Non-progression by Student Characteristics

A key purpose of this analysis is to allow identification of student cohorts most at risk of non-progression. This section allows the user to delve deeper and investigate the non-progression rates of new entrants by various student characteristics that may be associated with the non-progression rates at HEI, Field of Study and NFQ Level. The user can filter by year of entry, HEI and NFQ Level, as well as Gender, Domiciliary, age group (i.e. Mature/Non-Mature entrant) and Deprivation Index Score category (the metric for socio-economic status) separately for each year from 2015/16 to 2019/20.

There are three tables where the user can investigate non progression rates:

  • Table 1 – Non-progression rates by Leaving Certificate Points and Field of Study
  • Table 2 – Non-progression rates by Leaving Certificate Points and Socio-Economic Status (DIS)
  • Table 3 – Non-progression by Student Characteristics – Interactive Pivot

Exploring the Data
If we select level 6 and 7 only from the NFQ Level filter, we see higher non-progression rates for those with lower points on entry, particularly in ICT and Engineering, Manufacturing and Construction (while the rates are higher in Education and Social Sciences, these cohorts have smaller numbers of entrants at these NFQ levels and Leaving Certificate Points ranges) (2019/20 data). The non-progression rates are much lower for those level 6/7 entrants with higher leaving cert points (0% non-progression, or 100% progression in some cases), although it is important to note that there are relatively small numbers of entrants to Level 6/7 programmes with higher leaving cert points.

Males have a higher non-progression rate than females, particularly at L6 and L7. If we cross tabulate by Gender and NFQ Level (in Table 3), we can see that the overall rate for females is 7 %, against 11% for males, but at Level 6, the rates are 13% and 18% for females and males respectively. The gap is narrower at Level 8, at 8% for females and 11% for males. If we cross tabulate by Gender and Field of Study (again, Table 3), we can see that there are two fields of study (excluding Generic programmes) in which there are equally low rates of non-progression for males and females; for all other fields of study, the non-progression rate for females is lower than that for males. This appears to be the case even in fields where there is a much higher proportion of males than females, such as Engineering and ICT, albeit with lower numbers of female entrants. For example, the non-progression rates for females and male entrants to Information & Communications Technologies (ICT) courses is 13% and 11% respectively at Level 8, and 21% and 26% for (combined Level 6 and 7). Cross tabulating by Leaving certificate points and gender shows that the non-progression rate for males is higher in the lower points, although the disparity lessens and the rates converge at the higher ranges. In years prior to 2019/20, the rate was higher for males across all disciplines and LC points ranges.

At a glance, an association between leaving cert points (where data is available) and non-progression rates can be seen: 3% rate overall for those in each of the two highest ranges against 31% and 28% for those in the two lowest ranges. The variation is even sharper in some Fields of Study, for example, 38% and 39% for entrants into Information and Communications Technologies (ICT) in the lowest leaving cert points range against 2% and 4% for those in the top two ranges (2019/20 data).

In addition, there would appear to be an association between socio-economic status (as identified by Deprivation Index Scores (DIS) grouping) and non-progression rates, although less pronounced than that for gender. Table 2 (unfiltered) shows the non-progression rate for entrants from affluent backgrounds is at 7%, below the overall rate of 9%, whereas the rate is 12% for entrants from disadvantaged backgrounds. Again however, the association between LC points and non-progression rates is evident, with the non-progression rates relatively consistent (and relatively low) at the higher leaving certificate ranges, but higher at lower points ranges.

Looking at 2018/19 entrants: For all four DIS groupings, the non-progression rates at Level 6/7 could be up to double those at Level 8, with non-progression rates of the disadvantaged cohort at 16%, while that of the affluent cohort at 16%, compared to 8% and 13% respectively at Level 8. If we exclude level 8 from the analysis and look at level 6 and 7 only (for example, by deselecting Level 8 from the NFQ filter, Table 2), we can see higher rates of non-progression for entrants from the disadvantaged cohort with lower leaving certificate points (46% in 155 to 200 points range, against 19% for affluent entrants in the same points range). Note that only 10% of the entrants to Level 6 and 7 courses in 2018/19 were from the affluent groupings, and there are relatively few entrants to level 6 and 7 courses form the higher Leaving Certificate points ranges. For the 2019/20, the non-progression rates fell overall, and while the rates in general are lower for the affluent than disadvantaged groups, the gap has narrowed slightly at level 7 and 8 and significantly at level 6.

Non-progression rates of Student Cohorts with Small Numbers of New Entrants

The user should be cautious when drawing conclusions based on cohorts of students with small numbers of new entrants. In relation to non-Irish students, mature students and gender minorities, be aware of the following:

  • The gender breakdown of the student population is 53%/47% female/male (2019/20). Less than 1% of the new entrants in 2018/19 or 2019/20 identified as neither Male or Female.
  • In 2018/18 and 2019/20, only 6% and 5% of new entrants respectively were non-Irish.
  • 7% of entrants in both 2018/19 and 2019/20 were mature entrants (i.e. 23 or over in the year of entry). Note that mature years does not necessarily mean the student entered on the basis of mature year. To view those students who have gained entry to Higher Education on the basis of mature years, the user will need to select the required option under the Entry Basis menu (this option is only available from 2018/19 and later years).
  • Socio-economic status is measured by Deprivation Index Scores. This data is only available for the years 2017/18 forward. For all three years since since 2017/18, 10% of new entrants came from a Disadvantaged background, with 15% to 16% coming from an affluent background (This data is unavailable for Trinity College Dublin for 2017/18).

The user can use the tooltip function (activated by hovering the mouse over the table cells) to see where the cohort has less than 25 new entrants.

Table 1 - Non-progression rates by LC Points and Field of Study

Table 2 - Non-progression rates by Leaving Cert Points and DIS Category

Table 3 - Non-progression by Student Characteristics - Interactive Pivot

The following chart has an additional level of user interactivity. The user can select the row and/or column from the two drop down menus above the chart and filter data using the drop down menus below.

Go to next section: Trends in Non-Progression Rates: 2015/16 to 2019/20

ISCED is the International Standard Classification of Education – Field of Study
Further information on classifications here.
For further reading on the use of Deprivation Index Scores for socio-economic profiling, please see HEA (2019) A Spatial & Socio-Economic Profile of Higher Education Institutions in Ireland

Our use of cookies

We use necessary cookies to make our site work. We'd also like to set optional analytics cookies to help us improve it. We won't set these optional cookies unless you enable them. Using this tool will set a cookie on your device to remember your preferences.

For more detailed information about the cookies we use, see our Privacy Policy page

Necessary cookies

Necessary cookies enable core functionality such as security, network management, and accessibility. You may disable these by changing your browser settings, but this may affect how the website functions.

Analytics cookies

We'd like to set Google Analytics cookies to help us to improve our website by collecting and reporting information on how you use it. The cookies collect information in a way that does not directly identify anyone.