Research Flashcards
Strengths of statistics
Using statistics in research and analysis has several strengths:
- Objectivity: Statistics provide a quantitative basis for understanding social phenomena, which helps to minimize bias. This objectivity allows researchers to draw conclusions based on data rather than personal opinions or subjective interpretations.
- Generalizability: Statistical data can often be generalized to larger populations when collected from representative samples. This makes it possible to infer trends and patterns across different groups, enhancing the applicability of findings.
- Clarity and Precision: Statistics allow for clear and precise communication of complex information. Numerical data can be presented in various formats (charts, graphs, tables) that make it easier to understand and interpret.
- Trend Analysis: Statistics enable researchers to identify trends over time, helping to track changes and developments in various fields. This can be particularly useful in areas like economics, health, and social behavior.
- Support for Hypotheses: Statistical analysis can provide strong support for or against specific hypotheses, helping to validate theories and models within research.
Overall, the use of statistics enhances the rigor and reliability of research findings, making them more credible and useful for decision-making.
Weaknesses of Statistics
Using statistics also has several weaknesses:
- Misinterpretation: Statistics can be easily misinterpreted or manipulated to support a specific agenda. Without proper context or understanding, data can lead to misleading conclusions.
- Over-reliance on Quantitative Data: Focusing solely on statistical data may overlook qualitative aspects of a situation. Important nuances and human experiences can be lost when relying only on numbers.
- Sampling Issues: If the sample used for statistical analysis is not representative of the population, the results may be biased or inaccurate. Poor sampling methods can lead to significant errors in conclusions.
- Complexity: Advanced statistical methods can be complex and difficult to understand for those without a strong background in statistics. This can create barriers to effective communication of findings.
- Context Dependency: Statistics often require context to be fully understood. Without understanding the background or the conditions under which the data was collected, the results may not be meaningful.
These weaknesses highlight the importance of using statistics carefully and in conjunction with other forms of analysis.
What is quanitative data?
numerical (quantity - a lot of data can be inferred)
What is qualitative data?
Data that uses words - e.g questionnaires (good quality)
Strengths of Secondary Data
Using secondary data has several strengths:
- Cost-Effective: Secondary data is often less expensive to obtain than primary data because it has already been collected and published by other researchers or organizations. This can save both time and resources.
- Time-Saving: Since secondary data is already available, researchers can quickly access and analyze it, allowing for faster research processes compared to collecting primary data.
- Large Datasets: Secondary data often includes large datasets that would be difficult or impossible for an individual researcher to gather. This can enhance the robustness of the analysis.
- Historical Analysis: Secondary data allows researchers to examine trends over time by accessing historical data that may not be feasible to collect anew.
- Diverse Sources: There is a wide range of secondary data sources available, including government reports, academic articles, and datasets from organizations, providing a variety of perspectives and information.
Overall, secondary data can be a valuable resource for research, helping to supplement primary data and provide context for findings.
Weaknesses of Secondary data
Using secondary data also has several weaknesses:
- Relevance: The data may not be perfectly aligned with the specific research question or context. It was collected for a different purpose, so it might not address the current needs or objectives of the researcher.
- Quality and Reliability: The quality of secondary data can vary significantly. If the original data collection methods were flawed or biased, it can affect the validity of the analysis based on that data.
- Lack of Control: Researchers have no control over how the data was collected, which can lead to issues with accuracy, completeness, and consistency. This can be problematic if the data does not meet the required standards for the current analysis.
- Outdated Information: Secondary data may be outdated, especially in rapidly changing fields. Relying on old data can lead to conclusions that do not reflect the current situation.
- Limited Context: Secondary data often comes without detailed context, making it difficult to understand the circumstances surrounding its collection. This can hinder proper interpretation and analysis.
These weaknesses highlight the importance of critically evaluating secondary data sources and considering them in conjunction with primary data when conducting research.
Positivesa and negatives of primary research
Positives
Reliable
Relevant
Scientific
Preferred by positivists
Negatives
Costly
Time consuming
Sampling needs to generalize
Positives and negatives of decondary research
Positives
Large scale
Cheap
Quick
Only way to study historical issues
Preferred by interpretivists
Negatives
Reliability could be questioned
Interpretation issues.
How to do simple random sampling
Use a computer to generate a random sample, everyone has as much chance as the other
How to do systematic random sampling
Researcher uses a system to pick the participants e.g every tenth name on register
How to do stratified random sampling
Involves picking people from different groups within population
How to do snowball sampling
Researcher selects one person, then asks them to put them in touch with other people
How to do quota sampling
Each interviewer had an exact number of people from categories they need, e.g females, teenagers
How to do purposive sampling
Sample or collected according to a known characteristic.
What does probability sampling mean?
Random.
6 Types of research methods
Postal questionnaires
Structured interviews
Informal interviews
Group interviews
Participant observation
Official statistics
