Statistical research stages

Statistical research stages

1. Step 1: Planing

   • To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research
     • The goal of the research is often to investigate a relationship between variables within a population. You start with a prediction, and use statistical analysis to test that prediction
       • A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.
         • While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.
           • For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain

2. Step 2: Collect data from a sample

   • Sampling for statistical analysis
     • Probability sampling: every member of the population has a chance of being selected for the study through random selection.
     • Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.
   • Create an appropriate sampling procedure
     • Based on the resources available for your research, decide on how you’ll recruit the data.( Experimental-Correlational)
   • Calculate sufficient sample size
     • A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.
       • Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
       • Statistical power: the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
       • Expected effect size: a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
       • Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

3. Step 3: Organization

   • By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.
     • Calculate measures of central tendency
     • Calculate measures of variability
     • There are various ways to inspect your data
       • Organizing data from each variable in frequency distribution tables.
       • Displaying data from a key variable in a bar chart to view the distribution of responses.
       • Visualizing the relationship between two variables using a scatter plot.

4. Step 4: Analysis

   • Estimation
     • A point estimate: a value that represents your best guess of the exact parameter/ An interval estimate: a range of values that represent your best guess of where the parameter lies.
   • Hypothesis testing
   • Parametric tests

5. Step 5: Interpret your results

   • In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.
   • Effect size
   • Decision errors
   • use previous research to continually update your hypotheses based on your expectations and observations.