Number of blocks: 1

Chart decomposition level: 3

Balance factor: 3

Number of arrows connected to the block: 11

Detailed analysis of the process "A2" Module "Suppliers"

Number of blocks: 4

Detailed analysis of the process "A3" Module "Objects"

Number of blocks: 3

Chart decomposition level: 2

Balance factor: 5.75

Detailed analysis of the process "A1" Module "Workers"

Number of blocks: 3

Chart decomposition level: 2

Balance factor: 5.75

Analysis of the process details "A 4.1 Module "Reports"

Number of blocks: 3

Chart decomposition level: 2

Balance factor: 5.75

Analysis of the detailing of the process "A 5" Module "Contractors"

Number of blocks: 3

Chart decomposition level: 2

Balance factor: 5.75

The balance factor at the child decomposition levels for the child process levels The store information system indicates that the diagram is balanced. Because the balance coefficient is not equal to zero, then it is possible to carry out further decomposition of some levels, after which it is possible to analyze the names of the activities of this model.

When carrying out a quantitative analysis of the model, a graph of the decomposition coefficient was constructed, in which we see that with an increase in the level of decomposition, the decomposition coefficient decreases. Thus, the decrease in this coefficient indicates that as the model is decomposed, the functions are simplified, therefore, the number of blocks decreases. The graph of the decomposition coefficient is shown in Figure 10.

Figure 10 - Graph of the decomposition coefficient

On the graph of the dependence of the balance factor on the level of decomposition, the existing peaks relative to the average value show the overload of the information system subsystems for the enterprise, the balance factor for the diagram is maximum. The graph of the balance coefficient is shown in Figure 11.

Figure 11 - Graph of the balance factor

Fundamentals of Quantitative Analysis

Quantitative analysis of the financial market is forecasting prices and profitability of financial assets, assessing the risks of investing in financial assets using mathematical and statistical methods of time series analysis.

At first glance, quantitative analysis resembles technical analysis, since both of these types of analysis use historical data on the price of a financial asset and historical data on other characteristics of a financial asset. But there is a significant difference between technical analysis and quantitative analysis.

Technical analysis is based on empirically found patterns. And these patterns do not have a strict scientific justification.

While the methods of quantitative analysis have a strict mathematical justification. Many of the methods of quantitative analysis are successfully applied in such sciences as physics, biology, astronomy, etc.

Basic ideology of quantitative analysis

The basic ideology of quantitative analysis is very similar to the approach that is practiced in the natural sciences.

In quantitative analysis, some hypothesis is first put forward about the functioning of the financial market. Based on this hypothesis, a mathematical model is built. This model should capture the main idea of ​​the proposed hypothesis and discard irrelevant random details.

Then, with the help of mathematical methods, this model is studied. The most important thing in such a study is to make a forecast of the prices of financial assets. Such a forecast can be made both for the current moment of time and for historical moments of time. Then there is a comparison of the forecast with the real price chart.

Basic quantification model

The most important quantitative analysis model is the Efficient Financial Market model, which is formed on the basis of the Efficient Market Hypothesis.

In quantitative analysis, an efficient market is such a situation when all participants in the financial market at any given time have access to all information related to the financial market. This means that all market participants not only always have all the information, but also have the same identical information. It does not happen that one of the market participants has some additional insider information that would be inaccessible to other market participants.

Under such conditions, all prices of all financial assets are always at their equilibrium values. That is, the price of any financial asset in an efficient market is always equal to the price at which supply and demand are equal to each other. In an efficient market, there is no such thing as any financial asset being overvalued or undervalued.

An efficient market leads to the fact that as soon as traders have some new information, prices immediately change immediately, reacting to the emergence of new information. Thus, prices are always in equilibrium, no matter how they change.

Therefore, from the point of view of quantitative analysis, it is impossible to earn in an efficient market, as in a real market, when investors buy undervalued assets and sell overvalued assets. Also, in an efficient market, there are never market bubbles when the price moves opposite from its equilibrium value.

Quantitative analysis states that in an efficient market, the price of a financial asset changes randomly so that the most likely price at the next point in time will be the current price. And prices different from the current price will be less likely. Such a random process is called a martingale. (Do not confuse martingale and martingale. Martingale is one of the money management strategies. In French, both of these words are homonyms, that is, they are written the same way "martingale", but have different meanings.)

This means that it is impossible to speculate on financial assets in the efficient market in the short term. The only way to make money in such a market is to buy securities for long-term holding. This is a "buy and hold" strategy.

Violation of the basic model of quantitative analysis

If the efficient market hypothesis is violated, the prices of financial assets will deviate from their equilibrium values. Therefore, depending on one or another hypothesis of efficient market disruption in quantitative analysis, it is possible to build such mathematical models that allow you to earn on the difference between real and equilibrium prices.

Specific hypotheses of deviation from the basic model often do not have a rigorous scientific justification in quantitative analysis. These hypotheses of deviation from the base model lead to different mathematical models of the financial market. And, accordingly, these mathematical models can lead to completely different forecasts of financial asset prices.

Therefore, depending on which hypothesis of deviation from the basic model in quantitative analysis is accepted by financial market participants, they begin to adhere to one or another model of their behavior in the market. In this regard, the task of testing the market for its efficiency, how much the market differs from the efficient market, becomes very relevant.

This problem in quantitative analysis is solved using the methods of statistical testing of hypotheses, which underlie the efficient market. Such verification is possible if there is an adequate model that determines the profitability of financial assets under the condition of market equilibrium.

Quantitative analysis and psychology

Based on the foregoing, it becomes clear that in the financial markets there is also a connection between quantitative analysis and the psychology of traders and investors, as was the case for technical analysis and fundamental analysis. The market prices of a financial asset can change in one direction or another, depending on which hypothesis of deviation from the base model is accepted by the proponents of quantitative analysis, who own the largest amount of funds involved in this market.

Quantitative Time Series Analysis

Quantitative analysis of time series is associated with great mathematical difficulties. These difficulties are related to the statistical non-stationarity of the price behavior of many exchange assets.

When studying time series, it is usually considered that the time series of changes in the prices of a financial asset is the sum of some dynamic component and a random component. The dynamic component depends on the fundamental economic laws, according to which the price should change. And the random term is associated with some non-economic factors, for example, with the emotional behavior of traders, with the release of some force majeure news, etc.

The task of quantitative analysis is to identify this dynamic component and filter out random noise. The identified dynamic component can be extrapolated into the future. This extrapolation gives the average value of the forecast price. And the filtered random noise makes it possible to estimate the statistical moments of a higher order. This is primarily a second-order statistical moment, that is, dispersion, which is associated with volatility. Knowing the dispersion and volatility allows you to assess the risks.

Such a time series analysis scheme is used, for example, when searching for signals from extraterrestrial civilizations among space radio noise. This is exactly the task when we are completely unaware of the dynamic signal that we are looking for.

But the quantitative analysis of the time series of exchange prices has an order of magnitude more difficult task. After all, extraterrestrial civilizations, knowing the statistical and spectral characteristics of cosmic radio noise, will try to send such signals to the Universe that will be statistically and spectrally as different as possible from cosmic noise. They will do this on purpose to make it easier for other civilizations to find and recognize their signals.

And the financial market is not such a rational being. Therefore, for price time series there is no such clear separability of these series into dynamic and random components. Therefore, many mathematical methods for signal filtering in quantitative analysis simply do not work.

In fact, the time series of stock prices are the sum of several series. The first of these series is a purely dynamic series. The last series in this sum is a purely random series with a zero autocorrelation function. And the intermediate terms are intermediate series, in which the autocorrelation function vanishes after a while. And we have a whole range of zeroing times for the autocorrelation function.

Conclusion

In the field of economics and finance, statistical models and methods are called econometric. On the one hand, quantitative analysis of the financial market based on econometric models and methods is a development of traditional fundamental analysis in the area of ​​market uncertainty. And, on the other hand, quantitative analysis makes an attempt to more strictly substantiate the methods of researching historical data. This may further lead to a closer connection between quantitative and technical analysis.

Qualitative and quantitative methods are a tool for a certain work with data, their recording and subsequent analysis.

Qualitative Methods are aimed at collecting qualitative data and their subsequent qualitative analysis using appropriate techniques and techniques for extracting meaning; quantitative methods are a tool for collecting numerical data and their subsequent quantitative analysis using the methods of mathematical statistics (Fig. 3.1).

Rice. 3.1.

Accordingly, qualitative research can be defined as research that predominantly uses qualitative methods, while quantitative research can be defined as research built on the predominant use of quantitative methods.

It seems obvious to define the type of study by the corresponding type of methods. However, not all authors define qualitative and quantitative research in this way, and in the methodological literature one can find different interpretations of them. Indeed, a number of authors (see, for example: Semenova, 1998; Strauss, Corbin, 2007) characterize qualitative studies as those in which non-quantitative data collection methods are used, and data analysis is carried out using various qualitative interpretive procedures, without involving calculations and methods. mathematical statistics. In other manuals devoted to qualitative research (the most famous among them: Handbook of Qualitative Research..., 2008), along with exclusively qualitative (phenomenological, discourse-analytical, narrative, psychoanalytic) methods, the so-called Q-methodology is analyzed, in which collection of numerical data and their quantitative analysis. Q-methodology is usually contrasted with "R-methodology". The R-methodology uses objective indicators of tests, questionnaires, rating scales, which reflect the constructs created by the researcher himself - it is these objective indicators that are subjected to the mathematical processing procedure in the R-methodology (for example, using factor analysis procedures). Q-methodology, in turn, is aimed at obtaining subjective data. It is based on the Q-sorting procedure: the subjects are asked to sort a certain set of statements (as a rule, obtained from themselves as a result of a special survey or interview procedure), distributing these statements along a pre-organized continuum specified by some scale. The subjects sort the statements according to their own subjective evaluation, and further the matrix of these subjective evaluations is processed by multivariate statistics methods. As already mentioned, Q-methodology procedures are included in qualitative research manuals, despite the fact that they involve obtaining quantitative data and applying statistical methods. The authors believe that the Q-methodology is one of the possible alternatives to the main "objective" psychological research, and since it is the direction of qualitative research that embodies the spirit of cognitive alternatives, the Q-methodology based on quantitative methods is discussed in the context of qualitative research.

As can be seen, the interpretation of qualitative and quantitative research is not always strictly tied to the types of methods used in research. Very often, the peculiarities of the organization of research serve as a constitutive sign of the separation of qualitative and quantitative research. The problem of distinguishing different types of studies from the point of view of their organization will be considered in the next paragraph. To avoid confusion here, we propose to dwell on this at the beginning of the paragraph. methodical definition of qualitative and quantitative research as built on the predominant application of a certain type of methods. Qualitative research mainly deals with qualitative data and qualitative methods of their analysis, quantitative research - with quantitative data and their quantitative analysis.