Stats NZ has a new website.

For new releases go to

www.stats.govt.nz

As we transition to our new site, you'll still find some Stats NZ information here on this archive site.

  • Share this page to Facebook
  • Share this page to Twitter
  • Share this page to Google+
Appendix 1: Methodology

Data from Te Kupenga

The data for this report comes from Te Kupenga 2013. Te Kupenga is a survey of Māori well-being. It includes measures based on the Māori perspective of cultural well-being, including wairuatanga (spirituality), tikanga (Māori customs and practices), whanaungatanga (social connectedness), and te reo Māori. The survey also contains general social and economic well-being measures, such as paid and unpaid work, civil participation, and self-assessed health status. These measures give an overall picture of the social, cultural, and economic well-being of Māori in New Zealand in 2013.

Te Kupenga 2013 (Data quality section) has more information.

Te Kupenga used a self-rated question to assess a respondent’s ability to speak in te reo Māori. Respondents were asked to place themselves in one of five categories as follows:

  1. very well (I can talk about almost anything in Māori)
  2. well (I can talk about many things in Māori)
  3. fairly well (I can talk about some things in Māori)
  4. not very well (I can only talk about simple/basic things in Māori)
  5. no more than a few words or phrases.

Respondents who rated their ability to speak te reo Māori between 1 and 4 were asked more detailed questions about the environments in which they used te reo Māori. Those who rated their ability as ‘no more than a few words or phrases’ were not asked these detailed questions.

Respondents were asked about their te reo Māori usage both inside and outside the home. Respondents could choose from:

  • no Māori
  • some Māori
  • Māori equally with English (or another language)
  • mostly Māori
  • all Māori.

Logistic regression used to inform analysis

We used logistic regression in this analysis to establish what measures had strong independent relationships with ability in and the use of te reo Māori. This logistic regression was used only to decide which measures would be included in the report and these measures were presented using descriptive statistics. Results from the regression analysis form no part of the main report. But the results are presented here for your information.

As the five possible response options for respondents to all of the te reo Māori questions have a logical order, we used a cumulative multinomial logistic regression. The advantage of using regression analysis is that it holds other factors constant, while looking at the association between the likelihood of speaking or using te reo Māori and the factor of interest.

A cumulative multinomial logistic regression describes the relationship between the lowest against all higher categories of the te reo Māori variables and the relationship between the next-lowest category and all higher categories. Because the relationship between all pairs of groups is the same, there is only one set of coefficients. Therefore, results from the model refer to the likelihood of having a higher ability to speak te reo Māori and using more te reo Māori inside or outside the home.

Model of te reo Māori

While the ability in and use of te reo Māori for Māori is no doubt a complex process, Te Kupenga contains many measures that would allow us to look at what is involved in this process. To restrict the number of these measures that we put into the model, we began with a simple framework that might help explain how a high ability or use in te reo Māori is achieved. This framework has the following themes:

  • culture and identity
  • education
  • whānau
  • socio-economic
  • social
  • demographic.

We selected a number of measures from Te Kupenga to represent each of these themes in the logistic regression model.

Interpreting odds ratios

We present the results of the logistic regression analysis in appendix 2 in the form of odds ratios. An odds ratio is the odds of an event happening divided by the odds of the opposite event happening. For example, suppose that 400 females spoke te reo Māori and 200 did not. The odds of a female speaking te reo Māori are 400/200 = 2, or 2 to 1. This means the chances of a female speaking te reo Māori are reasonably good. To give another example, suppose that 500 males spoke te reo Māori and 1,000 did not. The odds of a male speaking te reo Māori would be 500/1,000 = 0.5 or 1 to 2. The chances of their speaking te reo Māori are therefore significantly lower than for females.

For continuous explanatory variables (for example age), an odds ratio of greater than 1 indicates a higher likelihood of speaking te reo Māori as the value of the explanatory variable increases and an odds ratio less than 1 indicates a lower likelihood.

For categorical explanatory variables, the odds ratio compares the likelihood of speaking te reo Māori compared with the reference category. An odds ratio greater than 1 indicates a higher likelihood of speaking te reo Māori compared with the reference group, while an odds ratio of less than 1 indicates a lower likelihood.

  • Share this page to Facebook
  • Share this page to Twitter
  • Share this page to Google+
Top
  • Share this page to Facebook
  • Share this page to Twitter
  • Share this page to Google+