This documentary research work is about helping the researcher to know how the research should be structured, taking into account that the first thing is to select a topic, and that the problem is current, and thus give it a solution, since all problems appear as a result of a difficulty, which originates from a need.
Documentary research must include a statement of the problem, experimental research, variables, hypotheses, its methodology, statistics, measures of central tendency, measures of dispersion, tabulation and graphs.
To carry out an investigation, the statement of the problem must be clearly understood, to later carry out the hypothesis, which will be in charge of shaping the investigation, since it will lead us to elaborate the objective to be achieved, the design of feasible investigation of the problem, the selection of the method, the instruments, the techniques and the selection of the resources, both human and material, with which the investigation will be carried out.
Therefore, in order to carry out the investigation, it is necessary to know if someone had already investigated regarding the problem, so that from there the investigation can be resumed and a solution to the problem is given.
To carry out an investigation, the researcher must know the meaning of each concept of the steps to follow in a documentary investigation, for which we present the definition of each one of them.
THE THEORETICAL FRAMEWORK OF AN INVESTIGATION
Carlos Sabino (1996), affirms that «the approach of an investigation cannot be carried out if what we propose to know is not made explicit: it is always necessary to distinguish between what is known and what is not known regarding a topic to define clearly the problem to be investigated ”. The correct approach to a research problem allows us to define its general and specific objectives, as well as the limitation of the object of study.
The author adds that no fact or phenomenon of reality can be approached without an adequate conceptualization.
The researcher who poses a problem does not do it in a vacuum, as if he had no idea of it, but always starts from some previous ideas or information, from some theoretical and conceptual references, even though they do not yet have it. a precise and systematic character.
The theoretical framework, referential framework or conceptual framework has the purpose of giving the research a coordinated and coherent system of concepts and propositions that allow addressing the problem. "It is a matter of integrating the problem within an area where it makes sense, incorporating previous knowledge related to it and ordering it in such a way that it is useful to our task."
The purpose of the theoretical framework is to place our problem within a set of knowledge, which allows us to guide our search and offer us an adequate conceptualization of the terms that we will use.
"The starting point to build a frame of reference is our prior knowledge of the phenomena that we address, as well as the lessons that we extract from the work of bibliographic review that we will necessarily have to do."
The theoretical framework answers the question: what antecedents exist? Therefore, its objective is to give research a coordinated and coherent system of concepts, propositions and postulates, which allows obtaining a complete vision of the theoretical system and of the scientific knowledge that one has about the subject.
Ezequiel Ander-Egg tells us that in the theoretical or referential framework «general theoretical propositions, specific theories, postulates, assumptions, categories and concepts that have to serve as reference to order the mass of the facts concerning the problem are expressed or problems that are the subject of study and research. In this sense, «every theoretical framework is elaborated from a larger theoretical body, or directly from a theory. For this task, it is assumed that a review of the existing literature on the research topic has been carried out. But with the sole consultation of existing references, a theoretical framework is not elaborated: this could become an eclectic mix of different theoretical perspectives, in some cases, even opposed.The theoretical framework that we use is derived from what we can call our a priori options, that is, from the theory from which we interpret reality.
Roberto Hernández Sampieri and others highlight the following functions that the theoretical framework fulfills within an investigation:
- Helps prevent errors that have been made in other studies Guidance on how the study will be carried out (by going to the background, we can see how a specific research problem has been treated, what types of studies have been carried out, with what type of subjects, how the data have been collected, in which places they have been carried out, what designs have been used) It broadens the horizon of the study and guides the researcher to focus on their problem, avoiding deviations from the approach It leads to the establishment of hypotheses or statements that will later have to be tested in reality.It inspires new lines and areas of research.It provides a frame of reference to interpret the results of the study.
In general, it could be stated that the theoretical framework also has the following functions:
- Orient towards the organization of data and significant facts to discover the relationships of a problem with existing theories.
- Prevent the researcher from addressing issues that, given the state of knowledge, have already been investigated or lack scientific importance.
- Guide in the selection of the factors and variables that will be studied in the research, as well as their measurement strategies, their validity and reliability.
- Prevent possible confounding factors or strange variables that could potentially generate unwanted biases.
- Guide the search and interpretation of data
1.1 THE ELABORATION OF THE THEORETICAL FRAMEWORK
The development of the theoretical framework generally comprises two stages:
- Review of existing literature. It consists of highlighting, obtaining and consulting the bibliography and other materials that may be useful for study purposes, from which the relevant and necessary information that concerns our research problem must be extracted and collected.
- Adoption of a theory or development of a theoretical perspective. In this aspect, we can find different situations:
- That there is a fully developed theory, with abundant empirical evidence, and that it applies to our research problem. In this case, the best strategy is to take that theory as the very structure of the theoretical framework, that there are several theories that apply to our research problem. In this case, we can choose one and base ourselves on it to build the theoretical framework or take parts of some or all of the theories, as long as they are related to the study problem.
III. That there are "pieces or chunks" of theory with moderate or limited empirical support, suggesting important variables, applicable to our research problem. In this case, it is necessary to build a theoretical perspective.
- That there are only guides not yet studied and ideas vaguely related to the research problem. In this case, the researcher has to look for literature that, although it does not refer to the specific problem of the research, helps him or her orient himself within it.
Once the pertinent readings have been carried out, we will be in a position to elaborate our theoretical framework, which will be based on the integration of the information collected.
The order that the integration will take will be determined by the objective of the theoretical framework. If, for example, it is of a historical nature, it is advisable to establish a chronological order of the theories and / or of the empirical findings. If the research is related to a series of variables and we have information from theory, as well as from previous studies of each of these variables and the relationship between them, it would be convenient to define sections that cover each of the relevant aspects, in order to integrate the data pertinent to our study.
In any case, it is essential in any investigation that the author incorporates his own ideas, criticisms or conclusions regarding both the problem and the material collected. It is also important that the most outstanding questions are related, going from the general to the concrete, that is, first mentioning generalities of the subject, until we reach what is specifically related to our research.
1.2 STATEMENT OF THE PROBLEM
It is generally one of the shortest stages of an investigation; however, on occasions, it can become the longest stage of the process, due to causes such as lack of information, poor vision, lack of communication, etc.
The question is the pattern that suggests the sense of search; The actions, means, resources, techniques or procedures involved will be convenient to the extent that they favor providing the data that allow the response to be shaped.
The question can express several ideas, so the following points should be taken into account to carry out the research problem:
What do you want to investigate?
How do you want to investigate?
How far do you want to investigate?
What elements are available to carry out the research?
Why do you want to investigate?
How much time is available?
The interrogative expressions determine the condition of the matter when asking the question: what, who, where, how, when, etc., are the words that indicate a particular question, for example:
What is the relationship between the variables X and Y?
Does it have practical relevance?
I'm interested?, It is important?, Is it based on previous research?
Is actual?
The approach must be correct and precise, important and well defined, to avoid the accumulation of data that may be irrelevant and therefore a lack of necessary data is appreciated. To be accurate, the scope of study must be delimited.
The investigation should be a penetrating analysis of a limited problem, and not a cursory examination of a broad field of study.
So then, when posing a problem, the previously acquired knowledge is always considered; But, understanding them as a claim to solve the unknowns that the development of knowledge itself contains and also, in the problem, the results of experimentation and theoretical development that cannot be fully explained with the support of previous knowledge are fundamentally pointed out.
1.3 DELIMITATION OF THE PROBLEM
Like any phenomenon in the universe, the research problem is not static but corresponds to a dynamic. Since the problems exist in a state of latency (their manifestations are not yet evident) waiting to be recognized.
Although the problem is latent, it is not always fully recognized, part of it can be identified when something of it manifests itself, therefore when visualizing the problem, several enigmas may be found with aspects that require an answer, if that happens, They must be reduced or located in goals that can be addressed to a single study, therefore it must be raised in an appropriate way, with clear and concise language, that is, it must be delimited.
The identification and clear delimitation of a problem with a view to its scientific investigation is not something easy to achieve, since there are no rules for it; the very act of "inventing" or discovering a problem is something that escapes logical analysis.
However, really thinking about a specific problem that is theoretically significant and, in principle, searchable, can become a very careful thought process and that is not usually undertaken without a minimum of vocation.
What generally happens when one poses a problem that he believes is already delimited by consulting more information, it turns out that a series of questions are derived that for us can be problems, in such a way that our original problem is a very general problem, so the last question from which another that interests us no longer arises will be our problem to investigate.
- 4 FORMULATION OF THE HYPOTHESES
We can define the hypothesis as an attempted explanation or a "provisional" answer to a research problem. Its function consists of delimiting the problem to be investigated according to some elements such as time, place, characteristics of the subjects, etc.
Getting to verify or reject the hypothesis that has been previously elaborated, confronting its theoretical statement with the empirical facts, is the primary objective of any study that seeks to explain some field of reality.
To make an adequate hypothesis, we must take into account the following points:
The terms used must be clear and specific in order to define them in an operational way, so that any researcher who wants to replicate the research can do so. A hypothesis without empirical reference constitutes a value judgment. If a hypothesis cannot be subjected to empirical verification, from the scientific point of view it has no validity.
The hypotheses must be objective and not carry any value judgment; that is, the phenomenon should not be defined with adjectives such as "better" or "worse", but only as we think it happens in reality.
The hypotheses must be specific, not only with regard to the problem, but also to the indicators that are going to be used to measure the variables that we are studying.
The assumptions must be related to the resources and techniques available. This means that when the researcher formulates his hypothesis, he must know if the resources he possesses are adequate to verify it.
The hypothesis must be directly related to the theoretical framework of the research and be derived from it.
The hypotheses must be the product of objective observation and its verification, be within the reach of the researcher.
REQUIREMENTS OF THE HYPOTHESES
The hypotheses must:
Establish the variables to study, that is, specify the variables to study, set limits.
Establish relationships between variables, that is, the hypothesis must be specified in such a way that it serves as the basis for inferences that help us decide whether or not it explains the observed phenomena. The hypotheses must establish quantitative relationships between variables.
Maintain consistency between facts and hypotheses, since these are based, at least in part, on already known facts. Therefore, the hypotheses should not establish contradictory or inconsistent implications with what has already been verified in an objective way.
TYPES OF HYPOTHESES
Null hypothesis. For all types of research in which we have two or more groups, a null hypothesis will be established.
The null hypothesis is one that tells us that there are no significant differences between the groups.
For example, suppose a researcher believes that if a group of young people undergoes intensive training for the Mathematics Olympics, they will be better at solving problems than those who received no training. To demonstrate his hypothesis, he takes a sample of young people at random, and also randomly distributes them into two groups: one that we will call experimental, which will receive training, and another that will not receive any training, which we will call control. The null hypothesis will indicate that there is no difference in problem solving performance between the group of young people who received the training and the one who did not.
A null hypothesis is important for several reasons:
It is a hypothesis that is accepted or rejected according to the result of the investigation.
Having a null hypothesis helps to determine if there is a difference between the groups, if this difference is significant, and if it was not due to chance.
Not all research requires the formulation of a null hypothesis. Recall that the null hypothesis is the one by which we indicate that the information to be obtained is contrary to the working hypothesis.
In formulating this hypothesis, it is intended to deny the independent variable.
That is, it is stated that the cause determined as the origin of the problem fluctuates, therefore, it must be rejected as such.
Another example:
Hypothesis: children's learning is directly related to their age.
Null hypothesis: there is no significant difference between learning in children of different ages.
Conceptual hypothesis. It is the hypothesis that is formulated as a result of the theoretical explanations applicable to our problem. It helps us explain the phenomenon we are investigating from a theoretical point of view.
It is the guiding hypothesis of the investigation, it tries to approach the problem as a basis for the search for data. You cannot cover more than what is proposed in the research objectives or disagree with them. We can state it as a causal or determining relationship from the statement of the problem, from which the variables arise
Work hypothesis. It is one that serves the researcher as the basis of his research, that is, it tries to give a tentative explanation to the phenomenon that is being investigated. This is the hypothesis that the researcher will try to accept as a result of his research, rejecting the null hypothesis.
The working hypothesis is said to be operational by presenting the conceptual or general hypothesis quantitatively (in measurable terms).
Alternative hypothesis. When answering a problem, it is very convenient to propose other hypotheses in which independent variables appear different from the first ones we formulate. Therefore, in order not to waste time in useless searches, it is necessary to find different alternative hypotheses in response to the same problem and choose among them which ones and in what order we are going to treat their verification.
The hypotheses, of course, will differ depending on the type of research being carried out. In exploratory studies, sometimes the objective of the investigation may be simply to obtain the minimum knowledge that allows a hypothesis to be formulated.
It is also acceptable that, in this case, they are not very precise, as when we affirm that "there is some kind of social problem in such a group", or that the planets have some kind of atmosphere, without specifying what elements it is composed of.
Works of a descriptive nature generally present hypotheses of the type "all Xs have, to some extent, the Y characteristics." For example, we can say that all nations have some international trade, and dedicate ourselves to describing, quantifying, the commercial relations between them. We can also make claims of the type "X belongs to type Y", as when we say that a technology is capital intensive. In these cases, we describe, classifying it, the object of our interest, including it in a complex ideal type of higher order.
Finally, we can construct hypotheses of the type "X produces (or affects) Y", where we will be in the presence of a relationship between variables.
Only in cases of explanatory research is it necessary to clearly formulate what the research hypotheses are. In descriptive investigations and, even more so, in exploratory ones, it is possible to omit hypotheses, either because they are so broad and poorly defined that they say very little to whoever reads the research report, or because it is not possible or necessary to verify them..
Difficulties in formulating hypotheses:
Lack of knowledge or lack of clarity in the theoretical framework.
Lack of aptitude for the logical use of the theoretical framework.
Lack of knowledge of proper research techniques to write
Hypothesis in due form.
Usefulness of hypotheses:
The correct use and formulation of hypotheses allow the researcher to test aspects of reality, reducing the distortion that their own desires or tastes could produce. They can be tested and proven to be likely correct or incorrect without interfering with the individual's values or beliefs.
Structure of the hypotheses
A hypothesis is generally specified by the IF - THEN structure (when two variables are involved). When the variables are more than two, the most frequent structures are:
- If P, then Q, under conditions R and S. If P1, P2 and P3, then Q.
- 5 DETERMINATION OF THE VARIABLES
The simplest definition is the one referred to the ability of objects and things to modify their current state, that is, to vary and assume different values. Sabino (1980) states:
"We understand by variable any characteristic or quality of reality that is capable of assuming different values, that is, it can vary, although for a given object that is considered it may have a fixed value.
Briones (1987: 34) defines:
«A variable is a property, characteristic or attribute that can occur in certain subjects or can occur in different degrees or modalities… they are classificatory concepts that allow individuals to be placed in categories or classes and are capable of identification and measurement.
CLASSIFICATION OF VARIABLES
Independent variable:
It is that characteristic or property that is supposed to be the cause of the phenomenon studied. In experimental research this is the name given to the variable that the researcher manipulates.
Dependent variable:
Hayman (1974: 69) defines it as a property or characteristic that is to be changed by manipulating the independent variable.
The dependent variable is the factor that is observed and measured to determine the effect of the independent variable.
Intervening Variable:
They are those characteristics or properties that in one way or another affect the expected result and are linked to the independent and dependent variables.
Moderator Variable:
According to Tuckman: they represent a special type of independent variable, which is secondary, and is selected in order to determine whether it affects the relationship between the primary independent variable and the dependent variables.
Qualitative variables:
They are those that refer to attributes or qualities of a phenomenon. Sabino (1989: 80) points out that a definite numerical series cannot be built on this type of variable.
Quantitative Variable:
They are those variables in which characteristics or properties can occur in different degrees of intensity, that is, they admit a numerical scale of measurement.
Continuous Variables:
They are those that can adopt between two intermediate reference points. Academic grades (10.5, 14.6, 18.7, etc.)
Discrete Variables:
They are those that do not admit intermediate positions between two numbers. For example, in Barinas the territorial division is made up of 11 municipalities by no (10.5 or 11.5 municipalities).
Control variables:
According to Tuckman: He defines it as those factors that are controlled by the researcher to eliminate or neutralize any effect that they might otherwise have on the observed phenomenon.
Variable Operationalization:
It is an important step in the development of the investigation. When the variables are identified, the next step is their operationalization.
2.1 EXPERIMENTAL INVESTIGATION
Experimental research is a type of research that uses logic and principles found in the natural sciences. The experiments can be carried out in the laboratory or in real life.
These generally involve a relatively small number of people and address a fairly focused question. Experiments are most effective for explanatory research and are often limited to topics in which the researcher can manipulate the situation in which people find themselves.
In most of these experiments, the researcher divides the people under investigation into two or more groups.
The two groups receive identical treatments, except that the researcher gives one group and not the others the condition in which he is interested: the treatment.
The researcher measures the reactions of both groups accurately. By controlling the conditions of both groups and giving treatment to one of them, the investigator can conclude that the different reactions of the groups are due solely to the treatment.
CHARACTERISTICS OF THE EXPERIMENTAL METHODOLOGY
Definition: An experiment is a study in which at least one variable is manipulated and the units are randomly assigned to the different levels or categories of the manipulated variables. (Pedhazur and Pedhazur, 1991)
Characteristics of the experimental design:
- Manipulation: it is the deliberate intervention of the researcher to cause changes in v. dependent. Randomization: greater size of effects compared to equalization.
All experimental designs are characterized by manipulation, but can be classified according to randomization in:
- Truly experimental.Casiexperimental.
In experimental designs, randomization is how the subjects are distributed in the different groups that are part of the study. The first randomized clinical trial was carried out in 1947 by Sir Austin Bradford Hill and it was carried out on the effect of Streptomycin in Tuberculosis, it is the first study carried out with an experimental design, until that moment the research design that was carried out was the same "Case study", simple observational studies.
Randomization measures and reduces error.
In Health Sciences, as it is so important to study the effects produced by a variable, its consequences and the cause-effect relationship that can occur, it is very important to know the error and reduce it as much as possible, for this reason the research studies they must be and must meet the characteristic of randomization, therefore, experimental designs must be used.
Example: Lung Ca incidence study. To carry it out, two groups of people would be taken who must have identical characteristics in terms of the same number of individuals that compose it, age groups that make it up and identical proportion in terms of gender, then we would proceed to the comparison and investigation of Ca lung in each of the groups.
ADVANTAGES OF EXPERIMENTAL DESIGN
- The effect of disturbing or strange variables is eliminated through the effect of randomization. Control and manipulation of the predictor variables clarify the direction and nature of the cause. Flexibility, efficiency, symmetry and statistical manipulation.
Feasibility of experimental designs
- Impossibility of manipulating some variables Ethical issues Practicality
DISADVANTAGES OF EXPERIMENTAL DESIGN
- Difficulty of eligibility and handling of control variables Difficulty of having representative samples Lack of realism
EXPERIMENTAL DESIGN QUALITY
- Internal validity External validity Ecological validity Construct validity
2.2 METHODOLOGY
Stages that the researcher must carry out to carry out an experimental investigation.
* Presence of a problem. For which a bibliographic review has been carried out.
* Identification and definition of the problem.
* Definition of hypotheses and variables and their operation.
* Design of the experimental plan.
-Research design.
-Determination of the population and sample.
-Selection of measuring instruments.
-Elaboration of instruments.
-Procedures for obtaining data.
* Data reliability test.
* Carrying out the experiment.
*Data treatment. Here at this point it must be taken into account that one thing is the raw data, another is the processed data and another, the data that must be given as final
2.3. STATISTICS IN RESEARCH
The statistical application process involves a series of steps:
Selection and determination of the population or sample and the contained characteristics to be studied. In the event that you want to take a sample, it is necessary to determine its size and the type of sampling to be carried out (probabilistic or non-probabilistic).
Obtaining the data. This can be done by directly observing the elements, applying surveys and interviews, and conducting experiments.
Classification, tabulation and organization of the data. The classification includes the treatment of the data considered anomalous that can, at a given moment, falsify an analysis of the statistical indicators. Tabulation involves summarizing the data in statistical tables and graphs.
Descriptive analysis of the data. The analysis is complemented by obtaining statistical indicators such as the measures: central tendency, dispersion, position and shape.
Inferential analysis of the data. Data treatment techniques are applied that involve probabilistic elements that allow conclusions to be inferred from a sample to the population (optional).
Elaboration of conclusions. The final report is built.
2.4 CENTRAL TREND AND DISPERSION MEASURES
Frequency distribution.
Frequency Distribution. It is a grouping of data into mutually exclusive categories giving the number of observations in each category.
The steps to obtain a frequency distribution are as follows:
1) Determine the number of classes you want. One method of determining the number of classes is the "2 through k" rule. This rule suggests selecting as the number of classes the smallest number (k), such that 2 through k is greater than the number of data (n).
2) Determine the interval or the class width. Generally, the class or interval size should be the same for all classes. The classes together must span at least the distance from the smallest value of the raw data to the largest value. Expressed in the following formula:
i = H - L / k
Where:
i = class interval
H = highest observed value
L = lowest observed value
k = number of classes
Usually the result of the formula is rounded to some suitable number, such as a multiple of 10 or 100.
3) Set the limits of each class. It is about setting the limits of each class so that each observation can be placed in only one class. Class boundaries that are unclear or overlapping should be avoided.
4) Put a mark for each observation that remains in each class.
5) Count the number of observations in each class (class frequency)
The relative frequency is obtained by dividing the class frequency by the total data (n). The percentage frequency is obtained by multiplying the relative frequency by 100.
2.5 TABULATION AND GRAPHIC PRESENTATION
The graphical representations of the frequency distributions are usually made with so-called bar graphs (in which the classes are indicated on the horizontal axis and the class frequencies on the vertical axis) or with foot graphs, especially used for show percentage frequencies.
It is important to mention that although graphical representations serve to give a quick view of the way the data behaves, they can also be used (depending on how they are configured) to give a wrong idea of the information that is to be presented.
Measures of central tendency
The purpose of any measure of central tendency is to accurately indicate the center of a set of observations. Some of the most common measures of central tendency are the mean, median, and mode.
Arithmetic average
The arithmetic mean is probably the most important measure of central tendency, in fact it is the most used. It is also called average and we see it applied daily in almost all the spaces and a half dedicated to providing information. Some examples might be the average balance of a bank account, the average salary of a company's employees, the average grade of a student, etc.
Formally defined, the arithmetic mean is the sum of all the values in a sample or population divided by the number of values in the population or sample.
When what is calculated is the mean of a population, it is represented by the Greek letter "". On the other hand, when what is calculated is the mean of a sample, it is represented by "x". Thus, the formulas are as follows:
Population mean = X
N
Where:
= Population mean
X = Represents any particular value
N = Number of individuals in the population
= Indicates the addition operation
Sample mean x = X
n
Where:
x = Population mean
X = Represents any particular value
n = Number of individuals in the population
= Indicates the addition operation
Some characteristics of the arithmetic mean are:
- Every interval or ratio data set has a mean.
- A data set has only one mean.
- The mean is useful for comparing two populations.
- The arithmetic mean is the only measure of central tendency in which the sum of the deviations of the values from the mean will always be zero.
Expressed symbolically (X - x) = 0
Median
Sometimes when there are one or two very large or very small in a data set, the arithmetic mean may not be representative. In those cases, the center point of that data set can best be described using the median.
The median is the central observation of the values of a population or sample once they have been ordered in ascending or descending order. For an even number of observations, the median is the average of the two intermediate values.
Some characteristics of the median are:
-All ordinal, interval, or ratio data sets have a median.
-A data set only has a median.
-The median is not affected by extremely large or extremely small values, so it is especially useful when you have these values.
fashion
The mode is the value that occurs most frequently in a data set. The mode is especially useful for finding the center point of a nominal or ordinal data set.
Some characteristics of fashion are:
-The mode can be determined in data groups at all levels (nominal, ordinal, interval and ratio).
-There can be more than one mode for each group of data.
-Fashion is not affected by extremely large or extremely small values, so it is especially useful when you have these values.
Other measures of central tendency
Other frequently used measures of central tendency are the weighted mean and the geometric mean. A brief explanation of both is given below.
The weighted mean is a special case of the arithmetic mean. It occurs when there are several data with the same value, which can happen when they have been grouped in a frequency distribution. The formula used is:
Weighted mean x = (wX)
w
Where:
x = Population mean
X = Represents any particular value
= Indicates the addition operation
w = Indicates the weight or number of repetitions of each value
For its part, the geometric mean is useful to find the average of percentages, proportions, indices or growth rates. By definition, the geometric mean of a set of n positive integers is the nth root of the product of the n values. The formula used is the following:
Geometric Mean GM = n √ (X1) (X2)… (Xn)
Where:
GM = Population mean
X = Represents any particular value
n = Number of individuals in the population
This same trend measure applied to problems of average percentage increase is as follows:
Percentage increase GM = n √ Value at the end of the period - 1
Average over time Value at the beginning of the period
Measures of dispersion
Dispersion measures are used to obtain complementary information to measures of central tendency and measure the way in which the data that make up a population or sample are distributed. Thus, the range is based on the location of the highest and lowest values of a group of data, and the variance and standard deviation in the deviations of each of the data that make up the population or sample with respect to its mean.
Variance
Variance is one of the most reported measures of central tendency, and as already mentioned, it is based on the difference between the value of each observation and the mean.
In conceptual terms, the variance is the arithmetic mean of the squared deviations from the mean.
When what is calculated is the variance of a population, it is represented by the Greek letter "σ2" (squared), and when what is calculated is the variance of a sample, it is represented by the letter "s2" (also squared). The formulas to calculate each are as follows:
It is important to note that the sample variance formula for calculations has the advantage that it is not necessary to calculate the mean to obtain it.
TABLE PRESENTATIONS
First I will define what a table is and then work on the different kinds of tables requested:
A table is a table that consists of the joint arrangement, ordered and normally totalized, of the sums or total frequencies obtained in the tabulation of the data, referring to the categories or dimensions of a variable or of several variables related to each other.
The tables systematize the quantitative results and offer a numerical, synthetic and global vision of the observed phenomenon and the relationships between its various characteristics or variables. In it, the qualifying phase of quantitative research ends and is finally finalized.
Having the definition of what a table is, we can then work each of the types of tables requested:
Data entry table: This is a table in which only the data obtained from the scientific research or experiment appear. It is the simplest table and it is used when more information about the data is not needed, these tables are built by tabulating the data, this procedure is relatively simple, to do it we deal with a set of statistical data obtained by record the results of a series of n repetitions of some experiment or random observation, assuming that the repetitions are mutually independent and are carried out under uniform conditions, it is important to say that the result of each observation can be expressed numerically, for this type of tables data entry you can work with one or more variables,so that our statistical material consists of n observed values of the variable Xj.
The observed values are usually recorded, first in a list, if the number of observations does not exceed 20 or 30, these data are recorded in increasing order of magnitude.
With the data in this table, various graphic representations can be made and certain numerical characteristics such as the mean, median, etc. can be calculated.
EX: Grouping in a data table
10, 1, 6, 9, 2, 5, 7, 4, 3, 8
| X | one | two | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
GRAPHIC METHODS
First I will define what is a graph or diagram in statistics
A diagram is a kind of schematic, consisting of lines, figures, maps, used to represent either statistical data at scale or according to a certain proportion, or the elements of a system, the stages of a process and the divisions or subdivisions of a classification. Among the functions that the diagrams fulfill, the following can be pointed out:
They make data, systems and processes more visible
They reveal their variations and their historical or spatial evolution.
They can show the relationships between the various elements of a system or a process and represent the correlation between two or more variables.
They systematize and synthesize the data, systems and processes.
They clarify and complement the tables and the theoretical or quantitative expositions.
The study of their arrangement and the relationships they show can suggest new hypotheses.
Some of the most important diagrams are tree diagram, area or surface diagram, band diagram, bar diagram, block diagram, circular diagram, circular polar diagram, point diagram, stem and leaf diagram, histograms and box and mustache graphics or boxplots.
Univariate graphs
To work the univariate graphs we must first know what the univariate statistical analysis is and after this we will work the requested methods
Statistical analysis that operates with data referring to a single variable or frequency distribution and seeks to determine its statistical properties. The aeu provides the analyst with representative measures of the distribution or averages, dispersion indices of the distribution data, procedures to normalize the data, measures of inequality of some data in relation to others and, finally, measures of the asymmetry of the distribution.
Graphs of points: It is a variation of the simple linear diagram which is formed by straight lines or curves, which result from the representation, on a coordinate axis, of frequency distributions, it builds by placing the values corresponding to the variable and on the ordinate axis the value corresponding to the frequency for this value. It mainly provides information regarding frequencies. This is used when only frequency information is needed.
When the sample is grouped by intervals, we work with the class mark of the class interval, the class mark is the midpoint of the interval
EJ: Duration of neon tubes
| X (hours) | Xm | F |
| 300-400 | 350 | two |
| 400-500 | 450 | 6 |
| 500-600 | 550 | 10 |
| 600-700 | 650 | 8 |
| 700-800 | 750 | 4 |
| S 30 |
Neon tubes duration
CONCLUSION
All research work requires steps to follow to carry out a quality and time-consuming research, which does not lack information, has the necessary sources and the population to which it is going to be investigated and more than anything. you don't lose your aim.
Therefore, it is extremely important that the researcher is steeped in the steps to follow in the problem, that he knows how to structure a problem statement, how to make a hypothesis, what are the variables, the method and its objective to follow so that it does not there is confusion when carrying out your investigation.
All research has an introduction, development, conclusion, bibliography and annexes, the latter is attached to the tables, graphs and evaluation instruments or all the information that supports the research, it is important to emphasize that the researcher eliminates personal preferences and feelings already that could hide the results of the investigation.
In methodological research, it is possible to contribute to an alternative, which responds to the problems of a specific society, giving it a solution.
REFERENCES BIBLIOGRAPHY:
- Arana Federico. (1975), "Experimental method for beginners" Edit. Joaquín Motriz, Mexico 77pp Bunge Mario (1989),. "Science, its method and its philosophy" Edit. Nueva Imagen Argentina, 35-61Hernández, R.; C. Fernández and P. Baptisa, (1995), «Research Methodology», Mc.Graw-Hill, Mexico. 505 pp. García –Córdoba F, L: ucía Teresa García - Córdoba., (2004), «The problematization. An opportunity to stimulate and value the generation of researchers ”. Edit. Higher Institute of Educational Sciences of the State of Mexico. Mexico 58 pp. Moreno Rodríguez Diana, Ma del Refugio López Gamiño, Ma. Luisa Cepeda Islas, Patricia Plancarte Cansino and Irma Rosa Al varado Guerrero, (2000), «Proposal of Problem», UNAM FES Iztacala page 5-14 Pick, Susan and López, Ana Luisa. (1994) HOW TO INVESTIGATE IN SOCIAL SCIENCES. 5th ed. Mexico. Ed. Trillas SAPolit, D. and B. Hungler. 1985. "Scientific research in health sciences." 2nd. Interamerican. Mexico. 595pp Tamayo and Tamayo, Mario. (1998). The process of scientific research. 3rd ed. Mexico: Ed. Limusa SA
PAGES VISITED ON THE INTERNET
- Obtained from: http: //www.monografias.com Obtained from «http://es.wikipedia.org/wiki/Investigaci%C3%B3n_experimental«
