Interpretation and collection of data from the research process in Psychology

How experiments can be used to collect information in social research. Learn how surveys, such as interviews and questionnaires, can be used to collect data in social research. Study how content analysis is used to collect data in social research.

Results analysis

It is the linking of the results of the data analysis with the research hypothesis, with the theories and with existing and accepted knowledge.

Types issues that we could have with the Interpretations of certain specific data: attenuation of the measurement scale. As executions that systematically reach or can never reach, the limits of the measurement scale have to be interpreted. This problem can be solved by doing a pilot study, detecting these failures and expanding the scale in the new interpretation.

Roof effect. If we always touch the highest scores. Soil effect. If we always touch the lowest scores. Tailor regression. It is an unwanted phenomenon that appears in almost all investigations when a quantitative trial is requested. It is the tendency to issue answers close to the average or central values ​​when high -end evaluations are requested. It can lead us to erroneous conclusions.

The results They must be interpreted Regarding: the magnitude of the effect obtained and the trends or regularities observed. Compare these results with those obtained by other researchers in similar works. Clear conclusions of the work done.

Collection, data analysis

Data collection: through systematic observation, surveys and experiments. In natural media (field study) or artificial media (situations created by the researcher). Data analysis Factors to take into account when performing four tasks of data analysis: we must decide, although we suggest the double environment: descriptive statistics. If we stay in the sample. Inferential statistics. If we want to infer towards the population using probability. Measurement level of variables: interval or reason measurement level. Try to measure at the highest possible level, as these include the bass, but not vice versa. Problem that has been raised and the way in which the data has been collected. A balance between the possible and the convenient must always be made, so as not to be flooded with different analysis. It is advisable to perform a "analytical" systematic pluralism: systematicity implies that there must be a detailed plan with determined objectives both to collect and analyze data.

Pluralism (any way of investigating has its limitations. These can be minimized optimizing the analysis, for which it is necessary to ensure multiple and plural forms of analysis. This plurality includes non -empirical data and purely mathematical or theoretical developments. Tasks Data analysis: ways to summarize data. Have indexes that resume different aspects of distribution. Central tendency indices. Indicate the center of a distribution.

Calculate:

• The arithmetic mean: we add the scores and divide them by the number of them. Eg. (31+31+25+28+30)/5 = 29 Fashion: the most frequent observation is 31
• The median: ordering the scores, the central score is 30. Variability or dispersion indices. They indicate how disperses are the variable data.
• Variance or biased variance. Calculating the differential scores (subtracting the average of each score), with the square, adding them and dividing them between the number of them. Eg. S2S = / 5 = 5.2
• Unsisped variance. We divide the number of cases less one: eg. VI = / (5-1) = 6.5
• Typical unscreated deviation. Drawing the square root of the unscreated variance (VI) eg. Dt = Ö vi = Ö 6.5 = 2.55
• Typical biased deviation. Drawing the square root of the variance or biased variance (s2s) ex. Ss = Ö s2s = Ö 5.2 = 2.28 total amplitude of the distribution. If the minimum value of the maximum value is subtracted ex. AT = 31 - 25 = 6
• Asymmetry indices. ¿It is a symmetric scores distribution?. Subtracting average fashion and dividing this difference between the standard biased deviation. AS = (29 - 31) / 2.28 = -0.88 If it is less than zero, that is, negative (there are more high scores than you go down) if it is greater than zero, that is, positive (there are more low scores than high)

If it is zero it is symmetric (one part of the distribution is a reflection of the other) point indices. ¿It is a distribution of flattened scores? Looking for patterns (regularities or differences) in data. One of the best ways is the graphic representation. Forecasting results depending on data. Predictions exploiting their relationships. When a pattern is recognized the best way to summarize it is through a function. Although it does not go through all the points it offers us a simpler, although incomplete way, to describe the data in addition to the nature and intensity of the relationships between them.

Generalizing the population from the sample. Generalize the results above to more broad fields than those of the initial sample from which we start making inferences to the population with the help of descriptive data analysis by applying probability. We go through inferences to generalize to population results.

This article is merely informative, in psychology-online we have no power to make a diagnosis or recommend a treatment. We invite you to go to a psychologist to treat your particular case.

If you want to read more articles similar to Interpretation and collection of data from the research process in Psychology, We recommend that you enter our category of experimental psychology.