Types Of Data Collection And Analysis For Research

Primary Data Collection

Data collection is one of the most important part of every research. There are broadly two types of data which are used for the research. The first one is the primary data, which is also known as the first hand data. The primary data are those data which are collected by the researcher as per the requirement of the research. The major techniques used for data collection are the primary survey (which is used to collect the quantitative data), personal interview (which is used to collect the qualitative data). To collect the quantitative data the close end questionnaire is used whereas for the qualitative data collection the open ended questionnaire is used.
The second type of the data which is used for the research is the secondary data. This type of data is collected by someone else for different purpose. The major sources of the secondary data includes the published journals, books, government data center, company reports etc. The secondary data is cheap as compared to the primary data(Cierniak and Reimann, 2011; Mangal and Mangal, 2013; Rajasekar, Philominathan and Chinnathambi, 2013).

For the current research the secondary data has been used. The data has been collected for 60 different firms situated in different countries around the world. The companies has been selected from the master data set and the selection of the companies from the master data was random. The random sampling has been used, so the results from the analysis can be generalized. Once the sample was selected the data cleaning process has been conducted which included identifying the missing values and the also the identification of the outliers. The missing values were recoded so that the results are not affected. Once the data cleaning process was completed the data was exported to SPSS and the further analysis was conducted(Armstrong, 2012; George, Seals and Aban, 2014; Monem A Mohammed, 2014).

Data analysis has been conducted in two different ways. In the first section the results from the descriptive analysis has been shown and in  the next section the results from the inferential analysis has been shown which includes the chi square test, correlation analysis and the regression analysis.

Descriptive results for the continuous variable are shown in the table below. Various measures of the central tendencies and the skewness kurtosis of the variables have been included in the descriptive analysis(M, no date; Hancock, 2009; Macdonald and Headlam, 2010).

Results from the descriptive statistics of the disclosure score shows that the mean disclosure score is 88 with standard deviation of 22.66. The standard deviation indicates that there is no high variation in the variables and most of the data set lies around the mean value. The minimum and the maximum value of the disclosure score are the 0 and 100 which are also the range for the disclosure score. The Skewness of the variable is negative indicating that the variable is negatively skewed.  Descriptive statistics of other variables are also shown in the table above and the results for those values can also be explained in similar way. Generally the median value is considered as the more accurate measure of central tendency than the mean value. This is because the extreme values in the series affect the mean value but do not affect the median value. The skewness and kurtosis helps to explain the distribution of the series.

Secondary Data Collection and Analysis

The histogram of the disclosure score shows that most of the values lies to the right of the mean value and the value of skewness is also negative.

The histogram of IV1 indicates that the variable is normally distributed as most of the variable lies near the mean value.

For the IV4 most of the values lies to the left of the mean value and the variable is not normally distributed. 

For IV6 also the most of the values lies to the right of the mean value of the series.

Categorical variables

Since the numerical representation of the categorical variable are not much of use the graphical representation has been shown

In this section the results from the inferential analysis has been discussed. The inferential analysis has been conducted using different statistical techniques such as chi square test, correlation analysis and regression analysis.

The chi square test is used to test whether there is statistically significant difference in the observed value and the expected value.  This also test whether there is statistical difference in the values for different categories.

In this section the results from the inferential analysis has been discussed. The inferential analysis has been conducted using different statistical techniques such as chi square test, correlation analysis and regression analysis.

The chi square test is used to test whether there is statistically significant difference in the observed value and the expected value.  This also test whether there is statistical difference in the values for different categories.

As shown in the table above the Pearson chi square value of 100.296 with 90 degree of freedom is not statistically significant. This is because the two tail significance value is more than 0.05. So the null hypothesis cannot be rejected. In other words the disclosure score is not significantly different for different countries(ACCA, 2009).

Similarly the chi square test for different countries show that the Pearson chi square value of 136.082 with 126 degree of freedom is not statistically significant at 5 % as the significance value is more than 0.05. So, in this case also the null hypothesis cannot be rejected. In other words the disclosure score is not different for firms in different countries.

The correlation analysis has been conducted to examine the relationship between two variable and also the magnitude and direction of the relationship can be examined from correlation analysis(Wooldridge, 2002; Imbens and Wooldridge, 2009). For the current research also the correlation analysis has been performed and the results are shown in the table below.

Data Analysis Techniques

As the table shows the dependent variable is positively correlated with IV1, IV3, IV4 and IV5 whereas the negative correlation exist between disclosure score and IV6 and IV7. The positive correlation means that if one variable increase the other variable also increases. On the other hand the negative correlation shows that if one variable increases the other variable decreases.  Furthermore the value of the correlation coefficient shows the magnitude of the relation. The correlation coefficient close to -1 indicates strong negative relationship, whereas the correlation value of close to +1 indicate positive strong positive correlation.

The regression analysis is used to show the impact of the independent variables on the dependent variable. In this case also the regression analysis has been conducted to examine the impact of independent variables on the disclosure score, which is the dependent variable for the current case(Skrivanek, 2009; Armstrong, 2012; Monem A Mohammed, 2014). 

As the results shows the value of R square is 0.086 which indicates that the independent variables included in the data set is able to explain less than one percent of the variation in the dependent variable. The R square is very low in this case, however the overall goodness of fit of the model cannot be examined only on the basis of the R squared.

Furthermore the results from the ANOVA test shows that the F value is 0.689. However the significance value is more than 0.05 which indicates that the cumulative impact is also not significant.

Lastly the results from the regression coefficient show that the IV1, IV4, IV5 and IV7 have positive impact on the disclosure score. On the other hand independent variable such IV3 and IV6 shows negative impact on the dependent variable.  However in terms of statistical significance only IV5 shows statistically significant results at 10 %. Other variables do not show statistically significant results.

Hypothesis 1:

Null hypothesis: There is no significant relationship between IV1 and the disclosure score.

Alternative hypothesis: There is significant relationship between IV1 and the disclosure score.

Since the coefficient of IV1 is not statistically significant the null hypothesis cannot be rejected.

Hypothesis 2:

Null hypothesis: There is no significant relationship between IV3 and the disclosure score.

Alternative hypothesis: There is significant relationship between IV3 and the disclosure score.

Since the coefficient of IV1 is not statistically significant the null hypothesis cannot be rejected

The results from the analysis shows that the independent variables included in the data set do not show statistically significant impact on the disclosure score. This may be because there are other factors which affect the disclosure score other than the variables included in the current model. One of the major factor may be the corporate governance factors which are not included in the current case.

Descriptive Analysis Results

In the current case only the quantitative analysis has been conducted and there is no qualitative analysis. Furthermore the sample size for the current case is only 60 which is not very high. Furthermore there are no corporate governance variables included in the analysis which may have significant impact on the disclosure score(Manaseer, Al-Hindawi and Al-Dahiyat, 2012; Servaes, 2013; Singh, 2014; Aldosari and Atkins, 2015; Paul, Ebelechukwu and Yakubu, 2015). In addition there are limitations with respect to time and cost associated with the research.

Future research can be conducted taking into consideration the qualitative analysis also which will give more detail idea about the disclosure score and the how the firms determine the carbon emissions. Similarly similar research can be conducted taking into account a larger sample size and firms from other countries. 

References

ACCA (2009) High-impact Sectors: the Challenge of Reporting on Climate Change.

Aldosari, A. and Atkins, J. (2015) ‘A study of corporate social responsibility disclosure practices in Saudi Arabia’, in British Accounting and Finance Association Conference. Manchester.

Armstrong, J. S. (2012) ‘Illusions in regression analysis’, International Journal of Forecasting, 6, pp. 689–694.

Cierniak, G. and Reimann, P. (2011) Specification of Research Strategy and Methodology.

George, B., Seals, S. and Aban, I. (2014) ‘Survival analysis and regression models’, NCBI, 21(4), pp. 686–694.

Guo, Y. (2014) Climate Change Disclosure?: Determinants and impact. University of Hawai.

Hancock, B. (2009) An Introduction to Qualitative Research. Leicester.

Imbens, G. W. and Wooldridge, J. M. (2009) ‘Recent Developments in the Econometrics of Program Evaluation’, Journal of Economic Literature, pp. 5–86.

M, M. (no date) Research Design in Qualittative/ Quantitative / Mixed Methods.

Macdonald, S. and Headlam, N. (2010) Research Methods Handbook. Manchester.

Manaseer, A., Al-Hindawi and Al-Dahiyat (2012) ‘The impact of corporate governance on the performance of Jordanian banks’, European Journal of Scientific Research, 67(3), pp. 349–359.

Mangal, S. K. and Mangal, S. (2013) RESEARCH METHODOLOGY IN BEHAVIOURAL SCIENCES. PHI learning pvt. ltd.

Monem A Mohammed (2014) ‘Survival Analysis By Using Cox Regression Model with Application’, International journal of scientific & technology, 3(11).

Paul, G. D., Ebelechukwu, E. C. and Yakubu, S. (2015) ‘Impact of Corporate Governance on Financial Performance of Microfinance Banks in North Central Nigeria’, Interantional Journal of Humanities Social Sciences and Education, 2(1), pp. 153–170.

Rajasekar, S., Philominathan, P. and Chinnathambi, V. (2013) Research methodology. Tamilnadu India.

Servaes, H. (2013) ‘The impact of corporate social responsibility on firm value: The role of customer awareness.’, Management Science, 59(5).

Singh, S. (2014) Impact of corporate social responsibility disclosure on the financial performance of firms in UK. University of Twente.

Skrivanek, S. (2009) ‘The Use of Dummy Variables in Regression Analysis’. Morestream, LLC.

Winn, M. et al. (2011) ‘Impacts from climate change on organizations: a conceptual foundation’, Business Strategy and the Environment, 20(3), pp. 157–173.

Wooldridge, J. M. (2002) Econometric Analysis of Cross Section and Panel Data, booksgooglecom. MIT Press. doi: 10.1515/humr.2003.021.

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