Statistics for non statisticians training workshop

Rating: 
Average: 3 (30 votes)
Sector: 
Research

Research Hub is a Research and insights firm that specialises in data collection and analysis, market research, and monitoring and evaluation services. Our philosophy is anchored on the belief that the success of every project or idea is contingent on solid and credible information being available to decision makers. Whilst we know that managers can potentially tap into variety of data that exists out there in various forms, we know that dearth of time and analysis expertise can limit their ability to transform this data into useful information. Our packages are designed to provide cost effective solutions to organisations in the private, public and Not-for-profit sectors.

Statistics for non-statisticians training workshop

Training dates: 9th -13th July 2018

Location: Kigali (Venue TBD )

Award: Completion certificate

Duration: 4 days

Fees: 250,000 (RWF)

About Research Hub Ltd.

Research Hub is a Research and insights firm that specialises in data collection and analysis, market research, and monitoring and evaluation services. Our philosophy is anchored on the belief that the success of every project or idea is contingent on solid and credible information being available to decision makers. Since 2017, we have provided training services to a host of agencies in Rwanda and the region (check on our website http://www.researchub.co.rw/  ). Integral to our approach is the idea of capacity building of data end users in a bid to increase their ability to interpret data which should increased uptake of knowledge generated through various studies they commission.  

Statistics for non-statisticians course description

This course is designed for non-statisticians and has relevance to data users especially in the project management domain. It is intended to equip project managers, research users, policy makers, monitoring and evaluation professionals with ability to understand, interpret and question the validity of the quantitative reports and proposals done by consultants. It is important for end users to understand key assumptions that underpin statistical methods before deciding which approach to endorse.  The course is also meant to strengthen participant’s ability to analyse large datasets using two of the popular software (SPSS and Stata).

Learning objectives

Participants who have gone through this course are able to;

  • Articulate key relevant concepts as used in quanitative research
  • Determine sample size for a descriptive research designs
  • Identify the appropriate sample statistical designs for a study
  • Understand what it takes to generalize findings
  • Interpret statistical reports 
  • Have relevant understanding of counterfactual evaluation designs
  • Tell when to use what statistical test

Who should attend

  • Monitoring evaluation experts, project managers and all professional who supervise work done by researchers
  • Report writers and communication experts tasked to communicate research findings
  • Clinical Project Leaders who will be redesigning and evaluating studies

Course content;

Day One

  • Introduction to statistical concepts
  • Statistical symbols
  • Descriptive versus inferential studies
  • Definition and types of survey bias
  • Definition and types of sampling techniques (practical)
  • Extrapolation and generalization of research findings to the wider population
  • Types of data (categorical, continuous ordinal scale)
  • Statistical tests

Day two: Evaluation design 

  • Evaluation design concepts (treatment, control dependent &independent variable etc.)
  • Randomized control trial (RCT)
  • Quasi-experiments
  • Propensity score matching
  • Cross section studies

Analysis

  • Analysis of variance (ANOVA)
  • Frequency
  • Correlation coefficient
  • Regression analysis
  • Assumptions of underpinning inferential analyses
  • Data interpretation
  • Limitation of quantitative data

Day three  

  • Analysis of variance (ANOVA)
  • Frequency
  • Correlation coefficient
  • Regression analysis
  • Assumptions of underpinning inferential analyses
  • Data interpretation
  • Limitation of quantitative data

Day three

SPSS

  • Introduction to quantitative data analysis
  • Creating variables
  • Importing data
  • Manipulating data
  • Descriptive analysis
  • Bivariate analysis
  • Inferential statistical analyses

Day four

Stata

  • Introduction to quantitative data analysis
  • Creating variables
  • Importing data
  • Manipulating data
  • Descriptive analysis
  • Bivariate analysis
  • Inferential statistical analyses

Application process:

Interested applicants are encouraged to send filled application form (see attachment) and their latest CVs to info@researchub.co.rw not later than 3rd  July 2018.