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# chapter 1-3

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Carleton University

Psychology

PSYC 2002

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Winter

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Wednesday, January 8, 2014
PSYC 2002-B Introduction to Statistics in Psychology:
Chapter 1:An Introduction to Statistics and Research Design
Week One Readings:
A1-7
pp. 1-20 Chapter 1
Statistics is a research tool for evaluating data:
Data: is a set of scores or measurements
Statistics: Anumerical fact derived from data
- Statistics are used to evaluate the results of experiments; did the data support the
hypothesis?
- Quantify behaviour about data and its theoretical implications
Two branches of statistics:
1. Descriptive statistics
- Organizes, summarizes and communicates a group of numerical observations.
- Summary of measurements (ex: average)
2. Inferential statistics
- Uses sample data to make general estimates about the larger population.
- Enable inferences to be drawn from data
** Descriptive statistics summarize numerical information about a sample.
Inferential statistics draw conclusions about the broader population based on numerical
information from a sample.
Systematic versus random effects:
- If two groups show a difference, is the difference attributable to a systematic
difference or is the difference attribute to chance?
- If the individuals were randomly assigned to the groups then it is possible to
choose between these alternatives
Generalization: would a set of results hold if another group of participants were used?
o Sample- subset of individuals from a larger group
o Population- the set of all individuals that have some characteristic
Distinguishing between a sample and a population:
Asample: is a set of observations drawn from the population of interest.
Apopulation: includes all possible observations about which we’d like to know
something.
Generalization and random sampling:
- Generalization from sample to population requires a random sample o Each member of the population has an equal chance of being selected
o Selection of any one member is independent of the selection of the other
members
Scales of Measurement:
- Type of observations determines appropriate type of statistic:
o Nominal
o Ordinal
o Interval
o Ratio
Nominal and Ordinal Scales:
1. Nominal scale: observations categorized (ex: types of professions, political
affiliation)
2. Ordinal scale: observations rank ordered (ex: Maclean’s ranking of universities
2013) (aka rank-ordered variable)
Interval and Ratio Scales:
3. Interval scale: all categories have same size (Celsius temp)
4. Ratio scale: interval scale with an absolute zero point (kelvin temp, response
time, percent correct)
How to transform observations into variables:
1. Variables: are any observations of a physical, attitudinal, or behavioural
characteristic that can take on different values.
2. Discrete observation: can take on only specific values (ex: whole numbers) no
other values can exist between these numbers.
Discrete and Continuous Variables:
Discrete variables: indivisible categories (ex: number of participants in a condition,
number of conditions in an experiment)
Continuous variables: infinite number of possible values (ex: time, length)
Statistical notation:
N or n = number of scores, observations, ect.
X Y Z = variable names
** We conduct research to see if the independent variable predicts the dependant variable
** Agood variable is both reliable and valid
**Correlation research: when possible, researchers prefer to use an experiment rather
than a correlational study. Experiments use random assignment, which is the only way to
determine if one variable causes another. Chapter 1: Key terms
Nominal variable: is a variable used for observations that have categories, or names, as
their values.
st nd
Ordinal variable: is a variable used for observations that have rankings (ex: 1 , 2 , ect)
3. Continuous observation: can take on a full range of values (ex: numbers out to
several decimal places) an infinite number of potential values exist.
Interval variable: is a variable used for observations that have numbers as their values;
the distance (or interval) between pairs of consecutive numbers is assumed to be equal.
Ratio variable: is a variable that meets the criteria for an interval variable but also has a
meaningful zero point.
Variables and Research
Levels: are discrete values of conditions that a variable can take on.
An independent variable: Have at least two levels that we either manipulate or observe
to determine its effects on the dependent variable.
Adependant variable: is the outcome variable that we hypothesize to be related to or
caused by changes in the independent variable.
Aconfounding variable: is any variable that systematically varies with the independent
variable so that we cannot logically determine which variable is at work, also called a
Confound.
Reliability: refers to the consistency of a measure.
Validity: refers to the extent to which a test actually measures what it is intended to
measure.
Hypothesis testing: is the process of drawing conclusions about whether a particular
relation between variables is supported by the evidence.
An operational definition: specifies the operations or procedures used to measure or
manipulate a variable.
Acorrelation is an association between two or more variables.
In random assignment: every participant in a study has an equal chance of being
assigned to any of the groups, or experimental conditions in the study. An experiment: is a study in which participants are randomly assigned to a condition or
level of one or more independent variables.
In a between-groups design: participants experience one, and only one level of the
independent variable.
In a within-groups design: all participants in the study experience the different levels of
the independent variable, also called a repeated measures design.
Friday, January 10, 2014
PSYC 2002-B Introduction to Statistics in Psychology:
Chapter 2: Frequency Distributions
Week one Readings:
A1-7
pp. 1-20 Chapter 1
pp. 21-42 Chapter 2
Note:
Pass Workshop:
Wed 6-7:30 LAA204
Thurs 2:30-4 CB 2302
Frequency Distributions:
Frequency distribution: number of individual scores associated with each category in
some type of measurement system; arguably, simplest type of descriptive statistic
** Afrequency table shows the pattern of the data by indicating how many participants
had each possible score. The data in a frequency table can be graphed in a frequency
histogram or a frequency polygon.
Goal- organize data
o Scores ranked lowest to highest
o Same scores grouped together
Benefits of frequency distributions:
- Can see how all scores from group are distributed:
o How many high
o How many low
o How they are arranged
Frequency Distribution tables: components
- X = score
- F = number of scores for a given sco

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