Data presentation and analysis in research

Canada quality analysis and analysis is the process of developing answers to questions through the examination and interpretation of data. The basic steps in the analytic process consist of identifying issues, determining the availability of suitable data, deciding on which methods are appropriate for answering the questions of interest, applying the methods and evaluating, summarizing and communicating the ical results underscore the usefulness of data sources by shedding light on relevant issues. Some statistics canada programs depend on analytical output as a major data product because, for confidentiality reasons, it is not possible to release the microdata to the public. Data analysis also plays a key role in data quality assessment by pointing to data quality problems in a given survey. Analysis can thus influence future improvements to the survey analysis is essential for understanding results from surveys, administrative sources and pilot studies; for providing information on data gaps; for designing and redesigning surveys; for planning new statistical activities; and for formulating quality s of data analysis are often published or summarized in official statistics canada releases. Statistical agency is concerned with the relevance and usefulness to users of the information contained in its data. Analysis is the principal tool for obtaining information from the from a survey can be used for descriptive or analytic studies.

Data presentation and analysis in qualitative research

The study of background information allows the analyst to choose suitable data sources and appropriate statistical methods. Any conclusions presented in an analysis, including those that can impact public policy, must be supported by the data being to conducting an analytical study the following questions should be addressed:Objectives. This requires investigation of a wide range of details such as whether the target population of the data source is sufficiently related to the target population of the analysis, whether the source variables and their concepts and definitions are relevant to the study, whether the longitudinal or cross-sectional nature of the data source is appropriate for the analysis, whether the sample size in the study domain is sufficient to obtain meaningful results and whether the quality of the data, as outlined in the survey documentation or assessed through analysis is more than one data source is being used for the analysis, investigate whether the sources are consistent and how they may be appropriately integrated into the riate methods and an analytical approach that is appropriate for the question being investigated and the data to be analyzing data from a probability sample, analytical methods that ignore the survey design can be appropriate, provided that sufficient model conditions for analysis are met. However, methods that incorporate the sample design information will generally be effective even when some aspects of the model are incorrectly whether the survey design information can be incorporated into the analysis and if so how this should be done such as using design-based methods. See binder and roberts (2009) and thompson (1997) for discussion of approaches to inferences on data from a probability chambers and skinner (2003), korn and graubard (1999), lehtonen and pahkinen (1995), lohr (1999), and skinner, holt and smith (1989) for a number of examples illustrating design-based analytical a design-based analysis consult the survey documentation about the recommended approach for variance estimation for the survey. If the data from more than one survey are included in the same analysis, determine whether or not the different samples were independently selected and how this would impact the appropriate approach to variance data files for probability surveys frequently contain more than one weight variable, particularly if the survey is longitudinal or if it has both cross-sectional and longitudinal purposes. Consult the survey documentation and survey experts if it is not obvious as to which might be the best weight to be used in any particular design-based analyzing data from a probability survey, there may be insufficient design information available to carry out analyses using a full design-based approach.

Data analysis and interpretation in qualitative research

Assess the t with experts on the subject matter, on the data source and on the statistical methods if any of these is unfamiliar to determined the appropriate analytical method for the data, investigate the software choices that are available to apply the method. If analyzing data from a probability sample by design-based methods, use software specifically for survey data since standard analytical software packages that can produce weighted point estimates do not correctly calculate variances for survey-weighted is advisable to use commercial software, if suitable, for implementing the chosen analyses, since these software packages have usually undergone more testing than non-commercial ine whether it is necessary to reformat your data in order to use the selected e a variety of diagnostics among your analytical methods if you are fitting any models to your sources vary widely with respect to missing data. At one extreme, there are data sources which seem complete - where any missing units have been accounted for through a weight variable with a nonresponse component and all missing items on responding units have been filled in by imputed values. At the other extreme, there are data sources where no processing has been done with respect to missing data. It should be noted that the handling of missing data in analysis is an ongoing topic of to the documentation about the data source to determine the degree and types of missing data and the processing of missing data that has been performed. This information will be a starting point for what further work may be er how unit and/or item nonresponse could be handled in the analysis, taking into consideration the degree and types of missing data in the data sources being used. Consider whether imputed values should be included in the analysis and if so, how they should be handled.

If imputed values are not used, consideration must be given to what other methods may be used to properly account for the effect of nonresponse in the the analysis includes modelling, it could be appropriate to include some aspects of nonresponse in the analytical model. Report any caveats about how the approaches used to handle missing data could have impact on retation of most analyses are based on observational studies rather than on the results of a controlled experiment, avoid drawing conclusions concerning studying changes over time, beware of focusing on short-term trends without inspecting them in light of medium-and long-term trends. Instead, use meaningful points of reference, such as the last major turning point for economic data, generation-to-generation differences for demographic statistics, and legislative changes for social tation of the article on the important variables and topics. Always help readers understand the information in the tables and charts by discussing it in the tables are used, take care that the overall format contributes to the clarity of the data in the tables and prevents misinterpretation. In the presentation of rounded data, do not use more significant digits than are consistent with the accuracy of the y any confidentiality requirements (e. Minimum cell sizes) imposed by the surveys or administrative sources whose data are being e information about the data sources used and any shortcomings in the data that may have affected the analysis. Either have a section in the paper about the data or a reference to where the reader can get the e information about the analytical methods and tools used.

Standard errors, confidence intervals and/or coefficients of variation provide the reader important information about data quality. Check details such as the consistency of figures used in the text, tables and charts, the accuracy of external data, and simple that the intentions stated in the introduction are fulfilled by the rest of the article. As a good practice, ask someone from the data providing division to review how the data were used. Always do a dry run of presentations involving external to available documents that could provide further guidance for improvement of your article, such as guidelines on writing analytical articles (statistics canada 2008 ) and the style guide (statistics canada 2004). As well, sufficient details must be provided that another person, if allowed access to the data, could replicate the an analytical product to be accurate, appropriate methods and tools need to be used to produce the an analytical product to be accessible, it must be available to people for whom the research results would be , d. Related slideshares at r 10-data analysis & mae nalzaro,bsm,bsn,mn, registered hed on jun 9, you sure you want message goes you sure you want message goes you sure you want message goes r at victoria email a copy of the ppt to my email address. You sure you want message goes aduate student at pacific adventist c adventist you sure you want message goes raphy you sure you want message goes ine delos ant professor at calcutta institute of engineering and ta institute of engineering and r 10-data analysis & analysis ng for analysis  the purpose  to answer the research questions and to help determine the trends and relationships among the in data analysis  before data collection, the researcher should accomplish the following:  determine the method of data analysis  determine how to process the data  consult a statistician  prepare dummy tables  after data collection:  process the data  prepare tables and graphs  analyze and interpret findings  consult again the statistician  prepare for editing  prepare for fication of descriptiveanalysiskinds of data analysis 1.

Descriptive analysis  refers to the description of the data from a particular sample;  hence the conclusion must refer only to the sample. Descriptive statistics  are numerical values obtained from the sample that gives meaning to the data fication of descriptiveanalysis a. Formula: ef = n  where: e = sum of f = frequency n= sample fication of descriptiveanalysis b. Formula: where: x= ς___ x = the mean n ς = the sum of x = each individual raw score n = the number of fication of descriptiveanalysis c. Standard deviation - the most commonly used measure of variability that indicates the average to which the scores deviate from the fication of descriptiveanalysis d. Bivariate descriptive statistics  derived from the simultaneous analysis of two variables to examine the relationships between the variables. Correlation - the most common method of describing the relationship between two fication of descriptiveanalysiskinds of data analysis 1.

Inferential analysis  the use of statistical tests, either to test for significant relationships among variables or to find statistical support for the hypotheses. Inferential statistics  are numerical values that enable the researcher to draw conclusion about a population based on the characteristics of a population sample. The level of significance is a numerical value selected by the researcher before data collection to indicate the probability of erroneous findings being accepted as true. Analysis of variance (anova) - is used to test the significance of differences between means of two or more groups. The parts of tabular data are presented in the following:  rows - horizontal entries (indicates the outcome or the dependent variable)  columns - vertical entries (indicates the cause or the independent variable)  cells - are boxes where rows and columns intersect. Interpretation of data  after analysis of data and the appropriate statistical procedure, the next chapter of the research paper is to present the interpretation of the data, which is the final step of research process. The best thing is to review the stated problem and tie up with the result of your data analysis.

Recommendations  this is based on the result of the conclusions  the main goal is geared toward improvement or final researchoutput. Writing the final output  the researcher should know not only the parts in research process but also the forms and style in writing the research proposal and the research format of writing inary pages 1. Title page/ title of the study - is a phrase that describes the research study. Acknowledgement page - is a section wherein the researcher expresses his deep gratitude for those persons who assisted and helped him to make the study a successful one. Table of contents - from the word itself, it contains all the parts of the research paper including the pages. List of tables - this follows the table of content and indicates the title of the tables in the research paper. Introduction  this section refers to:  “what this study is all about” or “what makes the researcher interested in doing the study”.

Chapter ii review of related literature and studies  literature (foreign/local)  studies (foreign/local)  justification of the present study chapter iii research design and methodology  research design  research subject  instrumentation  data gathering procedure  statistical treatment of data chapter iv analysis and interpretation of data chapter v summary, conclusion and recommendations bibliography appendix curriculum of contents  indicates all the contents of research paper and the page number for each section is placed at the right-hand margin. In numbering the tables, use arabic ts from a college career course - linkedin board essential course - linkedin 2016 for course - linkedin tation, analysis and interpretation of analysis tative data ative data n nigatu analysis, presentation and interpretation of r 4 presentation of chnic university of the sent successfully.. Clipboards featuring this public clipboards found for this the most important slides with ng is a handy way to collect and organize the most important slides from a presentation. In the meantime, to ensure continued support, we are displaying the t styles and h dental ctthis paper provides a pragmatic approach to analysing qualitative data, using actual data from a qualitative dental public health study for demonstration purposes. The paper also critically explores how computers can be used to facilitate this process, the debate about the verification (validation) of qualitative analyses and how to write up and present qualitative research r l, ritchie j, o'connor w. Checklists for improving rigour in qualitative research: a case of the tail wagging the dog? Validity in qualitative health care research: an exploration of the impact of individual researcher perspectives within collaborative enquiry.

Burnardsenior research fellow, faculty of health, sport and science, university of glamorgan, pontypridd, cf37 1dlp. Ed e toolspdfanalysing and presenting qualitative datadownload as pdfview interactive pdf in readcubeshare on facebookshare on twittertoolstoolspdfrights & permissionsprintsharetwitterfacebookdigggoogle+hare uses cookies to improve functionality and performance, and to provide you with relevant advertising. See our privacy policy and user agreement for tation, analysis and interpretation of this presentation? Related slideshares at tation, analysis and interpretation of ella perez, cielito zamora high hed on jul 24, 2014. Presentation on how to prepare the fourth chapter of a you sure you want message goes you sure you want message goes ed abubakar tafawa balewa you sure you want message goes aduate student at pacific adventist c adventist you sure you want message goes manager at wana solutions you sure you want message goes here. Like your presentation,can i have a copy of your presentation/, you sure you want message goes cal surveyor at dilla rilekhya strator at data link e the inherent independence of tables and text, include in the body of the report sufficient analytical and summary statements derived from each table to provide the reader a comprehensible and logical interpretation of findings for expedience, place tables as close as possible to the discussion of the facts or data in the text, if this is not possible, mention the table number whenever it is being referred to in the preparation and reproduction of figures are more time-consuming and more expensive than those of tables. Analysis: social analysis (it is frequently qualitative because the understanding of the phenomenon under study may not require quantification or because the phenomenon itself does not lend itself to precise measurement) from the biggest to the smallest class most important to the least important ranking of students according to brightness.

Social analysis - (it is frequently qualitative because the understanding of the phenomenon under study may not require quantification or because the phenomenon itself does not lend itself to precise measurement). The discussion with a summary of the main ate – dissimilar; unlike; different if the results are contrary to what were expected or maybe just inconclusive, the researcher should explain the reasons for the unexpected results which may be due to methodological or theoretical concerns (beiger and gerlach, 1996) *methodological concern – the inconsistency or the deviation from the expected results may be due to how the researcher carried out the study and, in particular, the way the variable was measured. One possible reason is that the instrument used for data collection was not a valid one, thus it was not able to measure what is intended to measure (lacaba-bago, 2011) *theoretical concerns – in general, hypothesis are logically deduced from theories based on certain assumptions. Highly opinionated and sweeping statements should be f – of that or tation, analysis and interpretation of data. Tabular - (a systematic related idea in which classes cal facts or data are given and their subclasses are a column in order to present onships of the sets or or data in a definite, compact and. The table should be so it enables the reader hend the data t referring to the text;. Qualitative analysis – is on precise quantitative the biggest to the important to the g of students according to.

The textual presentation ment or expand ts of tables and charts,Rather than duplicate them. The findings are compared sted with that of retations are made ing techniques: visual course - linkedin ve insights: renaldo lawrence on course - linkedin 365 for course - linkedin r 10-data analysis & mae nalzaro,bsm,bsn,r 4 presentation of chnic university of the analysis, presentation and interpretation of r 4 presentation, analysis and tative data analysis sent successfully..