Correlation Research & Inferential Statistics. by Atheer L. Khamoo & Wissam A. Askar. Table of Content. Definition Purpose Independent and dependent variables Scatter plot Correlation coefficient Range of correlational coefficient Types of correlational study.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Correlation Research&Inferential Statistics
Atheer L. Khamoo & Wissam A. Askar
The goal of correlational research is to find out whether one or more variables can predict other variables. Correlational research allows us to find out what variables may be related. However, the fact that two things are related or correlated does not mean there is a causal relationship. It is important to make a distinction between correlation and causation. Two things can be correlated without there being a causal relationship
- 1 to + 1 and is identified by r.
• each data point on the scatterplot indicates the score
on both variables
• GPA and Study hours per week
• 3.0 18
• 3.5 21
• 2.4 12
• 1.8 10
• 2.7 11
• 5 data points, one for each student
In case of nonlinear relationship the value of r will be close to 0.
1. Naturalistic Observation2. The Survey Method 3. Archival Research
Involves observing and recording the variables of interest in the natural environment without interference or manipulation by the experimenter.
population by examining the sample
1. Probability sampling
It involves sample selection in which the elements are drown by chance procedures.
2. Nonprobability sampling
It includes methods of selection in which elements are not chosen by chance procedure.
1. Simple random sampling
2. Stratified sampling
3. Cluster sampling
4. Systematic sampling
It comprise the following steps:
1. Define the population
2. List all members of the population
3. Select the sample by employing a procedure where sheer chance determines which members on the list are drawn for the sample.
Population consists of a number of subgroups, or strata, that may differ in the characteristics being studied.
A common way of selecting members for a sample population using systematic sampling is simply to divide the total number of units in the general population by the desired number of units for the sample population.
For example, if you wanted to select a random group of 1,000 people from a population of 50,000 using systematic sampling, you would simply select every 50th person, since 50,000/1,000 = 50.
1-Convenience sampling, It is a sampling method in which units are selected based on easy access/availability.
which is regarded as the weakest of all sampling procedures, involves using available cases for study.
2-Purposive sampling _ also referred to as judgment sampling elements judged to be typical, or representative, are chosen from the population.
3-Quota Sampling involves selecting typical cases from diverse strata of a population. The quotas are based on known characteristics of the population to which you wish to generalize.
How large should a sample be?
Other things being equal a larger sample is more likely to be a good representative of the population than a smaller sample. However, the most important characteristic of a sample is its representativeness, not its size. A random sample of 200 is better than a random sample of 100, but a random sample of 100 is better than biased sample of 2500000.
The researcher has observed only a sample and not the entire population.
Sampling error is “the difference between a population parameter and a sample statistic”.
A statistical hypothesis is an assumption about a population parameter. This assumption may or may not be true. Hypothesis testing refers to the formal procedures used by statisticians to accept or reject statistical hypotheses.
The null always says there is norelationship or difference.
H0 (null) is that mean1=mean2, meaning the meanscores are equal OR thedifference between the mean scores is zero .
a = probability you will reject (e.g., 1% chance)
a = probability you will not reject (e.g., 99%)