Designing the study to collect numerical data

Designing the study to collect numerical data

1. Research design is developed to answer the research questions posed

   • characteristics: teh work plan within the quatntitative approach is complete, structured, meticulous, detailed, shedule, and must even contain the information collecion instruments.
     • In the case of experimental type designs, their classification refers to three specific levels of experimental. non experimental designs, are colassified according to the moment in which the data required
     • casuaity analysis tends to be privileged; Types of variables: inependents varibale and dependent variable.
       • Non-experimental designs of a cross-sectional or transactional type are dived in these types:
         • Explanatory
         • Descriptive
         • Correlational
     • Trend designs (Train): are those that analyze changes over time, its destinctive feature is that the focus is on the population.
       • Group (cohort) evolutionary analysis design: changes over time
       • Panel design: the same participants are measured or observed.

2. Quantitive research designs

   • it required to examine the behavior or phenomenon or events, based on teh operation of intentional changes in the variables that compose them
     • Is a study in which one or more variables are intentionally manipulated
     • the experiemntation implies the manipulation of the idependent varible, the measurement or assessment of the effect that such manipulation has on the dependent variable.

3. Population, sampling and subjects

   • The researcher must define procisly 2 important aspects: the study population and the sample, research must be clear where and with whom you are going to work
   • Probability sampling by a probabilistic mechanism a relatively small number of statistical unit (sample), from a much larger set (population)
   • Sample size: it must be stimated following the criteria offered by statistc
   • Sample choice
     • Probabilistic sample: in each elementary statistical unit must have a known probablity,not zerom in the sample, non probabilistic:
       • The probability sample is that it allows the sampling error to be measured
       • probability sample technique: simple random sampling, systemaic sampling, stratified sampling, cluster sampling, 2 stage and multi stage sampling, sampling with probabilities

4. Data collection: on the attributes, concepts, or variables of the units of analysis or cases.

   • The sources from which the data will be obtained
   • Where are these sources located? regularly in the selected sample, through what means on method are we going tocollect data, choosing one or more means and defining the procedures, once collected, how it can be analyzed and respond to the problem.
   • Elements:
     • varibales, concepts, attribtes to be measured
     • The operaional definition: How we have operationalized the variables, to determine the method to measure .
     • The sample, available resources (time, institutional, financial, resources.

5. REQUIREMENTS OF DATA COLLECTION INSTRUMENT

   • Reliability
     • Refers to the degree to which its repeated application to the same individual or object procedures the same results.
   • Validity
     • Evidence related to the criterion: establishes the validity of a measurement instrument by comparing its results with those of some external criterion
     • Evidence related to content: refers to the degree to which an instrument reflects a domain of the specific content of what is measured.

6. Construct-related evidence

   • Refers to how successfully an instrument represents and measures a theoretical concepts.

7. objectivity

8. Describing the data with numbers

   • Describe statistics for each varibale: the first task is to describe the data, values, or scores obtained fro each variable.
     • What are the measures of central tendency: are points in a distribution obtained
     • MAIN: its the central endency mode, median, and mean
     • MODE: occurs most frequently
     • Median: its the observed value that lies in the middle of the distribution.

9. What is frequency distribution

   • Is a set f scores ordered in their respective categories and is generally presented as a table.
     • The valid percentages; excluding missingvalues, cumulative percentages (percentage of what is accumulating in each category, from the lowest, to highest.

10. How else can frequency distribution be presented

    • can be presented in the form of histograms or other graphs.

11. MEASURES OF VARIABILITY: THE RANGE also called the span, is the difference between the highest score and the lowest score. and indicates the number of units on the meausremnet scale. example: 17, 18, 20, 24,28,30,33; 33 - 17= 16

12. Bar diagrams: repersents statestical series in a cartesian axes.

    • Frequency polygons: are used preferentially in the representation of quantitative characteristics of particular interest, when indicating cumulative frequesncies.
    • Pie charts: use to represent percentage and proportions.