Data Analysis Efficiency

Data analysis efficiency

Introduction

The data analysis seen from a more general, broader point, is a science that is responsible for studying, analyzing and examining a set of data with the purpose of drawing conclusions on information to make decisions, or simply expand knowledge about knowledge aboutvarious topics. During the research process the data analysis is of the utmost importance due to the multiple functions with which it meets. Data analysis makes it easier for the organization to improve the information it has available to help us make decisions.

Developing

The data analysis consists mainly in the performance of the operations to which the researcher will submit the data in order to achieve the objectives of the study. All these operations cannot be defined in advance rigidly. Data collection and certain preliminary analyzes can reveal problems and difficulties that will outcome the initial planning of the data analysis. However, it is important to plan the main aspects of the analysis plan based on the verification of each of the hypotheses formulated.

These definitions will in turn condition the data collection phase. When carrying out an exploration or survey, researchers must go through different stages within it, once the person culminates the data collection and processing stage, it begins the most fundamental stage of all research work: data analysis, this can be qualitative or quantitative. Qualitative data analysis is defined as the procedure through which the information collected by researchers is structured and handled.

To set ties, translate, extract meanings and conclusions. This analysis is characterized by its cyclical and circular form, versus the linear position adopted in quantitative data analysis. In the data, certain analyzes such as those are the following: descriptive statistics for each variable, reasons and fees, inferential statistics: from the sample to the population, parametric analysis, non -parametric analysis, multivariate analysis, qualitative analysis of the data and analysis and analysisquantitative and qualitative analysis: question of approaches.

Each of them is vital and of the utmost importance during research. It is important to mention that to carry out these analysis there are several computer programs and it is not necessary to perform all operations manually. Another important thing to mention is that there are two large families of data analysis techniques: qualitative techniques: in which the data is presented verbally (or graph) – such as interview texts, notes, documents. Quantitative techniques: in which the data is presented numerical.

These two modalities are radically different species, use completely differentiated knowledge and techniques. On the other hand, quantitative data are analyzed based on numbers and variables that can be measured, in order to establish statistics. This type of analysis is able to show much more precise conclusions, that is why it is the most used method in the field of exact and natural sciences. According to Schoenbach, in a well -executed study, the data collection plan includes procedures.

The instruments, and forms, designed and tested to maximize their precision. All data collection activities are monitored to ensure adherence to the data collection protocol and to promote actions to minimize and solve missing or questionable data situations. The monitoring procedures are established at the beginning and maintained throughout the study, since the irregularities are detected as soon as.

It is important to highlight that within the analysis of qualitative data, the researcher can face certain difficulties when developing it, some of them are: the polysemic nature of the data collected, due to the complex meanings that can be found in a video or videoAudio recording, it is necessary for the researcher to be very well prepared regarding the subject or point you want to extract from the analyzed reality. Another difficulty would be the magnitude of the data collected, that is, the abundance of both textual and visual and audio information. 

Due to this difficulty is that the researcher relies on computer software, a very useful tool for systematization and control of the analysis methodology. The relationship between analysis and interpretation and the specific way they take, both separately and together, vary from one study to another, depending on the different schemes or levels of research and, fundamentally, the proposed design. The data, from which the researcher initiates the analysis, are different according to the level of elaboration performed.

Which depends on the nature of the research problem and, consequently, on the type of research;Also of the techniques and procedures followed in the elaboration. Data analysis becomes a descriptive analysis, to examine and then inform the frequency measures that refers to the incidence, prevalence and extension, which are related to time and association that speaks of reasons and fees. All these measures are calculated by important subgroups and probably for the total study population. 

Standardization procedures are very important and even to take into account the differences in distributions by age and other risk factors, monitoring time, etc. The different statistical tests are divided into 2 large sets: parametric and non -parametric. Once the aspects indicated above were clearly defined, the test belongs to the test. When we must make a decision between two alternatives such as "null hypothesis" or "alternative hypothesis" Schoenbach, establishes that two types of errors can be made:

Type I: erroneously reject H0 (I.and., conclude, incorrectly, that the data is not consistent with the model). Type II: Do not erroneously reject H0 (I.and., conclude, incorrectly, that the data is consistent with the model). Regularly, in the probability of error type I it is the one that receives the most attention and is called the “level of significance” of the test. Currently, there are many methods to obtain the value of statistical significance. The selection depends on the measurement level of the variables, the sampling design from which the data was obtained, and other factors.

For this there are different methods and procedures such as ANOVA, which basically refers to the “variance analysis for more than two groups” is used when information is wanted to compare more than two groups. The multivariate analysis comes to solve (briefly) this big problem. This is a set of statistical procedures in general called to handle a set of variables so that their individual effects can be isolated without having to resort to designs where the control of confusing variables becomes impossible by the large number of subjects that would demand.

On the other hand, if what you want is to extract a score from the scale using different items is to obtain a more reliable measure of the construct than that is possible from a single item. Reliability in the scale is classically evaluated using the Alpha Cronbach coefficient, which can be considered as the average of all correlations. In the event that all items effectively measure the same construct in the same way and that they have been answered very similar, the only differences in their values should be due to random measurement errors.

Today we are in a digitalized world, since we rely on multiple and versatile programs such as SPSS and Minitab, programs that, having access to their data or external ones such as Microsoft Excel for example, can be captured and converted by adapting it to the base ofdata of the same SPSS or Minitab program. The collection and analysis of the information are two complex processes that develop simultaneously in qualitative research. The analysis accompanies the information collection process since its inception.

Guiding the field work and allowing you to enter the issues that may arise during subsequent immersions in the field. In addition, qualitative researchers include the analytical process as another section of field work, unlike what happens in other types of research. However, in scientific research, especially in social sciences such as the educational and social communication field, they can be carried out through two different methods: qualitative and quantitative. 

Each with its own epistemological foundation, techniques and methods keeping the object of study clearly. Although both methods are based on different rules and development process, they do not work separately. In this way "it is not about seeing that the qualitative is the best and that the quantitative is overcome or very criticized" (monk). Both allow an approach to know different aspects of reality, and know them in different ways. 

Depending on the interest in what you want to know, it is that the use of one or another method can be decided. Each of the methodologies offers important elements, have limits and have possibilities. The researcher’s task, in any case, is to know the potentials of each paradigm, be very clear in their research questions and know which of them to generate to generate the knowledge you want. Data analysis processing can be found through a qualitative or quantitative collection through different means. 

conclusion

This process consists of collecting data and then organizing and classifying them either in tabs or graphics, and subsequently being analyzed truthfully and the closest to reality being a great help to the research process. Always taking into account that currently various computerized programs will be of great help for data analysis we must be aware of how our results will be extracted and subsequently introduced to the programs. This in order to avoid possible errors and uncertainty as well as faster and more efficiency.