RESEARCH DESIGN 3.1 Measurement of variables

2430 1990. This measurement relies on the analysis of income and sales variability as follows: Income smoothing index = CV I CV S Where: I = one-period change in income S = one-period change in sales CV j = coefficient of variation for variable j i.e. j‘s standard deviation divided by it‘s expected value Inco me smoothing is indicated by an index of less than 1. Eckel‘s index is developed specifically as a dichotomous measurement of income smoothing. Thus for the purpose of this study, the sample companies are classified as smoothers or non -smoothers, depending on whether the income smoothing index is less than 1 or more than 1, respectively. 2431 Four possible income smoothing objectives are examined in this study. They are income from operations, income before tax, income after tax and income attributable to shareholders.

3.2 Sample selection and source of data

The population of interest comprises companies listed, both on the main board and the second board, of the Bursa Malaysia Berhad previously known as Kuala Lumpur Stock Exchange KLSE as of 31st December 1990. The year 1991 was taken as the initial year as the study has employed a ten-year time series data collection that is from 1991 to 2000. This ten-year time frame was used by Barefield and Comisky 1972, with the justification to identify the variability and average absolute growth increments for companies that have opportunity to do income smoothing. This procedure is consistent with εoses‘s 1λ87, pg. γ6β suggestion that multi-period studies capture achievements of smoothing, whereas one- period studies reflect attempts to smooth. A 2432 total of 161 companies have been taken as samples for the purpose of this study Please refer to Table 1.

3.3 Statistical Method

Logit analysis is used in a multivariate setting to investigate the factors associate d with income smoothing. The logit model is considered appropriate because the dependent variable is a dichotomous variable and the independent variables are either intervally or nominally measured. The logit model can be expressed as follows: Smooth i =  +  1 NED i +  2 DUAL i +  3 REM i +  4 INST i +  5 OWN i +  6 CCC i +  7 AUD i +  8 IND i +  9 PROP i +  10 SIZE i +  11 PROFIT i +  i