Data Preparation & Analysis

Compiledby

Prof. Rajiv KumarIIM Calcutta

Data Entry

• Some thumb rules/norms– Coding of open-ended responses:

• A good coding scheme: Categories are– Mutually exclusive– Collectively exhaustive

• Illustration of coding schemes

– Variables in column, cases (e.g., respondents in rows)

– Maintaining records of forms/questionnaires to be able to go back

Data Preparation• Why are data missing:

– Carelessness (or fatigue) of respondents?– Intentional non-response?

• The extent of missing data:– If list-wise deletion is feasible– 10% or less missing data in a case/observation

ignorable– Otherwise, further analysis is warranted

• Imputation of values– Not applicable for non-metric data– Drawbacks of imputation– Imputation: For example, substituting with mean

• Outlier detection

Hair et al., 2009

Quantitative Data Examination

• Examination of variables:– Histogram and test for normality– Data transformation, in case of not meeting

assumptions such as normality or homoscedasticity

Hair et al., 2009

Qualitative Data Preparation

• When to stop data collection: Theoretical saturation– Simultaneous collection and analysis of data

• Writing notes or memos

• Transcribing or not?– Use of software in transcription– Line numbering for transcripts