PSYCH 101 - Psychological Statistics

• Quantitative Research Methods

  • Can be used to interpret subjective and wordy data
  • Translates "words" into numbers or gives numerical meaning to said data
• Statistics


  Can be used to scientifically back up psychology-related studies
• Descriptive statistics


  Simply describes and expounds the gathered data by summarizing, comprehending, and analyzing it
• Inferential statistics


  • Derives and provides conclusions from the described data (from descriptive statistics)
  • Extremely crucial & aims to draw conclusions & make predictions about a population from a sample
• Calculating average

  1. If even numbers, get the average of the two middle numbers

  2. If none, write nothing
• Population


  • More of characteristics/interests
  • Usually geographical strata
• Sampling


  1. Randomly select a group/cluster as the sample

  2. There is a quota to be reached
• Statistics


  the branch of mathematics that deals with collecting, organizing, analyzing, interpreting, and presenting data
• 0.05 Significance


  P-value of 0.05: 5% chance that the null hypothesis is true and observed results are due to chance. Statistically significant at the 5% level indicates a 5% chance that the observed results are due to random chance
• Measures of Central Tendency

  1. Mean (average)
  2. Median (middle value)
  3. Mode (most frequently occurring value in the dataset)
• Descriptive Statistics


  Summarizes and describes main features of a dataset: central tendency, dispersion, and shape
• Measures of Dispersion


  1. Range
  2. Variance
  3. Standard Deviation
• Mean


  The calculated average of all values; sensitive to outliers
• Median


  The middle value when ordered from smallest to largest; less affected by outliers
• Mode


  The most frequently occurring value
• Range


  difference between the maximum and minimum values in a data set
• Variance


  average of the squared differences from the mean

  
  

  is a measure of how the data points deviate from the mean.

  
  

  in probability theory and statistics, it is a measure of a random variable's statistical dispersion, reflecting how much its values frequently deviate from the anticipated value

  
• Standard Deviation


  the square root of the variance

  
  

  indicates how widespread the data is from the mean.

  
  

  A low standard deviation implies that the data is tightly packed around the mean,

  
  

  whereas a high standard deviation suggests that the data is more spread out

  
• Weighted Mean


  A technique to calculate the average value of data, giving more importance to some values than others

  
• Probability Sampling

  • refers to the selection of a sample from a population, when this selection is based on the principle of randomization, that is, random selection or chance
  • is more complex, more time-consuming and usually more costly than non-probability sampling
• Average of Weighted Means

  
  

  formula for the total average of all weighted mean

  
• Non-probability sampling


  • does not involve random selection and does not guarantee that every member of the population has an equal chance of being included in the sample
  • relies on the researcher's judgment or convenience and may lead to biased samples, limiting the generalizability of findings to the broader population
• Advantages of Non-probability sampling

  • least expensive and time consuming
  • sampling units are accessible, easy to measure and cooperative
• Disadvantages of Non-probability Sampling


  • selection bias
  • may not be representative of the whole population
  • cannot generalize the population
• Advantages of Probability Sampling


  • easily understood
  • results may be projected to target population
  • different techniques can help
  • statistics tools are applicable
• Disadvantages of Probability Sampling


  • may be time consuming, difficult, and costly to do
  • can result in large samples
  • may not result in as precise or representative sample, and may need a more complex technique
• Simple Random Sampling


  A method where each element in the population has an equal chance of being selected.
• Stratified Random Sampling


  A method where the population is divided into subgroups and then a random sample is taken from each subgroup.
• Cluster Sampling


  A method where the population is grouped into clusters and then a random sample of clusters is selected.
• Systematic Random Sampling


  A method where the population is arranged in a specific order, and then every Nth item is selected.
• Convenience Sampling


  A method where the sample is chosen because it is easily accessible or readily available.
• Snowball Sampling


  A method where existing members of the population are asked to refer friends or acquaintances who fit the desired criteria.
• Quota Sampling


  A method where the sample is divided into subgroups and then a fixed number of participants are selected from each subgroup.
• Judgment or Purposive Sampling


  A method where the sample is selected based on specific characteristics or criteria which lie on the researcher's judgment and expertise
• Simple Random Sampling

  
• Stratified Random Sampling

  
• Cluster Sampling

  
• Systematic Random Sampling

  
• Quota Sampling

  
• Snowball Sampling