Chapter 1 Flashcards
Statistics
refers to a general field of mathematics, statistical procedures
refers to a set of mathematical procedures for organising, summarising, and interpreting information
purposes of statistics:
- Statistics are used to organise and summarise the information so that the researcher can see what happened in the research study and can communicate the results to others
- Statistics help the researcher to answer the questions that initiated the research by determining exactly what general conclusions are justified based on the specific results that were obtained
- Statistical procedures help ensure that the information or observations are presented and interpreted in an accurate and informative way
- statistics provide researchers with a set of standardized techniques that are recognized and understood throughout the scientific communit
Population
the set of all the individuals of interest in a particular study.
- Because populations tend to be very large, it usually is impossible for a researcher to examine every individual in the population of interest. Therefore, researchers typically select a smaller, more manageable group from the population and limit their studies to the individuals in the selected group. In statistical terms, a set of individuals selected from a population is called a sample
Sample
a set of individuals selected from a population, usually intended to represent the population in a research study.
A sample is intended to be representative of its population, and a sample should always be identified in terms of the population from which it was selected
Relationship between a population and a sample
- The population - all of the individuals of interest ->
- The sample - is selected from the population ->
- THE SAMPLE - The individuals selected to participate in the research study ->
- The results from the sample are generalised to the population
Variable
A variable is a characteristic or condition that changes or has different values for different individuals
Data
measurements or observations
Data set
A collection of measurements or observations
Datum
a single measurement or observation and is commonly called a score or raw score
Parameter
is a value, usually a numerical value, that describes a population. A parameter is usually derived from measurements of the individuals in the population.
for example, the average score for the population—is called a parameter
Statistic
is a value, usually a numerical value, that describes a sample. A statistic is usually derived from measurements of the individuals in the sample.
- A characteristic that describes a sample is called a statistic. Thus, the average score for a sample is an example of a statistic
Typically, the research process begins with a question about a population parameter. However, the actual data come from a sample and are used to compute sample statistics
Descriptive statistics
are statistical procedures used to summarize, organize, and simplify data
- Often the scores are organized in a table or a graph so that it is possible to see the entire set of scores. Another common technique is to summarize a set of scores by computing an average
Inferential statistics
consist of techniques that allow us to study samples and then make generalizations about the populations from which they were selected.
Sampling error
is the naturally occurring discrepancy, or error, that exists between a sample statistic and the corresponding population parameter
correlational method
Two different variables are observed to determine whether there is a relationship between them
- When the data from a correlational study consist of numerical scores, the relationship between the two variables is usually measured and described using a statistic called a correlation
Limitations of the correlational method
The results from a correlational study can demonstrate the existence of a relationship between two variables, but they do not provide an explanation for the relationship - does not give a cause-and-effect
Comparing Two (or More) Groups of Scores: Experimental and Nonexperimental Methods
- In this situation, the relationship between variables is examined by using one of the variables to define the groups, and then measuring the second variable to obtain scores for each group.
- we examine descriptive statistics that summarize and describe the scores in each group and we use inferential statistics to determine whether the differences between the groups can be generalized to the entire population
Experimental and non-experimental methods
- The results from an experiment allow a cause-and-effect explanation
- A nonexperimental study does not permit a cause-and effect explanation
The experimental method
In the experimental method, one variable is manipulated while another variable is observed and measured. To establish a cause-and-effect relationship between the two variables, an experiment attempts to control all other variables to prevent them from influencing the results.
To accomplish this goal, the experimental method has two characteristics that differentiate experiments from other types of research studies:
- Manipulation: The researcher manipulates one variable by changing its value from one level to another
- Control: The researcher must exercise control over the research situation to ensure that other, extraneous variables do not influence the relationship being examined.
There are two general categories of variables that researchers must consider:
- Participant Variables: These are characteristics such as age, gender, and intelligence that vary from one individual to another. Whenever an experiment compares different groups of participants (one group in treatment A and a different group in treatment B), researchers must ensure that participant variables do not differ from one group to another.
- Environmental Variables: These are characteristics of the environment such as lighting, time of day, and weather conditions. A researcher must ensure that the individuals in treatment A are tested in the same environment as the individuals in treatment B.
three basic techniques to control other variables
- random assignment - each participant has an equal chance of being assigned to each of the treatment conditions. The goal is to distribute the participant characteristics evenly between the two groups so that neither group is noticeably smarter (or older, or faster) than the other. Random assignment can also be used to control environmental variables.
- matching - to ensure equivalent groups or equivalent environments. For example, the researcher could match groups by ensuring that every group has exactly 60% females and 40% males.
- holding them constant - For example, in the video game violence study discussed earlier (Polman et al., 2008), the researchers used only 10-year-old boys as participants (holding age and gender constant). In this case the researchers can be certain that one group is not noticeably older or has a larger proportion of females than the other.
Independent variable
The independent variable is the variable that is manipulated by the researcher. In behavioral research, the independent variable usually consists of the two (or more) treatment conditions to which subjects are exposed. The independent variable consists of the antecedent conditions that were manipulated prior to observing the dependent variable
Dependent variable
The dependent variable is the one that is observed to assess the effect of the treatment
Control condition
Individuals in a control condition do not receive the experimental treatment. Instead, they either receive no treatment or they receive a neutral, placebo treatment. The purpose of a control condition is to provide a baseline for comparison with the experimental condition.
Experimental condition
Individuals in the experimental condition do receive the experimental treatment.
Experiment
a real experiment must include manipulation of an independent variable and rigorous control of other, extraneous variables