Qualitative and Quantitative Data Management, Manipulation and Interpretation


Qualitativeand Quantitative Data Management, Manipulation and Interpretation



Thequantitative data is sourced from the global health observatory. Theglobal health observatory contains various pages that provideinformation regarding the global situation and highlight the trendsusing database views, core indicators, links and major publicationsto relevant web pages. Various approaches can be used to conduct dataanalysis of quantitative data. This entails use of differentstatistical software like SPSS and Excel. This include


Frequencydistribution depicts the technique that provides a big picture of thedata. The analysis involves representation of the frequency of thefrequency of specific value observed and their percentages for thesame variables. Histogram is a powerful tool in both SPSS and Excelto present the frequency distribution. For instance, in thequantitative data provided. The various fields provided include theGHO, Indicator, Publication Dates, and Sex, region, year, causes andthe numeric value. In this case, frequency can be determined toreflect the frequency of the years either 2000 or 2012, the sexmale, females or both sexes or even the regions African,Mediterranean, European, region of Americas, South East Asia andWestern pacific Asia.

Figure1:Frequency Distribution

Thefigure above is a frequency distribution chart illustrating thedistribution of the data collected based on the regions. It can beperceived from the analysis that all the regions considered African,Eastern Mediterranean, Region of American, West Pacific and SouthEast Asia regions are equally distributed. Their frequency lies at 54level. This implies consistency in the data collected based onregions.


Thedescriptive statistics uses dispersion and measures of centraltendency to describe the data. The most common measures of centraltendency are mean,mode and median. Mean reflects the average of all the valuespresented and is the most popular measure of central tendencyespecially when the data do not have an outlier. On the other hand,median reflects the middle value for the values lined in order. It isimportant for the data with an outlier and unevenly distributed. Modereflects the most common variable.

Onthe other hand, measures of dispersion critical in quantitative dataanalysis include, range, standard deviation and variance. A big rangeresults to a big standard deviation. A range reflects the differencein maximum and minim values while standard deviation is a reflectionof variation of a value from the mean. The square of the standarddeviation is the variance.

Fromthe data provided, the measures of central tendency and dispersionfor data can be presented in the format below.

Descriptive Statistics



Standard Error






Standard Deviation


Sample Variance
















Confidence Level (95.0%)


Theabove table was generated using excel application_dataanalysis_descriptive statistics options.

Theresults obtained show the measures of central tendency for the dataas mean is 117. 1992 (4dec place), median is 14.26008 and mode is 0.On the other hand, the measures of dispersion reflect the range as724.95795, standard deviation as 209.3474799 and the variance as43,826.36733. Basically, the analysis of descriptive statisticsprovides information on measures of central tendency and measures ofdispersion. It is imperative to note that both SPSS and Excel can beused to generate descriptive statistics and can also represent databased on the confidence interval.

Thequantitative research is deductive in nature and it is best suited intesting theories. According to Cohen (2006), the quantitativeresearch designs produce results that can be generalized.


Thequalitative data used in this paper was gotten from the focus grouptranscript from UK data service. The focus group transcription can begenerated for medical professionals, university students, marketresearch, property and legal professionals. A focus group entailspeople or a group that is discussion an issue(s). Normally, thequalitative data analysis methods used enhance the exploration of thecomplexities related with health care, particularly with thepatients. Diverse qualitative methods which incorporate differentepistemological and ontological perspectives are available. Among themost popular data management methods that are currently gainingpopularity among the healthcare researchers is the frameworkapproach, coding and theming processes.

Intheming, the researcher analysts try to obtain the theme for the datacollected using qualitative process. This accounts for ‘who saidwhat’ in order to relate to the theme or the ideas. For instance,in the qualitative data provided, the data collected is inform ofinterview answers with the focus group 3. The interviewees are Susan,Milly and Emily while the interviewer is Dr, Kathleen Lane. Theinterview was conducted in 2007. From the answers and the questionsposes by the doctor to interviewees and vice versa, it is possible toidentify the theme of the interview. It is clear that the main agendais old age and food access in social engagements.

Onthe other hand, the inductive reasoning can be applied in analysis ofqualitative data by employing the conceptual, perspective,relationship, setting and participant characteristics codes. Thesecodes facilitate development of taxonomies, themes and theories. Theintersectional analyses with the coded data for the characteristicsof the participants can be critical in comparative analyses. Theming,coding and framework data analysis and management methods are vitalin qualitative data analysis as they guide the analysts on theperspective of the collected data and enables them come up withrelevant conclusions.

Fromthe interview raw data provided, the representation of data in tablesand graphs may be impossible especially because of the nature of theresearch. This means that the most ideal method for representationthis data is through theme analysis, coding and framework analyses.According to the study by Rowe (2000), accountability as well s questfor professional status to the nursing needs makes the nursing becomeknowledge cantered. The exploration of knowledge sources and theircontribution is the main question that the profession is supposed toaddress (Hall, 2005). The healthcare practitioners can therefore usea range of knowledge in research to make the clinical decisions andenhance their planning.

Thequalitative research development in nursing results from influencesof different traditions. When compared to quantitative research, thequalitative research can be considered to be considerably new withvarious strategies and techniques for analysis emerging.Nevertheless, despite of the differences in qualitative andquantitative research, Eraut (1994) noted that all the research typesshould aim at systematic investigation and contribution of body ofknowledge to shape and guide the profession hence the need toconcentrate on all the available information. Therefore, qualitativeresearch must be assessed based on the merits to enable thepractitioners assess its worth.


Certainly,Corner (1990) has identified whole issue of the value for variousdata analysis approaches in relation to their roles to advancement ofnursing knowledge. The recognition of importance of the qualitativeresearch has been on increase in healthcare. Currently, a lot ofinformation is available on nursing research, data analysis,management interpretation and manipulation (Harrison, 2008 Langford2000 Robinson, 2011 and Castellani and John 2003).

Broadlyspeaking, the quantitative research is objective while qualitativeresearch is subjective. Therefore, in management, analysis andinterpretation of the quantitative data, the researcher can remainobjective and detached. This normally lacks in qualitative research.As it can be observed from the data provided, the qualitativeresearch appears to be of exploratory and aims at gainingunderstanding of the underlying motivations, opinions and reasons forthe differences in age and eating in social places. This has providedinsights on the problem and helps develop hypotheses for anypotential quantitative research. As it can be seen, the sample sizeis considerably small 4people compared to quantitative which has 324respondents.

Onthe other hand, for quantitative research, the sample size isconsiderably high and it quantifies an issue to generate numericaldata that can be easily transformed into other usablecharacteristics. In this case, measurable data has been used toformulate and uncover data to give important conclusions.

Theanalysis, manipulation and interpretation of the qualitative andquantitative data are especially critical in data policing, practiceand future research. Any institution regards data management andinterpretation as the most integral component that drives innovationand good practice. These acts as important drivers that maximize theeffects of data intensive research and improve success for futuregrant proposals and assures on research integrity.


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