study design is a set of methods and procedures used to collect and analyze indicators of the variables specified in the study of the study task.

The study design defines the type of study (descriptive, remedial, semi-experimental, experimental, review or analytical purpose) and subtype (as in the case of a longitudinal descriptive study), research objective, hypothesis, independent and dependent variables, design plan for experimental and statistical analysis.

Research design is the structure that was created to find answers to research questions. The method chosen will affect the results and how the results are made.

There are two main types of research design: qualitative and quantitative. However, there are many ways to classify research projects. A study design is a set of conditions or collections.

There are many designs that are used in research, each with its own advantages and disadvantages. The choice of method to be used depends on the purpose of the study and on the nature of the phenomenon.

Key Features of Study Design

Parts of Study Design

Sample Design

This is due to the methods of selecting the elements to be observed for the study.

Observational Design

This is related to the state in which the observation will be created.

statistical design

He is concerned about how the information and data collected will be analyzed.?

Operational design

This is due to the methods by which procedures are collected when sampling.

How to design a study

The study plan describes how the study will be conducted; forms part of the research proposal.

Before creating a study design, you must first formulate the problem, the main question and additional questions. So the first thing to do is to identify the problem.

The study plan should be an overview of what will be used to conduct the project study.

It should describe where and when the study will be conducted, the sample to be used, the approach and methods to be used. This can be done by answering the following questions:

• Where? In what place or situation will the investigation be conducted?
• When? At what point in time or at what time will the investigation be conducted??
• Who or what? What kind of people, groups or events will be investigated (in other words, a sample)?
• How? What approaches and methods will be used to collect and analyze data?

example

The starting point of research design is the main research problem, which emerges from the approach to the problem. An example of a basic question might be:

What are the factors that make H&M online shoppers end up shopping in a brick and mortar store?

Answers to these questions:

where? On the main question, it is obvious that the research should focus on the H&M online store and possibly the traditional store.

when? The research should be carried out after the consumer has purchased the product in a traditional store. This is important as you are figuring out why someone is going down this path rather than buying a product online.

Who or what? In this case, it is clear that consumers who have made their purchase in a brick-and-mortar store should be considered. However, it may also be decided to examine consumers who, if they have made their purchase online, to compare different consumers.

How can you? This question is often difficult to answer. Among other things, you may need to consider the amount of time you have to conduct research and if you have a budget to collect information.

In this example, both qualitative and quantitative methods may be appropriate. Options may include interviews, surveys, and observations.

Various research projects

Structures can be flexible or fixed. In some cases, these types coincide with quantitative and qualitative research plans, although this is not always the case.

In fixed projects, the study design is already established before information is collected; they are usually guided by theory.

Flexible designs provide more freedom in the process of collecting information. One of the reasons why flex schemes can be used may be that the variable of interest cannot be quantified, such as culture. In other cases, the theory may not be available at the start of the investigation.

Exploratory research

Exploratory research methods are defined as formal research. The main methods are: literature survey and experience survey.

A literature-related survey is the simplest method of setting a research problem.

On the other hand, the experience survey is a method that looks for people who have had hands-on experience. The goal is to get new ideas related to the research problem.

In case of descriptive and diagnostic investigation

These are studies that concern the description of the characteristics of a person or group in particular. In a diagnostic study, we want to determine the frequency with which the same event will occur.

Research that tests hypotheses (experimental)

These are those in which the researcher tests the hypothesis of random relationships between variables.

Characteristics of good study design

A good research design should be relevant to this particular research problem; usually includes the following features:

• The way to get information.
• Availability and skills of the researcher and his team, if they exist.
• The purpose of the problem to be studied.
• The nature of the problem to be studied.
• Availability of time and money for research work.

links

1. Study design. Retrieved from wikipedia.org
2. Basic research. Retrieved from cirt.gcu.edu
3. Study design. Retrieved from explorable.com
4. How to create an exploratory design (2016). Retrieved from scribbr.com
5. Study design (2008). Retrieved from slideshare.net.

Experiment design (DOE , DOX or experimental design) is the development of some task that seeks to describe or explain the change in information under conditions that are hypothesized to reflect the change. The term is usually associated with experiments in which the design introduces conditions that directly affect change, but can also refer to the design of quasi-experiments in which natural conditions that affect change are chosen for observation.

In its simplest form, an experiment aims to predict outcomes by introducing a change in preconditions, which is represented by one or more independent variables, also referred to as "input variables" or "predictors." A change in one or more independent variables is usually hypothesized to result in a change in one or more dependent variables, also referred to as "output variables" or "response variables." The pilot design may also define control variables that should be kept constant to prevent external factors from influencing the results. Experimental design includes not only the selection of suitable independent, dependent, and control variables, but planning the delivery of the experiment under statistically optimal conditions, taking into account the limitations of available resources. There are several approaches for determining the set of design points (unique combinations of explanatory variable settings) to be used in an experiment.

Major concerns in development include action creation, reliability, and reproducibility. For example, these problems can be partly addressed by careful selection of the independent variables, reducing the risk of measurement error, and ensuring that the documentation of the methods is sufficiently detailed. Related challenges include achieving appropriate levels of statistical power and sensitivity.

Properly designed experiments advance knowledge in the natural and social sciences and engineering. Other applications include marketing and policy development.

story

Systematic clinical trials

In 1747, while serving as a surgeon on HMS Salisbury, James Lind conducted a systematic clinical trial to compare remedies for scurvy. This systematic clinical study is a type of ME.

Lind selected 12 people from the ship, all suffering from scurvy. Lind restricted his subjects to males who "looked like I could them", that is, he granted strict entry requirements to reduce outside change. He divided them into six pairs, giving each pair a different supplement to their basic diet for two weeks. The procedures were all means that were suggested:

• A quart of cider every day.
• Twenty-five Gutts (drops) of vitriol (sulfuric acid) three times a day on an empty stomach.
• One half pint of sea water each day.
• A mixture of garlic, mustard and horseradish in a nutmeg-sized lump.
• Two tablespoons of vinegar three times a day.
• Two oranges and one lemon every day.

Citrus treatments stopped after six days when they ran out of fruit, but by then one sailor was fit for duty and the others had nearly recovered. In addition, only one group (cider) showed some effect of his treatment. The rest of the crew presumably served as controls, but Lind did not report results from any control (untreated) group.

Statistical experiments, next C. Pierce

The theory of statistical inference was developed by Ch. Peirce in Illustrations to the Logic of Science (1877-1878) and The Theory of Probable Inferences (1883), two editions that emphasized the importance of randomization based on inference in statistics.

randomized experiments

C. Pierce randomized volunteers to a blind, repeated measurements design to assess their ability to distinguish between weights. Peirce's experiment inspired other researchers in psychology and education, who developed a research tradition of randomized experiments in laboratories and specialized textbooks in the 1800s.

Optimal Designs for Regression Models

comparison In some areas of study it is not possible to have independent measurements on a traceable metrological standard. Comparisons between treatments are far more valuable, and are generally preferred, and are often compared to scientific controls or traditional treatments that act as a baseline. Randomness Randomization is the process of assigning individuals to random groups or to different groups in an experiment so that each person in the population has the same chance of becoming a study participant. Random assignment of individuals into groups (or conditions within a group) distinguishes rigorous, "true" experiment from observational studies or "quasi-experiment". There is an extensive body of mathematical theory that explores the consequences of decisions to allocate units to treatments by some random mechanism (such as tables of random numbers, or the use of randomized devices such as playing cards or dice). The assignment of units to the treatment is random, usually to mitigate the puzzling effect that makes the effects due to factors other than treatment, presumably as a result of the treatment. Risks associated with random distribution (eg, having a major imbalance in a key characteristic between treatment and control groups) are calculable and therefore can be managed to an acceptable level using a sufficient number of experimental units. However, if the population is divided into several subpopulations that are in some way different, and the study requires that each subpopulation be equal in size, stratified sampling may be used. Thus, the units in each subpopulation are random, but not the entire sample. The results of an experiment can be reliably generalized from experimental units to a larger statistical population of units only if the experimental units are a random sample from a larger population; the likely error of such an extrapolation depends on the sample size, among other things. Statistical replication Measurements are generally subject to variation and measurement uncertainty; Therefore, they are repeated and complete experiments are replicated to help identify sources of variability, to better assess the true effects of treatment, to further enhance the experiment's reliability and validity, and to add to existing knowledge of the topic. However, certain conditions must be met before a replication experiment is initiated: the original research question has been published in a peer-reviewed journal or is widely cited, the researcher is independent of the original experiment, the researcher must first attempt to replicate the original data using the original data, and The review should indicate that the study conducted is a replication study that attempted to follow the original study as closely as possible. blocking Blocking is the non-random arrangement of experimental units into groups (blocks/lots) consisting of units that are similar to each other. Blocking reduces the known but irrelevant sources of inter-block variability and therefore provides greater accuracy in estimating the source of variation under study. Orthogonality Orthogonality concerns forms of comparison (contrast) that can be legitimately and effectively exercised. The contrasts can be represented by vectors and sets of orthogonal contrasts are uncorrelated and independently distributed if the data is normal. Because of this independence, each orthogonal processing provides different information to the others. If there is T- procedures and T- 1 orthogonal contrasts, all information that can be captured from the experiment can be obtained from the set of contrasts. Factorial experiments Use factorial experiments instead of a single factor-at-a-time method. They are effective in assessing the effects and possible interactions of several factors (independent variables). An experiment design analysis is built on the foundation of ANOVA, a collection of models, Partition of the observed variance into components, according to what factors, the experiment should evaluate or test.

example

This example is attributed to Hotelling. It conveys some of the flavor of these aspects of the theme, which involve combinatorial constructions.

The weights of eight objects are measured using balance panning and a set of standard weights. Each weighty measures the difference in weight between objects in the left pan versus any objects in the right pan, adding a calibrated weight for a lighter pan, until the balance is in balance. Each measurement has a random error. The average error is zero; on standard deviations according to the distribution of the probability of errors coincides with the number σ on different weightings; errors at different weighings are independent. Let us denote the true weights with

θ 1 , ... , θ 8 , (\displaystyle \theta _(1),\dots,\theta _(8).\)

We will consider two different experiments:

1. Weigh each object in one pan, with the other pan empty. Let X I be measured the weight of an object, I = 1, ..., 8.
2. There are eight weighings according to the following graph and let Y I difference to be measured I = 1, ..., 8:

left pan right pan First weighing: 1 2 3 4 5 6 7 8 (Empty) second: 1 2 3 8 4 5 6 7 third: 1 4 5 8 2 3 6 7 fourth: 1 6 7 8 2 3 4 5 fifth: 2 4 6 8 1 3 5 7 sixths: 2 5 7 8 1 3 4 6 sevenths: 3 4 7 8 1 2 5 6 eighths: 3 5 6 8 1 2 4 7 (\displaystyle (\ (begin array) (lcc) &(\text(left pan))&(\text(right pan))\\\hline(\text(1 weighting:))&1\2\3\4\5\6\7\8&(\ text((blank))) \\ (\ text(2)) & 1 \ 2 \ 3 \ 8 \ & 4 \ 5 \ 6 \ 7 \\ (\ text (3rd: )) & 1 \ 4 \ 5 \ 8 \ & 2 \ 3 \ 6 \ 7 \\ (\ text (4th :)) & 1 \ 6 \ 7 \ 8 \ & 2 \ 3 \ 4 \ 5 \\ (\ text (5th :)) : )) & 2 \ 4 \ 6 \ 8 \ & 1 \ 3 \ 5 \ 7 \\ (\text(6th:)) & 2 \ 5 \ 7 \ 8 \ & 1 \ 3 \ 4 \ 6 \ \ (\ text (7th: )) & 3 \ 4 \ 7 \ 8 \ & 1 \ 2 \ 5 \ 6 \\ (\ text (8th:)) & 3 \ 5 \ 6 \ 8 \ & 1 \ 2 \ 4 \ 7 \ end (array))) Then the calculated value of the weight θ 1 is θ ^ 1 = Y 1 + Y 2 + Y 3 + Y 4 - Y 5 - Y 6 - Y 7 - Y 8 8 , (\displaystyle (\widehat (\theta))_(1)=(\frac( Y_ (1) + Y_ (2) + Y_ (3) + Y_ (4) -Y_ (5) -Y_ (6) - Y_ (7) -Y_ (8)) (8)).) Similar estimates can be found for the weights of other items. For example θ ^ 2 = Y 1 + Y 2 - Y 3 - Y 4 + Y 5 + Y 6 - Y 7 - Y 8 8 , θ ^ 3 = Y 1 + Y 2 - Y 3 - Y 4 - Y 5 - Y 6 + Y 7 + Y 8 8 , θ ^ 4 = Y 1 - Y 2 + Y 3 - Y 4 + Y 5 - Y 6 + Y 7 - Y 8 8 , θ ^ 5 = Y 1 - Y 2 + Y 3 - Y 4 - Y 5 + Y 6 - Y 7 + Y 8 8 , θ ^ 6 = Y 1 - Y 2 - Y 3 + Y 4 + Y 5 - Y 6 - Y 7 + Y 8 8 , θ ^ 7 = Y 1 - Y 2 - Y 3 + Y 4 - Y 5 + Y 6 + Y 7 - Y 8 8 , θ ^ 8 = Y 1 + Y 2 + Y 3 + Y 4 + Y 5 + Y 6 + Y 7 + Y 8 8 , (\displaystyle (\(begin aligned)(\widehat (\theta)) _(2)=(&\frac(Y_(1)+Y_(2)-Y_(3 )-Y_(4)+(5 Y_)+Y_(6)-Y_(7)-Y_(8))(8)).\\(\widehat(\theta))_(3)&=(\ fracturing (Y_ (1) + Y_ (2) -Y_ (3) -Y_ (4) -Y_ (5) -Y_ (6) + Y_ (7) + (Y_ 8)) (8)).\\ ( \widehat(\theta))_(4)&=(\r hydraulic fracturing (Y_ (1) -Y_ (2) + Y_ (3) -Y_ (4) + Y_ (5) -Y_ (6) + Y_ (7) (-Y_ 8)) (8)). \\(\widehat(\theta))_(5)&=(\frac(Y_(1)-Y_(2)+Y_(3)-Y_(4)-Y_(5)+Y_(6)- Y_ (7) + (Y_ 8)) (8)). \\(\widehat(\theta))_(6)&=(\frac(Y_(1)-Y_(2)-Y_(3)+Y_(4)+Y_(5)-Y_(6)- Y_ (7) + (Y_ 8)) (8)) \\. (\widehat(\theta))_(7)&=(\frac(Y_(1)-Y_(2)-Y_(3)+Y_(4)-Y_(5)+Y_(6)+(7) Y_ ) -Y_ (8)) (8)). \\(\widehat(\theta))_(8)&=(\frac(Y_(1)+Y_(2)+Y_(3)+Y_(4)+Y_(5)+Y_(6)+ Y_ (7) + (Y_ 8)) (8)). \ (end justified)))

Experiment design question: Which experiment is best?

Estimation variance X 1 of & thetas 1 is σ 2 if we use the first experiment. But if we use the second experiment, the variance of the estimate given above is σ 2 /8. Thus, the second experiment gives us 8 times more than the accuracy for estimating one element, and evaluates all elements at the same time, with the same accuracy. What the second experiment achieves with eight would require 64 weighings if the items are weighed separately. However, we note that the estimates for the elements obtained in the second experiment have errors that correlate with each other.

Many experimental design problems involve combinatorial designs, as in this example and others.

To avoid false positives

False positives, often resulting from publication pressure or author's own confirmation bias, are an inherent danger in many areas. A good way to prevent skew that could potentially lead to false positives during the data collection phase is to use a double-blind design. When double-blind designs are used, participants are randomly assigned to experimental groups, but the researcher is unaware of which group the participants belong to. Thus, the researcher cannot influence the participants' response to the intervention. Experimental samples with undisclosed degrees of freedom are a problem. This can lead to conscious or unconscious "p-hacking": trying multiple things until you get the result you want. This typically involves manipulating - perhaps unconsciously - during statistical analysis and degrees of freedom until they return the figure below p<.05 уровня статистической значимости. Таким образом, дизайн эксперимента должен включать в себя четкое заявление, предлагающие анализы должны быть предприняты. P-взлом можно предотвратить с помощью preregistering исследований, в которых исследователи должны направить свой план анализа данных в журнал они хотят опубликовать свою статью прежде, чем они даже начать сбор данных, поэтому никаких манипуляций данных не возможно (https: // OSF .io). Другой способ предотвратить это берет двойного слепого дизайна в фазу данных анализа, где данные передаются в данном-аналитик, не связанный с исследованиями, которые взбираются данные таким образом, нет никакого способа узнать, какие участник принадлежат раньше они потенциально отняты, как недопустимые.

Clear and complete documentation of the experimental methodology is also important in order to support the replication of results.

Topics for discussion when creating development projects

A developmental or randomized clinical trial requires careful consideration of several factors before actually doing the experiment. Experimental design of the laying out of the detailed experimental plan in advance to do the experiment. Some of the following topics have already been discussed in the Experimental Design Principles section:

1. How many factors does design have, and are the levels of these factors fixed or random?
2. Are control conditions necessary, and what should they be?
3. Manipulation checks; did manipulation really work?
4. What are background variables?
5. What is the sample size. How many units must be collected for an experiment to be generalizable and have sufficient power?
6. What is the significance of the interaction between factors?
7. What is the influence of the long-term effects of the main factors on the results?
8. How do response changes affect self-report measures?
9. How realistic is the introduction of the same measuring devices into the same units, in different cases, with post-test and subsequent tests?
10. What about using a proxy pretest?
11. Are there lurking variables?
12. Should the client/patient, researcher, or even data analyst be conditionally blind?
13. What is the possibility of subsequently applying different conditions to the same unit?
14. How much of each control and noise factors should be taken into account?

The independent variable of a study often has many levels or different groups. In a true experiment, researchers can get an experimental group which is where their intervention is performed to test the hypothesis, and a control group which has all the same element in the experimental group without the intervention element. Thus, when everything else except for one intervention is kept constant, researchers can certify with some confidence that this one element is what is causing the observed change. In some cases, having a control group is not ethical. Sometimes this is solved by using two different experimental groups. In some cases, independent variables cannot be manipulated, such as when testing for a difference between two groups that have different diseases, or testing for a difference between men and women (obviously a variable that would be difficult or unethical to assign to a participant). In these cases, quasi-experimental design can be used.

causal attributions

In pure experimental design, the independent variable (the predictor) is manipulated by the researcher - that is - each participant in the study is selected at random from the population, and each participant is randomly assigned to the conditions of the independent variable. Only when this is done is it possible to verify with a high degree of probability that differences in outcome variables are caused by different conditions. Therefore, researchers should choose an experiment design over other design types whenever possible. However, the nature of the independent variable does not always allow for manipulation. In cases, researchers should be aware of not certifying causal attribution when their design does not allow it. For example, in observational projects, participants are not randomly assigned to conditions, and therefore, if there are differences found in the outcome variables between conditions, it is likely that there is something other than differences between conditions that cause differences in outcomes, which is the third variable. The same goes for studies with correlational design. (Ader & Mellenbergh, 2008).

Statistical control

It is best that the process is under reasonable statistical control prior to conducting the designed experiments. If this is not possible, proper blocking, replication, and randomization allow for the careful conduct of designed experiments. To control for interfering variables, the researcher establish control checks as additional measures. Researchers must ensure that uncontrolled influences (eg, perceptions of a source of trust) do not skew research results. The manipulation check is one example of a control check. Manipulation testing allows researchers to isolate key variables to reinforce support that these variables are working as intended.

Some effective designs for evaluating several main effects were found independently and in the near succession of Raja Chandra Bose and K. Kishen in 1940, but remained little known until the Plackett-Burmese designs were published in Biometrics in 1946. About the same time, CR Rao introduced the concept of orthogonal arrays as experimental samples. This concept was central to the development of Taguchi methods by Taguchi, who took place during his visit to the Indian Statistical Institute in the early 1950s. His methods were successfully applied and adopted by Japanese and Indian industry, and subsequently also adopted by American industry, albeit with some reservations.

In 1950 Gertrude Mary Cox and William Gemmell Cochran published the book Experimental Designs, which became the main reference work for the design of experiments on statisticians for many years thereafter.

The development of the theory of linear models has embraced and surpassed the cases that concerned the early authors. Today, theory relies on complex topics in

At the first stage, the design is carefully worked out (from the English. design- creative idea) of future research.

First of all, a research program is developed.

Program includes the topic, purpose and objectives of the study, formulated hypotheses, definition of the object of study, units and volume of observations, glossary of terms, description of statistical methods for forming a sample, collecting, storing, processing and analyzing data, methodology for conducting a pilot study, a list of statistical tools used .

Name Topics usually formulated in one sentence, which should correspond to the purpose of the study.

Purpose of the study- this is a mental anticipation of the result of an activity and ways to achieve it with the help of certain means. As a rule, the purpose of medical and social research is not only theoretical (cognitive), but also practical (applied) in nature.

To achieve this goal, determine research objectives, that reveal and detail the content of the goal.

The most important component of the program are hypotheses (Expected results). Hypotheses are formulated using specific statistical indicators. The main requirement for hypotheses is the ability to test them in the research process. The results of the study can confirm, correct or refute the hypotheses put forward.

Prior to the collection of material, the object and unit of observation are determined. Under object of medical and social research understand a statistical set consisting of relatively homogeneous individual objects or phenomena - units of observation.

Unit of observation- the primary element of the statistical population, endowed with all the features to be studied.

The next important operation in the preparation of the study is the development and approval of the work plan. If the research program is a kind of strategic plan that embodies the ideas of the researcher, then the work plan (as an annex to the program) is a mechanism for the implementation of the study. The work plan includes: the procedure for selecting, training and organizing the work of direct executors; development of regulatory and methodological documents; determination of the required volume and types of resource support for the study (personnel, finance, material and technical, information resources, etc.); definition of terms and responsible for separate stages of research. It is usually presented in the form network graphics.

At the first stage of medical and social research, it is determined by what methods the selection of units of observation will be carried out. Depending on the volume, continuous and selective studies are distinguished. In a continuous study, all units of the general population are studied, in a selective study, only a part of the general population (sample) is studied.

General population called a set of qualitatively homogeneous units of observation, united by one or a group of features.

Sample population (sample)- any subset of observation units of the general population.

The formation of a sample population that fully reflects the characteristics of the general population is the most important task of statistical research. All judgments about the general population based on sample data are valid only for representative samples, i.e. for such samples, the characteristics of which correspond to those of the general population.

The real provision of representativeness of the sample is guaranteed random selection method those. such a selection of units of observation in the sample, in which all objects in the general population have the same chances of being selected. To ensure random selection, specially developed algorithms are used that implement this principle, either tables of random numbers, or a random number generator available in many computer software packages. The essence of these methods is to randomly indicate the numbers of those objects that must be selected from the entire population in some way ordered. For example, the general population "regional population" can be sorted by age, place of residence, alphabetical order (last name, first name, patronymic), etc.

Along with random selection, when organizing and conducting medical and social research, the following methods of forming a sample are also used:

Mechanical (systematic) selection;

Typological (stratified) selection;

serial selection;

Multistage (screening) selection;

cohort method;

The "copy-pair" method.

Mechanical (systematic) selection allows you to form a sample using a mechanical approach to the selection of units of observation of an ordered general population. At the same time, it is necessary to determine the ratio of the volumes of the sample and the general population and thereby establish the proportion of selection. For example, in order to study the structure of hospitalized patients, a sample of 20% of all patients who left the hospital is formed. In this case, among all the "medical records of an inpatient" (f. 003 / y), ordered by numbers, every fifth card should be selected.

Typological (stratified) selection involves a breakdown of the general population into typological groups (strata). When conducting medical and social research, age-sex, social, professional groups, individual settlements, as well as urban and rural populations are taken as typological groups. In this case, the number of units of observation from each group is selected in the sample randomly or mechanically in proportion to the size of the group. For example, when studying the cause-and-effect relationships of risk factors and oncological morbidity in the population, the study group is first divided into subgroups by age, gender, profession, social status, and then the required number of observation units is selected from each subgroup.

serial selection the sample is formed not from individual units of observation, but from whole series or groups (municipalities, health care institutions, schools, kindergartens, etc.). The selection of series is carried out using proper random or mechanical sampling. Within each series, all units of observation are studied. This method can be used, for example, to evaluate the effectiveness of the immunization of the child population.

Multistage (screening) selection involves a phased sampling. By the number of stages, one-stage, two-stage, three-stage selection, etc. are distinguished. So, for example, when studying the reproductive health of women living in the territory of a municipality, at the first stage, working women are selected, who are examined using basic screening tests. At the second stage, a specialized examination of women with children is carried out, at the third stage, an in-depth specialized examination of women with children with congenital malformations. Note that in this case of purposeful selection for a certain attribute, the sample includes all objects - carriers of the studied attribute on the territory of the municipality.

cohort method are used to study the statistical population of relatively homogeneous groups of people united by the onset of a certain demographic event in the same time interval. For example, when studying issues related to the problem of fertility, a population (cohort) is formed that is homogeneous on the basis of a single date of birth (a study of fertility by generations) or on the basis of a single age of marriage (a study of fertility by length of family life).

Copy-Pair Method provides for the selection for each unit of observation of the group under study of an object that is similar in one or more features (“copy-pair”). For example, factors such as body weight and sex of the child are known to influence infant mortality rates. When using this method, for each death of a child under 1 year of age, a “copy-pair” of the same sex, similar in age and body weight, is selected from among living children under the age of 1 year. This method of selection should be used to study risk factors for the development of socially significant diseases, individual causes of death.

At the first stage, research is also developed (ready-made is used) and replicated statistical toolkit (cards, questionnaires, table layouts, computer programs for controlling incoming information, forming and processing information databases, etc.), into which the studied information will be entered.

In the study of public health and the activities of the health care system, sociological research is often used using special questionnaires (questionnaires). Questionnaires (questionnaires) for medical and sociological research should be targeted, oriented, ensure the reliability, reliability and representativeness of the data recorded in them. During the development of questionnaires and interview programs, the following rules must be observed: the suitability of the questionnaire for collecting, processing and extracting the necessary information from it; the possibility of reviewing the questionnaire (without violating the system of codes) in order to eliminate unsuccessful questions and make appropriate adjustments; explanation of the goals and objectives of the study; clear wording of questions, eliminating the need for various additional explanations; fixed nature of most questions.

Skillful selection and combination of various types of questions - open, closed and semi-closed - can significantly increase the accuracy, completeness and reliability of the information received.

The quality of the survey and its results largely depend on whether the basic requirements for the design of the questionnaire and its graphic design are met. There are the following basic rules for constructing a questionnaire:

The questionnaire includes only the most significant questions, the answers to which will help to obtain the information necessary to solve the main objectives of the study, which cannot be obtained in any other way without conducting a questionnaire survey;

The wording of the questions and all the words in them should be understandable to the respondent and correspond to his level of knowledge and education;

The questionnaire should not contain questions that cause unwillingness to answer them. It is necessary to strive to ensure that all questions cause a positive reaction of the respondent and a desire to give complete and true information;

The organization and sequence of questions should be subject to obtaining the most necessary information to achieve the goal and solve the problems set in the study.

Special questionnaires (questionnaires) are widely used, among other things, to assess the quality of life of patients with a particular disease, the effectiveness of their treatment. They allow capturing changes in the quality of life of patients that have occurred over a relatively short period of time (usually 2-4 weeks). There are many special questionnaires, such as AQLQ (Asthma Quality of Life Questionnaire) and AQ-20 (20-Item Asthma Questionnaire) for bronchial asthma, QLMI (Quality of Life after Myocardial Infarction Questionnaire) for patients with acute myocardial infarction, etc.

Coordination of work on the development of questionnaires and their adaptation to various linguistic and economic formations is carried out by an international non-profit organization for the study of the quality of life - the MAPI Institute (France).

Already at the first stage of the statistical study, it is necessary to draw up layouts of tables, which will be filled in the future with the data obtained.

In tables, as in grammatical sentences, the subject is distinguished, i.e. the main thing that is said in the table, and the predicate, i.e. that which characterizes the subject. Subject - this is the main feature of the phenomenon under study - it is usually located on the left along the horizontal lines of the table. Predicate - signs characterizing the subject are usually located on top of the vertical columns of the table.

When compiling tables, certain requirements are observed:

The table should have a clear, concise title that reflects its essence;

The design of the table ends with the totals for columns and rows;

The table should not contain empty cells (if there is no sign, put a dash).

There are simple, group and combination (complex) types of tables.

A simple table is a table that presents a summary of data for only one attribute (Table 1.1).

Table 1.1. Simple table layout. Distribution of children by health groups, % of total

In the group table, the subject is characterized by several unrelated predicates (Table 1.2).

Table 1.2. Group table layout. Distribution of children by health groups, sex and age, % of the total

In the combination table, the signs characterizing the subject are interconnected (Table 1.3).

Table 1.3. Combination table layout. Distribution of children by health groups, age and gender, % of total

An important place in the preparatory period is occupied by pilot Study, whose task is to test statistical tools, to verify the correctness of the developed methodology for collecting and processing data. The most successful is such a pilot study, which repeats on a reduced scale the main one, i.e. makes it possible to check all the upcoming stages of work. Depending on the results of the preliminary analysis of the data obtained during piloting, the statistical tools, methods of collecting and processing information are adjusted.

The review tells about the methodical book of the American professor D. Morgan. It tells in detail about the strategies for integrating qualitative and quantitative methods, options for research designs.

Strekalova N. D. Corporate governance and innovative development of the economy of the North: Bulletin of the Research Center for Corporate Law, Management and Venture Investment of Syktyvkar State University. 2014. No. 4. S. 184-197.

The article reveals the essence of the case method as a research strategy, the basics of methodology and research design in management. The strengths and weaknesses of using the case method in conducting scientific research of masters of management are considered. Comparative characteristics of research and educational cases are given. The experience of organizing research is highlighted, the problems, opportunities and prospects for using the case method in the formation of research competencies of masters of management are discussed. In conclusion, practical recommendations are given on the organization of scientific research of masters of management based on the case method. The article contributes to the comparative analysis of teaching and research cases, description of the research methodology and design, identification of the strengths and weaknesses of the case study as a research method.

The paper discusses methodological solutions to the problem characteristic of European Union studies " n= 1" - the problem of the uniqueness of the EU, leading, it would seem, to the impossibility of conducting comparative studies. However, the penetration of comparative politics into European studies and the study of the EU within the framework of the new regionalism led to a surge in the number of articles using the comparative method. An analysis of four scientific journals shows that this trend is typical for English-language journals, but not for Russian journals.

Savinskaya O. B. In: Sociology and Society: Social Inequality and Social Justice (Yekaterinburg, October 19-21, 2016). Materials of the V All-Russian Sociological Congress. M.: Russian Society of Sociologists, 2016. S. 8467-8475.

This work is a methodological reflection of the current discussions about the formation of a new methodological approach - the strategy of mixing methods (mixed methods research), which involves the combination of qualitative and quantitative methods of collecting and analyzing data for a thorough study of a social phenomenon. The report discusses the main steps in the development of the method blending strategy (MMR), discusses the Russian translation of the term, and current classifications of research designs for multimethod studies. Achievements and outstanding issues are indicated in the last part of the article.

Theoretical Validation in Sociological Research: Methodology and Methods

In the social sciences, there are various types of research and, accordingly, opportunities for the researcher. Knowing about them will help you solve the most difficult problems.

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Research Strategies
In the social sciences, it is customary to single out the two most common research strategies - quantitative and qualitative.
Quantitative strategy involves using a deductive approach to test hypotheses or theories, relies on the positivist approach of the natural sciences, and is inherently objectivist. A qualitative strategy, on the other hand, focuses on an inductive approach for developing theories, rejects positivism, focuses on an individual interpretation of social reality, and is constructivist in nature.
Each of the strategies involves the use of specific data collection and analysis methods. The quantitative strategy is based on the collection of numerical data (mass survey data encodings, aggregated test data, etc.) and the use of mathematical statistics methods for their analysis. In turn, a qualitative strategy is based on the collection of textual data (texts of individual interviews, participant observation data, etc.) and their further structuring through special analytical techniques.
Since the beginning of the 90s, a mixed strategy began to actively develop, which consists in integrating the principles, methods of collecting and analyzing data from qualitative and quantitative strategies in order to obtain more reasonable and reliable results.

Research designs
Once the purpose of the study has been determined, the appropriate type of design must be determined. Study design is the combination of data collection and analysis requirements necessary to achieve the study objectives.
Main types of design:
Cross-sectional design involves collecting data on a relatively large number of observation units. As a rule, it involves the use of a sampling method in order to represent the general population. The data is collected once and is quantitative. Further, descriptive and correlation characteristics are calculated, statistical conclusions are drawn.
Longitudinal design consists of repeated cross-sectional interviews to establish changes over time. It is divided into panel studies (the same people take part in repeated surveys) and cohort studies (different groups of people who represent the same general population take part in repeated surveys).
Experimental design involves identifying the influence of the independent variable on the dependent variable by leveling the threats that may affect the nature of the change in the dependent variable.
The design of a case study is intended to study one or a small number of cases in detail. The emphasis is not on the distribution of the results to the entire general population, but on the quality of theoretical analysis and explanation of the mechanism of functioning of a particular phenomenon.

Research goals
Among the goals of social research are description, explanation, evaluation, comparison, analysis of relationships, the study of cause-and-effect relationships.
Descriptive tasks are solved by simply collecting data using one of the methods that are appropriate in a given situation - questionnaires, observations, document analysis, etc. One of the main tasks in this case is such a fixation of data, which in the future will allow their aggregation.
To solve explanatory problems, a number of research approaches are used (for example, historical research, case studies, experiments), which allow dealing with the analysis of complex data. Their goal is not only a simple collection of facts, but also the identification of the meanings of a large set of social, political, cultural elements associated with the problem.
The general purpose of evaluation studies is to test programs or projects in terms of awareness, effectiveness, achievement of objectives, etc. The results obtained are usually used to improve them, and sometimes simply to better understand the functioning of the programs and projects concerned.

Comparative studies are used for a deeper understanding of the phenomenon under study by identifying its common and distinctive features in various social groups. The largest of them are held in cross-cultural and cross-national contexts.
Studies to establish relationships between variables are also called correlation studies. The result of such studies is the receipt of specific descriptive information (for example, see about the analysis of pairwise relationships). This is fundamentally quantitative research.
Establishing cause-and-effect relationships involves conducting experimental studies. In the social and behavioral sciences, there are several varieties of this kind of research: randomized experiments, true experiments (involving the creation of special experimental conditions that simulate the necessary conditions), sociometry (of course, as J. Moreno understood it), Garfinkeling.