ANLY482 AY2017-18 T2 Group 16 Discussion

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PROJECT OVERVIEW

INSIGHTS

PROJECT MANAGEMENT

DOCUMENTATION

BACK TO ANLY482

EXPLORATORY DATA ANALYSIS

LOGISTIC REGRESSION

DISCUSSION

Summary

To sum up, these are the key factors affecting failures and successes of volunteer recruitment respectively:

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However, as we will later explain, these should not be simply interpreted as the preferences of new volunteers, but rather, the interplay between existing and new volunteers has to be taken into consideration as well.

Discussion

Based on the logistic regression model, we have come up with key insights for future planning.

1. Sector Differences

There were differences between the Children, Disability and Elderly sectors in terms of recruitment of new volunteers, and these statistically significant differences may explain the differentiation between Programmes, as opposed to intensiveness or frequency as tested in the previous section.

The Elderly sector was found to be statistically insignificant, indicating that elderly programmes neither discouraged or encouraged new volunteers. The Children sector were less likely to attract new volunteers, while the Disability sector were more likely to attract new volunteers.

2. Existing and New Volunteers

The increase in existing volunteers were less likely to increase the number of new volunteers. Even though recruitment of volunteers was open to all, existing volunteers may have taken priority over new volunteers, and new volunteers were perhaps recruited when there are not enough existing volunteers. This was extremely important as many of the other insights cannot simply be interpreted as a preference held by new volunteers - rather, that what was supposedly a factor that increased new volunteers may also be interpreted as unpopular slots that new volunteers have to fill in when existing volunteers do not sign up for it.

3. Public as a recruitment source

Recruiting from the public brought in at least one new volunteer , but did not decrease the probability of an abundance of new volunteers. In contrast, University volunteers did not report any statistical significance in either model. This could mean that public sources attract individuals more than groups of new volunteers.

4. Time-based factors

Finally, morning sessions increased the probability of having at least one new volunteer, while having the sessions on weekends and an increase in the number of hours increased the probability of an abundance of new volunteers. While these may show preference for new volunteers - that new volunteers would like to start their day with volunteering and would like to volunteer when they have time - another reason for the last observation on an increased number of hours in the session could be that there was a lack of existing volunteers willing to take up sessions that were very long, hence the need to recruit more new volunteers.

5. Programmes in need of new volunteers

Volunteers in programmes Arts Inspire, Confidence Building, Food Meetup, Outdoor Exploration, Excursion, Photography Enthusiasts, Book Group, Sports, Visit were serving more than 1 beneficiary on average in during their sessions and could consider finding new volunteers using the factors that increased the probability of recruiting new volunteers.