Psychological Statistics 2

• A branch of mathematics that involves data collection, analysis, and presentation.

  Allow us to make sense of and interpret a

  great deal of information.

  statistics
• Researches in Psychology typically

  begin with a general

  question about a

  specific group or groups

  of individual.
• Composed of the entire group of individuals that the researcher wants to study.

  population
• A small group of

  individuals selected

  from a population,

  usually picked to

  describe the population.

  sample
• whats the population? All College Students vs. 1st year college students only

  All College Students
• A characteristic or condition that is not

  constant – it can change or has different

  values for different individuals.

  Example: height, age, gender, marital status

  variable
• Any variables where the data

  represent amounts.

  Example: height, age, income, class size

  quantitative variable
• Separated by indivisible categories.

  Countable in a finite amount of time.

  Example: number of cars in a parking lot,

  number of attendees at a seminar

  discrete variable
• Would literally take forever to count.

  Can take an uncountable set of values.

  Example: weight, height, length, time, and

  temperature

  continuous variable
• They don’t have a numeric value and so

  cannot be added, subtracted, divided or

  multiplied.

  They have no order.

  Example: gender, race, eye color, blood type

  nominal
• Contains things that you can place in order.

  Example: hottest to coldest, richest to poorest,

  ranking data by 1st, 2nd, 3rd, and so

  ordinal
• Has ordered numbers with meaningful

  divisions.

  Levels of Measurement

  Example: a difference of 10 degrees between

  90 and 100 means the same as 10 degrees

  between 150 and 160.

  interval
• Hold no absolute zero and can represent

  values below zero.

  Example: the temperature can be below 0

  degrees Celsius and into negative

  temperatures.

  interval
• Has all the characteristics of an interval

  scale.

  Has an absolute zero or character of origin.

  Example: height and weight cannot be zero or below

  zero.

  ratio
• Statistical procedures that are used to

  summarize, organize and simplify data.

  What is the average attention span of Grade 1

  students?

  descriptive statistics
• Allow us to compare samples and make

  generalizations about the populations

  where they come from.

  How does the average attention span of Grade 1

  students compare against Grade 2 students?

  inferential statistics
• Involves the collection, analysis, and

  interpretation of data gathered from

  random samples of a population under

  study.

  sampling
• Uses some form of random selection of

  research participants from accessible

  population.

  Only random samples permit true statistical

  inference and foster external validity.

  probability sampling
• simple random sampling

  
• stratified random sampling

  
• cluster random sampling

  
• It does not involve the use of

  randomization to select research

  participants.

  Consequently, research participants are

  selected because of convenience or

  access.

  non-probability sampling
• convenience sampling

  
• purposive sampling

  
• quota sampling

  
• snowball sampling

  
• A way of presenting data that makes the pattern

  of the data easier to see.

  After the scores are ordered, you can condense

  the data into a frequency distribution – a table in

  which all of the scores are listed along with the

  frequency with which each occurs.

  frequency sampling
• Class Interval – group of scores

  Class limit – the end numbers of the class interval

  Class frequency – the number of scores falling in

  each class interval

  Class size – the difference between the upper limit

  of the class and the preceding class

  Class size = Upper limit – lower limit

  Class mark – the midpoint of a class interval

  Class mark = (Upper limit + Lower limit ) / 2
• A representative number that characterized

  the “middleness” of an entire set of data.

  The three measures of central tendency are

  the mean, the median, and the mode.


  measures of central tendency
• The most commonly

  used measure ofcentral tendency.

  Add all of the scores

  together and divide the

  sum by the total

  number of scores.

  mean
• Used in situations in

  which the mean might

  not be representative

  of a distribution.

  Used when there are

  extreme scores in your

  sample.

  median
• if Median = 4th data value + 5th data value/2 then what is the median

  12, 18, 16, 21, 10, 13, 17, 19

  
  

  =16.5
• It is the score in a

  distribution that

  occurs with the

  greatest

  frequency.

  mode
• In skewed distribution, especially

  distributions for continuous variables,

  there is a strong tendency for the

  mean, median and mode to be located

  predictably different positions.
• positively skewed

  
• negatively skewed

  
• Provides a quantitative measure of the

  differences between scores in a distribution.

  Tells whether the scores are clustered close

  together or are spread out over a large distance,

  and how much distance to expect between one

  score and another.

  variability
• different measures of variability: The distance covered by the scores in a

  distribution, from smallest score to the largest score.

  range
• range= Xmax - Xmin : What is the range of the following set of scores? 2, 8, 9, 11, 13, 15


  13
• Most used and most important measure of variability.

  Provides a measure of the standard, or average,

  distance from the mean, and describes whether the

  scores are clustered closely around the mean or are

  widely scattered.

  standard deviation