The use of statistics in outcomes assessment
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Statistics is the science of collecting, organizing, and interpreting data. It involves summarizing data using numbers or graphs, identifying patterns in data, and making predictions or inferences about a group based on the collected data. Statistics is useful for research inquiries that investigate relationships between variables and the effects of changes to variables. The key aspects of a statistical analysis involve planning the study, collecting and analyzing the data, and presenting the findings. Different levels of data measurement include nominal, ordinal, interval, and ratio scales. Statistical techniques can describe relationships between single variables or multiple variables over time.
The use of statistics in outcomes assessment
- 2. Definition of Statistics Statistics is a science of discipline. It is a branch of mathematics that deals with the collection, organization, presentation, computation and interpretation of data which are the, outcomes of learning” (Santos et al, 2000)
- 3. uses methods to summarize a collection of data by describing what was observed using numbers or graphs. also called predictive statistics, uses methods to draw patterns in the collected data, and then makes conclusions, predictions or forecasts about a group or about a process being studied.
- 4. Statistics is useful in the teaching-learning process, along several research-based inquiries: These inquiries investigates causes, in addition to drawing conclusions on the effect of changes in elements (called variables) being studied. Data are gathered and the correlations between intervention (predictors) and the result derived from a single group is investigated.
- 5. A) Planning the research-based inquiry around size, hypothesis, variability, subjects, etc; B) designing the experiment by blocking to reduce error; random assignment for unbiased estimates, and mapping the procedures; C) implementation and analyzing data; D) documentation and presentation of
- 7. It is a process where instructor communicate with concepts about external realities. There are three classes of phenomena which can be statistically measured: (a)Direct observables (b) Indirect observables (c) Constructs or creations we form in the mind resulting from observations.
- 8. • Indicator is a sign of the presence of a concept (variable) under study. • Dimension is a specific aspect of a concept combined into groups or sub- groups, such as compassion toward neighbors/fellow nationals/foreigners/animals/plants.
- 9. Concepts can become more clarified by a process of clarification called specification. This can also be done by categorization or the ordering or ranking of data.
- 10. Conceptualization (process to specify what we mean) Nominal definition (assigned to term, not the real entity) Operational definition (specifies how a concept is measured) Real definition (better clarified status of a real thing)
- 11. • Variables are a logical set of attributes, e.g. gender. On the other hand, an attribute is a quality or characteristics of something, e.g.. male, female. Attributes may represent any of the four levels of measurement:
- 12. A level of measurement describing a variable that has attributes which are different, e.g. gender, birthplace, college, major, etc. A level of measurement describing a variable with attributes that can be in a rank-order along some dimension.
- 13. A level of measurement describing a variable whose attributes are rank-ordered and have equal distances between adjacent attributes. A level of measurement describing a variable with attributes that have all the qualities of nominal, ordinal and interval and based on a ”true zero” point, e.g. age, length of residence in a place, etc.
- 15. • The use of an index or scale may help to explain and elicit understanding of concepts in a range of conceptual variations. • In an index, scores for individual attributes are constructed. On the other hand in a scale, scores are assigned to patterns of ideas. • A scale is constructed by assigning scores to patterns of responses according to higher and lower degrees of civic participation.
- 16. – Is a classification of observations in terms of attributes on two or more variables. Use to subject data or concepts for better understanding, analysis, or statistical interpretation.
- 18. – This is an analysis of a single variable for purposes of description.
- 19. A bivariate relationship refers to two variables.
- 20. Given the results of findings, there are inferential statistics which can assist in pursuading the audience/readers as to the significance, strength, deserved interest in a completed research. These are called parametric tests of significance.
- 21. This is useful in social science and is based o the null hypothesis: the assumption that there is no relationship between two variable.
- 22. Expected Frequencies Men Women Total Attended alumni affair 28 42 70 Did not attend affair 12 18 30 Total 40 60 100 Observed frequencies Attended 20 50 70 Did not attend 20 10 30 Total 40 60 100 Observed minus Expected Divided by Expected Attended 2.29 1.52X = 12.70 Did not attend 5.33 3.56 = <.001
- 23. It is casual model for understanding relationship between many variables. It is a useful graphic illustration of relationships among several variables which assumes that the values of one variable are used by another.
- 25. This represents changes in one or more variables over time. This is used to discover patterns among the variations in values of several factors. Cases under study are combined into groups representing an independent variable, and the extent to which the group differ from one another is analyzed in relation to some
- 26. Other multivariate techniques are the Discriminant Analysis, Log Linear models and the Geographic Information systems which require more sophisticated statistical procedures which can be learned in Statistics or formal Research classes.