PSYCHSTATS

Inappropriate use of independent-samples t-test

Comparing students' attitude change between the start and end of their degree.

Alternative hypothesis

Represents the researcher's prediction or expectation

T-test

Assesses the means of two independent groups

Requirement for using a t-test

The sample must be normally distributed.

Correlation coefficient close to one

Strong linear relationship between the two variables

Use "r"

APA format reporting for Pearson's product-moment

Purpose of correlation analysis

Measure the strength and direction of a relationship between variables.

1.00

Maximum possible value for a correlation coefficient

Positive

Expected correlation between a child's age and vocabulary

Important statistics when interpreting an independent sample t-test

• Descriptive statistics

• significance level

• t-value

Sequence of steps in hypothesis testing

• Formulate hypothesis

• collect data

• analyze data

• draw conclusions.

Example of a categorical variable

Gender

P-value

The probability of finding statistical significance

Identifying significant differences in independent t-test outpu

Look at the p-value

Interpretation of a Pearson test statistic of .876 with P < 0.01

Significant, strong, positive relationship.

Purpose of statistical tests

To test the null hypothesis.

Types of t-tests

• One-sample t-test

• independent two-sample t-test

• paired sample t-test

ANOVA

Used when there are more than two groups

High standard deviation in a graph

Indicates data is dispersed over a wide range of values

Correlation

Describes the direction and magnitude of a relationship between two variables

One-tailed test appropriateness

Identified by the alternative hypothesis.

Pearson's product-moment relationships

Assess only linear relationships

Null hypothesis for testing correlation

The two variables of interest are not correlated

Result interpretation

If r=0.46, p=0.78 at 0.05 level, reject the alternative hypothesis

Data type for Pearson's analysis (excluding dichotomous variable)

Interval or ratio

Dependent variable in a study

How long it took the participant to press the button when the light came on.

Null hypothesis in a study about control over a plant

Control over a plant will have no impact on the number of health complaints.

Two-tailed hypothesis test

Requires a smaller sample size than a one-tailed test.

Type I error

The error of rejecting H0 when H0 is true.

Low standard deviation in a graph

Looks closely clustered around the mean

Significance level reporting in APA format

0.000 as P < 0.05

Identification by Pearson's product-moment

Whether there is a relationship between variables.

Random assignment

Reduces the likelihood of confounding variables.

Example of an alternate hypothesis

There is a significant gender difference in the mean scores of mechanical aptitudes.

Positive correlation

When one variable decreases, the other also decreases.

Negative correlation

When one variable increases, the other decreases

Sampling error

The tendency for a sample to differ from the population due to chance.

Correlation coefficient of -1

Perfect negative relationship between variables.

Normal distribution shape

Bell-shaped.

Alternative hypothesis in a study comparing life satisfaction

There is a significant difference between younger and older adults on life satisfaction

Standard error of estimate

Zero when the correlation between a predictor and criterion is +1.00 or -1.00.

Simple random sampling

In a simple random sample,

every member of the population has an equal chance of selected. Your sampling frame should include the whole being population. To conduct this type of sampling, you like random number generators or other techniques that can use tools are based entirely on chance.

Systematic sampling

Systematic

is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

Stratified sampling

involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample. To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role).

Cluster sampling

also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

Convenience sampling

This method is dependent on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street.

Quota sampling

the selection of members in this sampling technique happens based on a pre-set standard.

In this case, as a sample is formed based on specific attributes, the created sample will have the same qualities found in the total population.

Judgment or Purposive Sampling

are formed by the discretion of the researcher. Researchers purely consider the purpose of the study, along with the understanding of the target audience

Snowball sampling

can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people.