Jinhua College student interviewing rice farmer. Photo credit: Zhu Zeng Rong (Zhejiang University)

Many thesis students who are using the survey as their data collection procedure, will often develop attitude, belief or perception statements on the topic of their thesis, such as mothers’ attitudes toward prenatal care, college students’ female beauty ideal, students’ perceptions of the credibility of TV ads on alcoholic drinks, etc.  Developing attitude statements shouldn’t end there. After the survey has been done and the attitude data encoded in a spreadsheet and uploaded in SPSS, a key step is to calculate the reliability score of the scale.

In surveys, scales are often used to measure respondent beliefs, perceptions and attitudes. Results are then used to make inferences and judgments on intervention points that need to be addressed in subsequent upscaling initiatives. When a scale is used, it is a standard procedure in the social sciences to determine its reliability. A popular measure of reliability is Cronbach’s alpha which determines the internal consistency or average correlation of items in a survey instrument to gauge its reliability. Internal consistency estimates how consistently the respondents have responded to the items within the scale. The closer the Cronbach’s alpha is to 1, the higher the internal consistency (Gliem & Gliem, 2003). In social science, the widely accepted alpha is 0.70 or higher for a set of items to be considered a scale.

Farmer interview in Lingui County, China

We calculated the Cronbach’s alpha of the belief and attitude scale on non-rice habitats used in a baseline survey instrument. As items were worded either positively or negatively, reverse scoring was done to negatively worded statements. The items within the scale were drawn from focus group discussions (FGDs) with rice farmers in China, Thailand and Vietnam. We used a Likert-type scale, as follows:  Definitely not true, in most cases not true, may be true, in most cases true and always true. The attitude scale on ecological engineering included these items:

Uses of non-rice habitat

  • Non-rice habitats are sources of pests and diseases.
  • Non-rice habitats are home to natural enemies.
  • All plants in the non rice habitats are of no use to me.
  • Bunds have some beneficial flowers that attract natural enemies.
  • Increase in wild flowers on bunds can reduce need for insecticide sprays.
  • Increase in beneficial flowers on rice bunds can help the bees.
  • Bees are important for pollinating fruit trees.
  • Increasing the beneficial flowers on rice bunds will make our fields beautiful.
  • Increasing beneficial flowers on rice bunds can bring better health to farmers and their families.
  • The natural enemies that the beneficial flowers bring can help prevent planthopper outbreaks.
  • Increasing the beneficial flowers on rice bunds can reduce the rat population.
  • If farmers increase the beneficial flowers on rice bunds all year round, the population of beneficial insects will increase.
  • Maintaining more beneficial insects will lower BPH population.
  • Replacing plants growing on the bunds with cash crops will give farmers more income.
  • Maintaining non-rice habitat

  • Increasing beneficial flowers on the bunds is a waste of time.
  • Increasing beneficial flowers on bunds is easy to do.
  • It is difficult to increase beneficial flowers on bunds because nearby paddy fields use herbicide.
  • We cannot increase the beneficial flowers on bunds because we burn our rice straw.
  • Increasing beneficial flowers on bunds is additional burden to farmers
  • It is difficult to increase beneficial flowers on bunds because farmers will step on them.
  • Farmers should not apply herbicide on the bunds.
  • Our bunds are narrow so there is no place for beneficial flowers.
  • Bunds in rice fields should not have any plants on them.
  • I am willing to try increasing beneficial flowers in the bunds to learn more about what they can do.
  • Cronbach’s alpha from survey data

    Using SPSS, reliability of the attitude scale was computed. Table 1 shows that the reliability tests performed on the belief and attitude data generated an acceptable Cronbach’s alpha of higher than 0.70, with relatively higher alpha for data from Vietnam farmers. This suggests that the items in the attitude scale are related enough to combine into an attitude scale or index. For each data set, only 16 to 22 out of 25 items were used in the analysis. Some items were excluded in the computation to increase the reliability coefficient.

    Table 1. Cronbach’s alpha obtained from attitude scale data in target countries.

    Country Cronbach’s alpha Number of items
    China (Jinhua and Lingui) 0.713 16
    Thailand 0.717 22
    Vietnam 0.745 22

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