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Analysis is more than coding

❶Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. The need for data cleaning will arise from problems in the way that data is entered and stored.

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He emphasized procedures to help surface and debate alternative points of view. Effective analysts are generally adept with a variety of numerical techniques. However, audiences may not have such literacy with numbers or numeracy ; they are said to be innumerate.

Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques. For example, whether a number is rising or falling may not be the key factor. More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy GDP or the amount of cost relative to revenue in corporate financial statements.

This numerical technique is referred to as normalization [7] or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation i.

Analysts apply a variety of techniques to address the various quantitative messages described in the section above. Analysts may also analyze data under different assumptions or scenarios.

For example, when analysts perform financial statement analysis , they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock.

Similarly, the CBO analyzes the effects of various policy options on the government's revenue, outlays and deficits, creating alternative future scenarios for key measures. A data analytics approach can be used in order to predict energy consumption in buildings.

Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. In education , most educators have access to a data system for the purpose of analyzing student data. This section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a Wikipedia article. The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question.

The initial data analysis phase is guided by the following four questions: The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase. The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study.

One should check whether structure of measurement instruments corresponds to structure reported in the literature. After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase.

One should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups. If the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in sample. Other possible data distortions that should be checked are:.

In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample and specifically the size of the subgroups when subgroup analyses will be performed during the main analysis phase. The characteristics of the data sample can be assessed by looking at:. During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.

Also, the original plan for the main data analyses can and should be specified in more detail or rewritten. In order to do this, several decisions about the main data analyses can and should be made:. Several analyses can be used during the initial data analysis phase: It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level: Nonlinear analysis will be necessary when the data is recorded from a nonlinear system.

Nonlinear systems can exhibit complex dynamic effects including bifurcations , chaos , harmonics and subharmonics that cannot be analyzed using simple linear methods. Nonlinear data analysis is closely related to nonlinear system identification. In the main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.

In the main analysis phase either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are tested.

Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction.

Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well.

When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. The confirmatory analysis therefore will not be more informative than the original exploratory analysis. It is important to obtain some indication about how generalizable the results are.

Are the results reliable and reproducible? There are two main ways of doing this:. Many statistical methods have been used for statistical analyses.

A very brief list of four of the more popular methods is:. Thus, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data. In most research studies, the analysis section follows these three phases of analysis. Descriptions of how the data were prepared tend to be brief and to focus on only the more unique aspects to your study, such as specific data transformations that are performed.

The descriptive statistics that you actually look at can be voluminous. In most write-ups, these are carefully selected and organized into summary tables and graphs that only show the most relevant or important information. Usually, the researcher links each of the inferential analyses to specific research questions or hypotheses that were raised in the introduction, or notes any models that were tested that emerged as part of the analysis. In most analysis write-ups it's especially critical to not "miss the forest for the trees.

The process of developing a good code system is already more than coding in the technical sense of just attaching a label to a data segment. Furthermore, having coded the data is not the end of the analysis process. After coding, the data is prepared for further analysisand exploration. Frequently used tools are the code-cooccurence explorer and the codes-PD table for the purpose of cross-case comparisons.

Results can be saved in various forms as a basis for new queries, for instance supporting researchers in identifying types and typologies in the data. Thus, analysis is more than coding and still largely dependent on the person sitting in front of the computer using thesoftware tool.

As I have no idea how his attitude and his decision would betoday, I decided not to include the original foreword, except for thefollowing quotation which, I promise, will remain true for some time tocome: Your will find pointers whether CAQDAS is a useful choice and where researchers have used it for data organization and management only.

The list is adapted from online QDA http: Action research consists of a family of research methodologies. The focus is a social problem, rather than the theoretical interests of a scientist. The aim is to promote change by engaging participants in a process of sharing knowledge. It contains among other elements also components of field research. Types of data include interviews, focus groups, observation, participant observation, participant-written cases and accounts.

How Professionals Think in Action. The practice of action inquiry, in P. Bradbury eds , Handbook of Action Research: Participative Inquiry and Practice. Teaching and Learning in Motion. Life History and biographical research is today often used interchangeably.

Data are collected in form of narrative interviews. Of interest is the entire life story in terms of its genesis and how it is constructed in the present.

The steps of data analysis involve thematic analysis, the reconstruction of the life history, a microanalysis of individual text segments, contrastive comparisons and the development of types and contrasting comparison of several cases.

Rosenthal proposes a combination of methods to analyze biographical data. Another example is the study by Gouthro Roberts , Brian Structures of meaning and objective Hermeneutics. Columbia University Press, S. Oevermann, Ulrich et al. Die Methodologie einer objektiven Hermeneutik und ihre allgemeine forschungslogische Bedeutung in den Sozialwissenschaften, in Hans-Georg Soeffner ed.

Fischer, Wolfram and Kohli, Martin Methoden der Biographie- und Lebenslaufforschung. Implications for Policies and Practices in Adult Education. Deviant Action and Self-Narration: Journal of the Theory of Social Behaviour, Vol 25 2 , A case study is based on an in-depth investigation of a single individual, group, or event to explore causation.

It may involve the collection of both qualitative and quantitative like documents, archival records, interviews, direct observation, participant-observation, physical artifacts. Several analytic strategies for case studies have been described like placing the evidence in a matrix of categories, pattern matching, statistical procedures, and also coding has been proposed as a way to approach analysis. It is a collection of ethnographic case studies of literacy practice in various marginalized cultural communities.

A methods source book. Casting nets and testing specimens: Two grand methods of psychology. Conversational Analysis or CA is the study of naturally occurring talk-in-interaction, both verbal and non-verbal, in order to discover how we produce an orderly social world. It does not refer to context or motive unless they are explicitly deployed in the talk itself.

The method was inspired bythe ethnomethodology of Harold Garfinkel and further developed in the late s and early s by the sociologist Harvey Sacks. Today CA is an established method used in sociology, anthropology, linguistics, speech-communication and psychology. Typically data are subjected to afine-grained sequential analysis based on a sophisticated form of transcription. In addition to sequential analysis, coding approaches have also been used in recent years for identifying recurrent themes.

The use of coding in conversational analysis however is questioned as an appropriate form of analysis by some. Ten Have, Paul A Practical Guide , Thousand Oaks: Making Thinking Visible with Atlas. Discourse Analysis DA and Critical Discourse Analysis CDA both encompass a number of approaches to study the world, society, events and psyche as they are produced in the use of language, discourse, writing, talk, conversation or communicative events.

It is generally agreed upon that any explicit method in discourse studies, the humanities and social sciences may be used in CDA research, as long as it is able to adequately and relevantly produce insights into the way discourse reproduces or resists social and political inequality.

Thus, the data collection can be comprised of a number of different data formats. An example is provided by Graffigna and Bosio Textual Analysis for Social Research. Fairclough, Norman; Clive Holes The Critical Study of Language.

Graffigna, Guendalina and Bosio, A. International Journal of Qualitative Methods 5 3 , article 5. Ethnography is a multi-method qualitative approachthat studies people in their naturally occurring settings. The purpose is to provide a detailed, in-depth description of everyday life and practice. An ethnographic understanding is developed through close exploration of several sources like participant observation, observation, interviews, documents, newspapers, magazine articles or artifacts.

The results of an ethnographic study are summaries of observed activities, typifications or the identification of patterns and regularities. Computer applications in qualitative research. Qualitative Social Research, 8 3 , Art.

Qualitative Social Research, 10 2 , Art. The founder of Ethnomethodology Harold Garfinkel , developed this methodto better understand the social order people use in making sense of the world through. As data sources he uses accounts and descriptions of day-to-day experiences. The aim is to discover the methods and rules of social action that people use in their everyday life. The focus is on how-question, rather than why-question as underlying motives are not of interest.

Ethnomethodologists conduct their studies in a variety of ways focusing on naturally occurring data. Central is the immersion in the situation being studied. They reject anything that looks like interview data. Important for an ethnomethodological analysis is self-reflection and the inspectability of data, thus the reader of an ethnomethodological study should be able to inspect the original data as means to evaluate any claim made by the analyst.

Steps in the process of data analysis include coding by type of discourse, counting frequencies of types of discourses, selecting the main types and checking for deviant cases. Francis, David and Stephen Hester. An invitation to Ethnomethodology. Language, Society and Interaction.

Its methodological roots are in phenomenology, social interactionism and ethnographyadapted by business studies and marketing research, but also used in other disciplines like medical research. The investigation is carried out in the naturalistic environment where the phenomenon occurs. Methods of data collection include participant observation, depth interviews, group interviews and projective techniques.

Analysis procedures consist of description, ordering or coding of data and displaying summaries of the data. Gendered Suffering and Social Transformations: Domestic Violence, Dictatorship and Democracy in Chile.

A focus group is a form of group interviewmainly used in marketing research.

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Methodology chapter of your dissertation should include discussions about the methods of data analysis. You have to explain in a brief manner how you are going to analyze the primary data you will collect employing the methods explained in this chapter. There are differences between qualitative data.

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In your research proposal, you will also discuss how you will conduct an analysis of your data. By the time you get to the analysis of your data.

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Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories: 1. Content analysis. This refers to the process of categorizing verbal or. Data analysis has two prominent methods: qualitative research and quantitative research. Each method has their own techniques. Each method .

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When using a quantitative methodology, you are normally testing theory through the testing of a hypothesis. Qualitative data analysis is a search for general statements about relationships among • Aims to derive theory from systematic analysis of data. 15 Methods of Data Analysis in Qualitative Research Compiled by Donald Ratcliff 1. Typology - a classification system, taken from patterns, themes, or other kinds of.