Data Collection and Analysis

Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes.

Be it Primary or secondary data, we assist you in collecting data which will serve to attain your research objectives. We have contacts in various institutions and access to various databases to ensure that you are able to get the data you want.

We assist in;

  • Primary data collection using questionnaires, interviews or experiments
  • Secondary data collection from local databases such as CBK, NSE, KIPPRA and KNBS
  • Secondary data collection from international databases such as World Bank, UN, Btirish Council and US Open data portal

Data Analysis
Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003) various analytic procedures “provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data”.

While data analysis in qualitative research can include statistical procedures, many times, analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase (Savenye and Robinson, 2004). The form of the analysis is determined by the specific qualitative approach taken (field study, ethnography content analysis, oral history, biography, unobtrusive research) and the form of the data (field notes, documents, audiotape, videotape).

An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. Improper statistical analyses distort scientific findings, mislead casual readers (Shepard, 2002), and may negatively influence the public perception of research. Integrity issues are just as relevant to analysis of non-statistical data as well.

Considerations/issues in data analysis
There are a number of issues that researchers should be cognizant of with respect to data analysis. These include:

  • Having the necessary skills to analyze
  • Concurrently selecting data collection methods and appropriate analysis
  • Drawing unbiased inference
  • Inappropriate subgroup analysis
  • Following acceptable norms for disciplines
  • Determining statistical significance
  • Lack of clearly defined and objective outcome measurements
  • Providing honest and accurate analysis
  • Manner of presenting data
  • Environmental/contextual issues
  • Data recording method
  • Reliability and Validity

At Buck Consulting we apply various methods to ensure that your analysis is in line with your Research Methodology. These include:

  • Descriptive analysis (mean, SD, Correlation and regression analysis)
  • Factor Analysis
  • T-test,
  • Analysis of Variance (ANOVA),
  • Analysis of Covariance (ANCOVA),
  • Multidimensional scaling,
  • Cluster analysis,
  • Discriminant function analysis
  • Pooled Ordinary Least Squares
  • Panel Regression Modelling
  • Chi-square
  • Qualitative Analysis (Content Analysis, Ethnography, Grounded Theory)

To analyze your data we apply various statistical software including:

  • IBM SPSS Statistics
  • SAS
  • E-Views
  • R