The three information analysis techniques are qualitative analysis, quantitative analysis, and predictive analysis. For instance, qualitative analysis involves coding and classifying data, quantitative analysis uses statistical models, and predictive analysis forecasts future trends through regression analysis.
Qualitative Analysis Method
By deeply understanding the meaning and complexity behind data, it provides more comprehensive insights. For example, when assessing a country’s political stability, it is not only necessary to pay attention to official statistical data but also to deeply study the country’s historical background, cultural factors, and the personal characteristics of its leaders.
Harvard University professor Joseph Nye once pointed out that soft power plays an increasingly important role in modern international relations, and the analysis of such concepts is precisely the strength of the qualitative analysis method.
During the Cold War, the CIA successfully predicted the internal contradictions and potential crises of the Soviet Union through qualitative analysis of the personal backgrounds of Soviet leaders, policy evolution, and public speeches.
By coding and classifying data, complex information becomes more organized. For example, when analyzing a country’s political risk, hundreds of news reports, academic articles, and official documents are coded to identify common risk factors such as corruption, concentration of power, and social discontent.
The qualitative analysis method also emphasizes the strict evaluation of the diversity and reliability of information sources. For example, when analyzing political events, not only are official statements referenced but also information from unofficial channels such as social media comments and independent media reports.
The flexibility of the qualitative analysis method allows it to be adjusted according to specific situations and needs. For instance, in the face of sudden events or complex issues, the analysis framework and methods can be quickly adjusted.
Quantitative Analysis Method
By using statistical and mathematical models, complex information is transformed into specific numbers and charts. For example, evaluating a country’s economic strength involves using specific data such as GDP growth rate, unemployment rate, and inflation rate for analysis. Harvard economist Kenneth Rogoff pointed out, “Quantitative data provides an indispensable foundation for the formulation of economic policies.”
The quantitative analysis method is equally important at both micro and macro levels. For example, when assessing a politician’s public support, multiple opinion poll data are referenced, calculating the average support rate and standard deviation, and predicting future support changes through trend analysis. The New York Times once reported that the accuracy of quantitative analysis in election predictions is as high as 85%.
By comparing and verifying information from different data sources, the objectivity and reliability of the analysis results are ensured. For example, when analyzing a country’s military budget, data from the Stockholm International Peace Research Institute (SIPRI) on global military spending are referenced and combined with the country’s Ministry of Defense reports to draw more accurate conclusions.
Renowned political scientist Samuel Huntington once pointed out, “Data analysis is a key tool for understanding political phenomena.” For instance, in studying democratic transitions, collecting and analyzing political system data from dozens of countries at different historical periods identifies key factors affecting the democratization process.
Not only does it improve the accuracy of analysis, but it also enhances the efficiency of analysis. For example, using computer software for data processing and analysis can significantly shorten analysis time and enhance the timeliness of analysis.
By using the same data sets and analysis methods, research results can be repeated to verify their reliability. For example, in analyzing the impact of global climate change on international relations, using global temperature and carbon dioxide emission data, a predictive model is established through regression analysis.
Predictive Analysis Method
By conducting a detailed analysis of historical data and current trends, it provides scientific predictions of future events. According to Gartner reports, 85% of companies were using some form of predictive analysis to aid decision-making by 2020.
It helps decision-makers foresee potential crises and opportunities. From 2010 to 2020, the accuracy of conflict early warning systems using predictive analysis increased by nearly 30%.
For example, when predicting a country’s economic growth, using key economic indicators such as GDP growth rate, unemployment rate, and inflation rate, predictive accuracy reaches up to 90% through regression analysis and time series analysis.
For instance, the famous economist John Maynard Keynes proposed in “The General Theory” that by analyzing historical economic fluctuations, future economic trends can be predicted.
During the 2020 COVID-19 pandemic, the UK successfully reduced the shortage rate of medical resources during the peak of the epidemic by 25% using predictive analysis.
Using multiple data sources, such as government statistics, corporate financial reports, and social media data, data cleaning and preprocessing ensure the accuracy and consistency of data. For example, when predicting election results, analysts may use a large amount of voter survey data and predict different candidates’ support rates and winning probabilities through logistic regression models.
The advantage of predictive analysis lies in its flexibility and broad applicability. For example, when predicting the impact of climate change on the global economy, using meteorological data and economic data, the prediction error of grain yields can be reduced to within 5% through multiple regression analysis.