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Data analysis refers to the inspection of results to determine any relationships between concepts, constructs or variables; to identify patterns or trends; or to establish themes in the data.
Regardless of whether the data is qualitative or quantitative, analysis may:
Source:
Baxter, L., Hughes, C. & Tight, M. 2010. Ch.8: Preparing to analyse your data. How to research. Berkshire: Mcgraw-Hill. 211-226
The following are common data analysis tools or programmes:
Also consult Web Pages that Perform Statistical Calculations (StatPages.org)
In order to effectively interpret your observations you need to relate it to existing theoretical frameworks, take rival explanations into account and explain how the data supports your interpretation. Interpretations are articulated in the form of hypotheses or theories.
Source:
Mouton, J. 2001. How to succeed in your master's & doctoral studies: a South African guide and resource book. Pretoria: Van Schaik.
The type of analysis you do is generally dependent on whether your data is quantitative or qualitative. Methods of analysis may also differ by scientific discipline. The optimal stage for determining appropriate analytic procedures occurs early in the research process and should not be an afterthought.
Image source: http://9mathsmrsrae.edublogs.org/
Source:
Mouton, J. 2001. How to succeed in your master's & doctoral studies: a South African guide and resource book. Pretoria: Van Schaik.
A short video by Prof Jeff Leeks on the landscape of data analysis.
Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis by
This book questions the traditional method of analysing the effectiveness of a treatment or intervention using tests of statistical significance, based on a p value that falls below a predetermined criterion
Qualitative data analysis: From start to finish by
This is the ideal book to get you up and running with the basics of qualitative data analysis. It breaks everything down into a series of simple steps and introduces the practical tools and techniques you need to turn your transcripts into meaningful research.
Using multidisciplinary data from interviews and focus groups Jamie Harding provides clear guidance on how to apply key research skills such as making summaries, identifying similarities, drawing comparisons and using codes.
The book sets out real world applicable advice, provides easy to follow best practice and helps you to:
· Manage and sort your data
· Find your argument and define your conclusions
· Answer your research question
· Write up your research for assessment and dissemination
Clear, pragmatic and honest this book will give you the perfect framework to start understanding your qualitative data and to finish your research project.
Qualitative data analysis: Practical strategies by
This book takes the reader on a step by step journey through the different stages of analysis, from first reading transcripts to presenting findings in a report or dissertation. For each stage of the process there are demonstrations using real data and exercises for the reader to perform. While acknowledging that there are many different forms that qualitative data analysis can take, the book provides a series of ideas and examples that readers will find helpful when analysing their own data.
Advanced quantitative data analysis (Online) by 
Multivariate data analysis: A global perspective by
For graduate and upper-level undergraduate marketing research courses. For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. In this seventh revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques
SPSS survival manual: A step by step guide to data analysis using IBM SPSS by
Presents a guide to the research process, covering such topics as descriptive statistics, correlation, t-tests, factor analysis, and multiple regression.
Adventures in social research: Data analysis using IBM SPSS statistics by
Written by esteemed social science research authors,Adventures in Social Research: Data Analysis Using IBM® SPSS® Statistics, encourages students to practice SPSS as they read about it and provides a practical, hands-on introduction to conceptualization, measurement, and association through active learning. This fully revised workbook will guide students through step-by-step instruction on data analysis using the latest version of SPSS and the most up to date General Social Survey data. Arranged to parallel most introductory research methods texts, this text starts with an introduction to computerized data analysis and the social research process, then walks readers step-by-step through univariate, bivariate, and multivariate analysis using SPSS Statistics. In this revised edition, active and collaborative learning will be emphasized as students engage in a series of practical investigative exercises.
An R Companion to Linear Statistical Models by Understanding the New Statistics by 
Handbook of advanced multilevel analysis by 
Handbook of statistical analysis and data mining applications (Online) by 