Hypothesis Development THEORETICAL FRAMEWORK

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CHAPTER III RESEARCH METHODOLOGY

A. Scope of Research

This research use quantitative method by using eviews and SPSS application. The scope of the research is the annual report of all family owned business listed in Indonesian Stock Exchange IDX within 2010 - 2013. This research will examine the influence of good corporate governance and family involvement towards firm performance in family firm. Good corporate governance proxies by board of director BOD and size of committee audit CA while family involvement proxies by family ownership FO and family directormanager FDM and firm performance measure by Tobin’s Q.

B. Sampling Method

Sampling method is kind of method that take data from population. Sample is a part of the number, and characteristic possesed by the population. Research will not take all the populations, because due to limited funds, manpower and time. So, sample can represents the population Sugiyono, 2009:5. The sampling method used in this research is purposive sampling method. In purposive sampling this research use judgemental sampling by spesific criteria Sekaran, 2009:79. The sample specific criteria in this research are as follow: 1. The company has published its annual report publicly within period 2010-2013. 48 2. All family owned business listed in IDX that use IDR currency. 3. The company has the data of board size, size of audit committee, family ownership and family directormanager that will be tested in its annual report.

C. Data Collection Method

This research uses secondary data. This type of data obtained through research literatures which provide the theoretical basis and frame of mind to support primary data, as well as to support problem identification discussion Indriantoro and Supomo, 2009:5. Secondary data refer to information gathered from sources that already exist Sekaran and Bougie, 2010:81. This research data will be acquired from reports on the company’s website, annual reports of company or the media reports. Secondary data used in this study are the annual report of family owned business listed on the Indonesia Stock Exchange in 2010 - 2013.

D. Data Analyze Method

Research model parameters estimated using regression on panel data. Panel data is a combination of data cross cross section with time series data time series. Panel data introduced by Howles in 1950. The time series data usually includes one object eg stock prices, currency exchange rate, or the rate of inflation, but includes the miraculous period can be daily, monthly, quarterly, annually, etc.. The data consists of some or cross many objects, often called the respondent, eg a company 49 with multiple types of data eg, income, advertising costs, retained earnings, and the level of investment. Winarno, 2007:37. Gujarati 2003:637 stated that the panel data technique is to combine a clear cross-section data and time series, provides several advantages over the standard approach of cross saction and time series are: 1. By combining the data time series and cross saction, the panel data provides a more informative data, more varied, the level of collinearity between variables are low, a greater degree of freedom and more efficient. 2. By analyzing the data cross saction in some periods of the right panel data used in the study of dynamic changes. 3. Data pane is able to detect and measure the effect of which can not be observed through pure data or pure time series data is cross-saction. 4. Data panel allows studying the behavior of more complex models. eg economies of scale phenomena and technological change can be better understood with the data from the panel on the data purely cross-saction or pure time series data. 5. By because panel data related to individual, company, city, state over time, it will be heterogeneous in that unit. techniques for estimating panel data can incorporate heterogeneity explicitly for each individual variable specific. Selection on panel data methods performed using the Chow test, Hausman test, and the LM test. Panel data processing method will be described in the next 50 section. After the selection of methods and establishment of regression models are done, need to be tested the feasibility of a model in testing the hypothesis proposed. Some of these criteria are:

1. Classical Assumption Test

According to Ariefianto 2012:26 to estimate a multiple linear regression and inference procedures, requires the fulfillment of some classical assumptions. If this assumption is accepted, then the parameters are obtained by Unbiased Best Linear Estimator BLUE. Classical assumptions to be tested are: normality, multicollinearity, autocorrelation, heterocedasticity. a. Normality According to Gujarati 2004:147, there are three test that can be used to detect the normality problem, they are : 1 histogram of residual 2 normal probability plot NPP, 3 Jarque-Bera test. In eviews, the most commonly used test to detect the normality problem is Jarque-Berra test. The hypothesis for Jarque-Berra test for normality is : a. Null hypothesis Ho : residual are normally distributed. b. Alternative hypothesis H 1 : residual are not normally distributed. The requirement to reject the H0 is : H0 is rejected if the JB value is chi aquare table, or if the probability α 0.05. Meanwhile, H0 is accepted if the JB value is chi square table, the probability i α 0.05 based on Widarjono 2013:50. 51 b. Multicollinearity The independent variables which contain of multicollinearity make the coefficient of regression become unsuitable with the substances, thus the interpretation become inappropriate Fadhliyah, 2008 Multicollinearity can also be detected by making the correlation matrix between the independent variables and the significance of these correlations. A strong multicollinearity is worth 0.8 according to Ghozali 2013:82. c. Autocorrelation According to Ghozali 2013:138 autocorrelation test aims to test whether in a linear regression model is no correlation between confounding errors residuals in period t with the error in period t-1 previously. if there is a correlation, then there is a problem called autocorrelation. autocorrelation arises because observations over time are related to each other. This problem arises because the residuals are not free from the obsevation to another observation. How to detect the presence of autocorrelation: 1. Test Durbin-Watson DW-Test Test Durbin - Watson is only used for autocorrelation level one and requires the intercept in the regression model and no variable lag between independent variables. The hypothesis to be tested are: