Good day! I am an undergrad and we are currently conducting a research with mixed approach. It has 2 phases, starting with the qualitative phase to quantitative phase. In quali, we plan to get the general internal resources (GIR) needed by PLCs to comply with sustainability disclosure requirements. After we get the GIR based on their responses, we will then ask the SMEs to get the level of existence of such GIR on them when they're preparing sustainability disclosure requirements (this is the quantitative phase). We're having a hard time because we do not know if T-test, Correlational, or Regression is applicable because our variables for quanti are independent.
Shane Duguran For your quantitative phase, several statistical treatments could be applicable:
1. Descriptive Statistics: Summarize the level of existence of GIR in SMEs.
2. T-Test: Compare the means if you have two groups (e.g., SMEs with high vs. low compliance).
3. Correlation Analysis: Measure the strength and direction of the relationship between GIR levels and sustainability disclosure compliance.
4. Regression Analysis: Predict the extent of sustainability disclosure compliance based on the level of GIR in SMEs.
Each of these methods will provide different insights depending on your specific research questions and data structure.
References:
1. Creswell, J. W., & Clark, V. L. P. (2017). Designing and Conducting Mixed Methods Research. Sage Publications.
2. Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
Beyond AI answers, what is your research question for the quantitative phase? Your sample(s)? Answer these two questions, and you can decide what to conduct.
Good day!
It sounds like you have a well-structured mixed-methods research design. To address your concern about which statistical analysis is appropriate for your quantitative phase, let's break down the steps and considerations:
1. Qualitative Phase:
- Objective: Identify the General Internal Resources (GIR) needed by PLCs to comply with sustainability disclosure requirements.
- Method: Collect qualitative data (e.g., interviews, focus groups) and analyze them to identify key internal resources.
2. Quantitative Phase:
- Objective: Assess the level of existence of these GIRs in SMEs when they are preparing sustainability disclosure requirements.
- Data Collection: Use a survey or questionnaire to quantify the existence of each identified GIR in SMEs.
Determining the Statistical Analysis:
Nature of Your Data:
- Independent Variables: These are likely the GIRs identified from the qualitative phase.
- Dependent Variables: If you have a specific outcome related to the sustainability disclosures (e.g., quality or comprehensiveness of the disclosures), this would be your dependent variable.
Analysis Options:
Given that your independent variables (GIRs) are potentially numerous and independent, you have a few options:
A. T-Tests
- Use When: Comparing means between two groups.
- Example: If you had a binary grouping variable (e.g., high vs. low performing SMEs), you could use t-tests to compare the existence levels of GIRs between these two groups.
B. Correlation
- Use When: Assessing the strength and direction of the relationship between two continuous variables.
- Example: You can use correlation to see if there is a relationship between the level of a specific GIR and the quality of the sustainability disclosures.
C. Regression Analysis
- Use When: You want to understand the relationship between one or more independent variables and a dependent variable.
- Example: Multiple regression could be used if you have one dependent variable (e.g., quality of sustainability disclosures) and several independent variables (levels of different GIRs).
Steps for Regression Analysis:
Recommendations:
- Start Simple: Begin with correlation analysis to understand the relationships between individual GIRs and your outcome variable.
- Move to Regression: If you have a clear outcome variable, use multiple regression to see the combined effect of all GIRs on the outcome.
Example Steps:
Good day! I have read all your responses, and I am very grateful for sharing your insights with us! Thank you so much for enlightening me; this is very much appreciated.
In the quantitative phase of data analysis, various statistical treatments can be applied to uncover patterns, trends, and relationships within the data. Descriptive statistics such as mean, median, mode, and standard deviation can provide a summary of the central tendency and dispersion of the data. Inferential statistics, including hypothesis testing and regression analysis, can be utilized to make predictions or test relationships between variables. Additionally, factor analysis or cluster analysis can help identify underlying structures or groupings within the data. By employing these statistical treatments effectively, researchers are able to draw meaningful conclusions from their quantitative data and make informed decisions based on robust evidence. It is important to choose the appropriate statistical techniques based on the research objectives and the nature of the data being analyzed to ensure accurate interpretations and reliable findings.
For the quantitative phase of your mixed-methods research, the appropriate statistical analysis would depend on the specific research questions and the nature of your variables.
Since you mentioned that the variables in the quantitative phase are independent, a few options you could consider are:
1. **T-test**:
- If you want to compare the level of existence of the GIR between two groups (e.g., companies that comply with sustainability disclosure requirements and those that don't), you could use a t-test to determine if the mean levels are significantly different.
2. **Correlation Analysis**:
- If you want to explore the relationships between the level of existence of different GIR, you could use correlation analysis to examine the strength and direction of the associations.
- This would be appropriate if you want to understand how the various GIR are related to each other, without necessarily establishing causality.
3. **Multiple Regression**:
- If you want to investigate the influence of multiple independent variables (the level of existence of different GIR) on a dependent variable (e.g., the overall compliance with sustainability disclosure requirements), you could use multiple regression analysis.
- This would allow you to understand the relative importance of each GIR in predicting the dependent variable, while controlling for the effects of the other GIR.
It's worth noting that the choice of statistical analysis will also depend on the scale of measurement for your variables (e.g., interval, ordinal) and the assumptions of the specific statistical tests.
Additionally, if you have a small sample size or non-normal distribution of your data, you may need to consider non-parametric tests, such as the Mann-Whitney U test or Spearman's rank correlation, instead of the parametric tests mentioned above.
I would recommend consulting with a statistician or a research methods expert to help you determine the most appropriate statistical analysis for your research questions and data. They can provide guidance on the assumptions, limitations, and interpretations of the different statistical techniques.