1. GROUP NO.5 MBA (3.5) 3rd sem (A.N)
1.Shakir Nawaz Khan .Roll No Farhat ullah Roll No Zubaida dawood Roll No Usman khurshid Roll No Nauman ayaz Roll No.61

2. Data analysis :
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science

7. USMAN KHURSHID .

13. SHAKIR NAWAZ

17. ……

22. ZUBAIDA DAWOOD

23. Qualitative Data Analysis
The outputs of qualitative data analyses are usually words, the inputs are also usually words – typically in the form of extended texts
Data is almost always derived from what the researcher has observed, heard in interviews, or found in documents

24. Qualitative Data Analysis
Social anthropological versus interpretivist approaches
Social anthropologists (and others, like grounded theorists and life historians) believe that there exist behavioral regularities (for example, rules, rituals, relationships, and so on) that affect everyday life and that it should be the goal of researchers to uncover and explain those regularities.

25. Qualitative Data Analysis
Social anthropological versus interpretivist approaches
Interpretivists (including phenomenologists and symbolic interactionists) believe that actors, including researchers themselves, are forever interpreting situations, and that these, often quite unpredictable, interpretations largely affect what goes on.

26. Qualitative Data Analysis
Does qualitative data analysis emerge from or generate the data collected?
The question of which comes first
Data or ideas about data

27. Qualitative Data Analysis
The strengths and weaknesses of qualitative data analysis revisited
Strengths
Can produce theories
More likely to be grounded in the immediate experiences of those participants than in the speculations of researchers.

28. Qualitative Data Analysis
The strengths and weaknesses of qualitative data analysis revisited
Weaknesses
Generalizability

29. Qualitative Data Analysis
Are there predictable steps in qualitative data analysis?
First researchers code their own data or acquire computer-ready data
Other steps are much more fluid
Typical flow includes data collection –data reduction—data displaying—conclusion drawing and verification

30. Qualitative Data Analysis
Data Collection and Transcription
Several software packages exist to facilitate the processing of qualitative data
Qualitative data software packages have many pros an cons and should be considered carefully before adopting.

31. Nauman ayaz .

32. Qualitative Data Analysis
Data Reduction
The various ways in which a researcher orders collected and transcribed data
Coding and memoing are common data reduction techniques

33. Qualitative Data Analysis
Coding
The process of assigning observations, or data, to categories
In qualitative analysis, coding is more open-ended because both the relevant variables and their significant categories are apt to remain in question longer

34. Qualitative Data Analysis
Coding
The goal of coding is to create categories that can be used to organize information about different cases
Assigning a code to a piece of data is the first step in coding
The second step is putting the coded data together with other data coded the same way

35. Qualitative Data Analysis
Coding
Types of Coding
One purpose of coding is to keep facts straight – called descriptive coding
Coding to advance your analysis is analytical coding
The preliminary phase of analytical coding is called initial coding
Initial coding eventually becomes focused coding, which is concentrating or elaborating on codes specific to analysis

36. Qualitative Data Analysis
Coding
Memos
Extended notes that the researcher writes to help herself or himself understand the meaning of codes

37. Qualitative Data Analysis
Data displays
Visual images that summarize information

38. Summary
Quantitative data analyses
Qualitative data analyses