Comparative study Research. definitely and

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1. Cross-Sectional COMPARATIVE R ESEARCH Presented by: Group 4

2. CROSS SECTIONAL RESEARCH Cross sectional study design is a type of observation study or descriptive research that involves analysing information about population at a specific point in time. This design measures the prevalence of an outcome of interest in a defined population. It provides a snapshot of the characteristics of the population at a single point in time. It can be used to access the prevalence of outcomes and employers determine relationships among variables and generate hypothesis about casual connections between vector to be explored in experimental design.

3. PURPOSE Typically, these studies are used to measure the prevalence of health outcomes and describe the characteristics of a population. In this study, researchers examine a group of participants and depict what already exists in the population without manipulating any variables or interfering with the environment. Cross-sectional studies aim to describe a variable, not measure it. They can be beneficial for describing a population or “taking a snapshot” of a group of individuals at a single moment in time. Cross-sectional studies are also unique because researchers are able to look at numerous characteristics at once.

4. Three Main Types of T-test Advantages Disadvantages Simple and inexpensive Does not help determine cause and effect Minimal room for error Report Biosis probable Multiple variables and outcomes can be research and compared at once. The timing of the Snapchat is not always representative. The data can be a starting point for future research. It cannot be used to analyse behaviour over a period of time.

5. TYPES OR METHODS OF CROSS SECTIONAL RESEARCH DESIGN Cross sectional studies can be categorised based on the nature of the data collection and type of the data being sought. 1. Descriptive 2. Analytical 3. Community survey/population based survey 4. Prevalence study 5. Occupational or environmental

6. TYPES OR METHODS OF CROSS SECTIONAL RESEARCH DESIGN Descriptive To describe the characteristics of a population. Analytical to investigate associations between variables Community survey to gather information on a population or a subset Prevalence study To determine the proportion of a population with the specific characteristics ,condition or disease Occupational or environmental To examine the effects of certain occupational or environmental exposures

7. TYPES OF ANALYTICAL RESEARCH There are further two types of analytical research. 1. Comparative research compares two or more groups to explain differences between them on a variable or variables of interest. 2. Correlational research studies the relationship between two or more variables in one group.

8. Steps to conduct a comparative research study

9. Steps to conduct a comparative research study Define the research question and goals : Clearly state the specific question you want to answer through the comparative study, outlining the key aspects you want to compare and the intended outcomes. Select the subjects for comparison : Identify the relevant entities (individuals, groups, organizations, policies, etc.) that will be included in the study, ensuring they are comparable based on your research question. Develop a theoretical framework : Establish a conceptual framework to guide your analysis, identifying key variables and their relationships within the context of your comparison. Gather data : Collect data from each subject using appropriate research methods like surveys, interviews, document analysis, or observation, ensuring consistency in data collection across all subjects.

10. Draw conclusions and recommendations Choose a comparison method : Decide on the most suitable method for analysing your data, such as side-by-side comparison, matrix analysis, or statistical analysis depending on the nature of your data. Analyse the data : Perform a thorough analysis of the collected data, identifying similarities and differences between the subjects while considering the variables and theoretical framework. Interpret the results : Explain the findings from your analysis, considering the implications of the comparisons and providing insights into the observed patterns. Draw conclusions and recommendations : Summarize the key findings of your comparative study, highlighting the most important insights and providing actionable recommendations based on the comparisons made.

11. Nature Data The nature of data in comparative studies can be qualitative or quantitative Qualitative data: Descriptive data that can be transformed into numerical values. Qualitative case studies are common in anthropology, sociology, and cultural geography. Quantitative data: Numerical data that can be counted or measured and given a numerical value. Statistical methods like regression, cluster analysis, factor analysis, and comparing means can be used to analyze quantitative data. Categorical Data : Data that can be grouped into categories or classes, such as demographic information (e.g., age, sex, location) Ordinal : Data that can be ranked or ordered, but the intervals between ranks may not be equal (e.g., Likert scales). Nominal : Data that can be labelled or named, but there is no inherent order or ranking (e.g., brand names, colours).

12. Draw conclusions and recommendations Choose a comparison method : Decide on the most suitable method for analysing your data, such as side-by-side comparison, matrix analysis, or statistical analysis depending on the nature of your data. Analyse the data : Perform a thorough analysis of the collected data, identifying similarities and differences between the subjects while considering the variables and theoretical framework. Interpret the results : Explain the findings from your analysis, considering the implications of the comparisons and providing insights into the observed patterns. Draw conclusions and recommendations : Summarize the key findings of your comparative study, highlighting the most important insights and providing actionable recommendations based on the comparisons made.

13. Advantages of comparative studies Broader understanding : By comparing different cases, researchers can gain a more comprehensive understanding of a phenomenon, identifying patterns and variations across different contexts. Theory building and testing : Comparative studies allow researchers to test hypotheses against diverse data sets, strengthening or refining existing theories. Identifying unique factors : Comparing different systems can reveal unique characteristics or factors that contribute to specific outcomes in each case. Policy implications: Findings from comparative studies can inform policy decisions by highlighting successful strategies and areas for improvement across different settings. Cross-cultural insights: When comparing different cultures or societies, researchers can gain valuable insights into diverse perspectives and practices.

14. Limitations of comparative studies Data comparability issues: Challenges arise when attempting to compare data collected using different methods or across different contexts, potentially leading to inaccurate interpretations. Selection bias: Choosing which cases to compare can introduce bias, potentially skewing results if the selection is not representative. Equivalence issues: Ensuring that concepts and variables are measured and interpreted consistently across different contexts can be difficult. Complexity of analysis: Analysing complex phenomena with multiple interacting variables across different cases can be challenging. Limited generalizability : Depending on the case selection, findings might not be easily generalizable to other contexts. Contextual factors : Failing to adequately account for the unique historical and social context of each case can lead to misinterpretations.

15. Associating testing COMPARATIVE R ESEARCH Presented by: Group 4

16. Limitations of comparative studies

17. Assumptions are made Continues Data (Ratio Scale) Normally Distributed Quantitative Data Linearity Population parameter is known Independence Large sample >30 More powerful No assumptions made regarding population Nominal or Ordinal scale Skewed Qualitative Data Median Population parameter is unknown Small sample size Less powerful Parametric Test VS Non-parametric Test

18. Is Your Data Normally Distributed?

19. Skewness and Kurtosis should be near zero. Shapiro-Wilk and Kolmogorov-Smirnov should be non-significant.

20. T-Test

21. T-Test The t test is a statistical test that tells you how significant the differences between group means are. It lets you know if those differences in means could have happened by chance. The t test is usually used when data sets follow a normal distribution but you don’t know the population variance. For example, a drug company may want to test a new cancer drug to find out if it improves life expectancy. In an experiment, there’s always a control group (a group who are given a placebo, or “sugar pill”). So while the control group may show an average life expectancy of +5 years, the group taking the new drug might have a life expectancy of +6 years. It would seem that the drug might work. But it could be due to a fluke. To test this, researchers would use a Student’s t-test to find out if the results are repeatable for an entire population. In addition, a t test uses a t-statistic and compares this to t-distribution values to determine if the results are statistically significant.

22. T-Score The t score is a ratio between the difference between two groups and the difference within the groups. Larger t scores = more difference between groups Smaller t score = more similarity between groups Every t-value has a p-value to go with it. A p-value from a t test is the probability that the results from your sample data occurred by chance. P-values are from 0% to 100% and are usually written as a decimal (for example, a p value of 5% is 0.05). Low p-values indicate your data did not occur by chance. For example, a p-value of .01 means there is only a 1% probability that the results from an experiment happened by chance.

23. Three Main Types of T-test Independent Samples t-test An Independent Samples t-test compares the means for two groups Paired sample t-test A Paired sample t-test compares means from the same group at different times (say, one year apart). One sample t-test A One sample t-test tests the mean of a single group against a known mean. T-tests are a type of parametric method The choice of one-tailed versus two-tailed test depends on the research hypothesis. One-tailed tests are more powerful, meaning they can detect a statistically significant difference more easily.

24. When to use One Sample T-test? To compare the mean of a single sample to a known or hypothesized population mean when the population variance is unknown. The sample mean is normally distributed. When you want to verify a claim about the mean of a population. Example A clinical audit of 20 hospitals is conducted, and the mean quality score is compared to the national average quality score. a company wants to test the claim that their batteries last more than 40 hours. The one sample t-test, also known as the single sample t-test.

25. When to Choose a Paired T-test? Choose the paired t-test if you have two measurements on the same item, person or thing. Before-and-after measurements The data should include two continuous numeric variables that represent the paired variables for each subject. The paired t-test determines whether the mean difference of the pairs equals zero (no effect). Examples Knee MRI costs at two different hospitals. Two tests on the same person before and after training. Two blood pressure measurements on the same person using different equipment. A paired t-test is also known as a repeated measures t-test.

26. when to use Independent T-test? It is used to compare the means of two groups that are unrelated. The sample mean is normally distributed. The variances between groups are equal. You do not know the population mean or standard deviation. You have two independent, separate samples. Example Comparing first year graduate salaries based on gender Comparing the effectiveness of two painkillers An independent samples t-test, also known as a two-sample t-test.

27. Parameter Group 1 Group 2 t(40) p Cohen’s d M SD M SD

28. ANOVA

29. When to use One Sample T-test? ANOVA, which stands for Analysis of Variance, is a statistical test used to analyze the difference between the means of more than two groups. A one-way ANOVA uses one independent variable, while a two-way ANOVA uses two independent variables.

30. When to use a one-way ANOVA? Use a one-way ANOVA when you have collected data about one categorical independent variable and one quantitative dependent variable. The independent variable should have at least three levels (i.e. at least three different groups or categories). The null hypothesis (H0) of ANOVA is that there is no difference among group means. The alternative hypothesis (Ha) is that at least one group differs significantly from the overall mean of the dependent variable.

31. how to use one-way ANOVA ANOVA determines whether the groups created by the levels of the independent variable are statistically different by calculating whether the means of the treatment levels are different from the overall mean of the dependent variable. If any of the group means is significantly different from the overall mean, then the null hypothesis is rejected. ANOVA uses the F test for statistical significance. This allows for comparison of multiple means at once, because the error is calculated for the whole set of comparisons rather than for each individual two-way comparison (which would happen with a t test). The F test compares the variance in each group mean from the overall group variance. If the variance within groups is smaller than the variance between groups, the F test will find a higher F value, and therefore a higher likelihood that the difference observed is real and not due to chance.

32. Performing a one-way ANOVA Research Question Is there a significant difference in the Mean current salary of different categories of employees? OR Is there a significant difference in the Mean current salary of employees based on job category?

33. Research Hypothesis Alternative Hypothesis H1 : There is a significant difference in the Mean current salary of different categories of employees Null Hypothesis Ho : There is not a significant difference in the Mean current salary of different categories of employees

34. Two-way Anova A Two-Way ANOVA (Analysis of Variance) is a statistical test used to examine the effect of two independent variables (or factors) on a dependent variable, and to explore any interaction between these factors. It is particularly useful when you want to analyze how different groups respond to multiple factors simultaneously.

35. When to Use Two-way Anova When you have two categorical independent variables and a continuous dependent variable. When you want to assess not only the main effects of each factor but also if there is an interaction between them.

36. Wilcoxon signed rank test Non-Parametric Test Alternative to Paired Samples t-Test It is used to see a difference in two measurements taken from the same individuals or units Two variables (Ordinal, Interval or Ratio) (Two observations of each subject, often separated by time)

37. Performing wilcoxon signed rank test Research Question Is there a significant difference between beginning salary and current salary of employees? Research Hypothesis H1: There is a significant difference between theginning salary and current salary of employees Null Hypothesis Ho1: There is no significant difference between beginning salary and current salary of employees