Research in Psychology and Basic Concepts in Statistics

Bryan Q. EngayApplied Psychology Program

University of the Philippines Extension Program in Pampanga

Basic Concepts

• Population – entire collection of events/observations we are interested in studying.

• Parameter – measurement taken from the population.

• Statistic – measurements collected from the sample drawn from the population.

Basic Concepts

• Population – denoted by the Greek letter (mu) µ.

• *we must always clearly define the population we are interested in.

Basic Concepts

• Samples – observations drawn from the population and used to infer something about the characteristics of the population.

• Sampling frame – term used to refer to the population where samples are drawn from.

• Variable/s – things/observations/constructs that can be measured, controlled, or manipulated in research and can take on different values.

Basic Concepts

• Independent variable – variables that could have an effect on other variables and the one usually controlled in research.

• Dependent variable – variable affected by the variations in the independent variable.

• Independent variables may be either quantitative or qualitative and discrete or continuous.

Basic Concepts

• Dependent variables are generally, but not always, quantitative and continuous.

• Confounding variables – variables that can affect the outcome of the study, but which are not strictly part of the study. (have to be controlled in experiments)

• Discrete variables – variables that can take on only a limited number of values. (e.g., gender, high school class)

Basic Concepts

• Continuous variables – variables which can assume, at least in theory, any value between the lowest and highest points on the scale. (e.g., age and self esteem score)

• Quantitative data/Measurement Data – data which are results of any sort of measurement.

• Categorical data – (frequency data or qualitative data)

Basic Concepts

Levels of Measurement of Variables/Scales

1. Nominal – variables that allow for only categorization into named sets. Individual items belong to some distinctively different categories, but there is no quantifying or ranking of items.

Examples: male or female, Republican, Democrat, or Independent.

Basic Concepts

2. Ordinal – variables which are ranked in order in terms of which has less and which has more of the quality represented by the variable, but not how much more.

Examples: socioeconomic status, military ranks, Holmes and Rahe (1967) Scale of Life Stress.

3. Interval – not only give rank but also quantify and compare the size differences (interval) between.

Basic Concepts

Examples: temperature, Fahrenheit and Celsius

4. Ratio – variables that have an identifiable absolute ottrue zero point.

Examples: length, volume, time

Basic Concepts

• Descriptive statistics – statistics primarily aimed at describing or summarizing data into meaningful framework.

• Examples: measures of central tendency (mean, median, mode), measures of dispersion (standard deviation, variance), graphical representations of data or distribution (histograms, graphs, scatter plot etc.)

Basic Concepts

• Exploratory Data Analysis (EDA) or Exploratory Statistics – (developed by John Tukey) necessity of paying close attention to data and examining them in detail before invoking more technically involved procedures.

• Inferential statistics – inferring hypotheses or educated guesses from the sample to the population with the use of statistical procedures.

Basic Concepts

Cornerstones of Research

• Validity – measures what is supposed to be measured in research.

• Reliability – replicability of findings in research.

• External validity – refers to whether or not experimental or research results can be generalized to a real-world situation.

Basic Concepts

• Internal validity – manipulation of variables in research has led to an observed/desired difference.

• Hypothesis – a formal way of expressing a question as a prediction that can be tested.

• Null hypothesis – hypothesis that states that there will be no effect of the independent variable on the dependent variable.