Subjective measurement

Examples of Subjective measurement in the following topics:

• Repeated Measures Design

  • Repeated measures analysis of variance (rANOVA) is one of the most commonly used statistical approaches to repeated measures designs.
  • Repeated measures design (also known as "within-subjects design") uses the same subjects with every condition of the research, including the control.
  • Conduct an experiment when few participants are available: The repeated measures design reduces the variance of estimates of treatment-effects, allowing statistical inference to be made with fewer subjects.
  • There are also several threats to the internal validity of this design, namely a regression threat (when subjects are tested several times, their scores tend to regress towards the mean), a maturation threat (subjects may change during the course of the experiment) and a history threat (events outside the experiment that may change the response of subjects between the repeated measures).
  • Repeated measures analysis of variance (rANOVA) is one of the most commonly used statistical approaches to repeated measures designs.

• Between- and Within-Subjects Factors

  • When different subjects are used for the levels of a factor, the factor is called a between-subjects factoror a between-subjects variable.
  • The term "between subjects" reflects the fact that comparisons are between different groups of subjects.
  • Therefore there was only one group of subjects, and comparisons were not between different groups of subjects but between conditions within the same subjects.
  • When the same subjects are used for the levels of a factor, the factor is called a within-subjects factor or a within-subjects variable.
  • Within-subjects variables are sometimes referred to as repeated-measures variables since there are repeated measurements of the same subjects.

• Experimental Designs

  • A within-subjects design differs from a between-subjects design in that the same subjects perform at all levels of the independent variable.
  • Within-subjects designs are sometimes called repeated-measures designs.
  • Within-subjects designs are often called "repeated-measures" designs since repeated measurements are taken for each subject.
  • Similarly, a within-subject variable can be called a repeated-measures factor.
  • Designs can contain combinations of between-subject and within-subject variables.

• Impairment Measurement

  • Business assets that have suffered a loss in value are given two tests to measure and recognize the amount of the loss.
  • Business assets that have suffered a loss in value are subject to impairment testing to measure and recognize the amount of the loss.
  • If the cash flows are less than book value, the loss is measured.
  • The impairment of a building is measured by determining the amount of value the asset has lost.
  • Summarize the steps a company takes to measure an assets impairment

• Within-Subjects ANOVA

  • Explain why a within-subjects design can be expected to have more power than a between-subjects design
  • Within-subjects factors involve comparisons of the same subjects under different conditions.
  • For example, in the "ADHD Treatment" study, each child's performance was measured four times, once after being on each of four drug doses for a week.
  • Therefore, each subject's performance was measured at each of the four levels of the factor "Dose. " Note the difference from between-subjects factors for which each subject's performance is measured only once and the comparisons are among different groups of subjects.
  • A within-subjects factor is sometimes referred to as a repeated-measures factor since repeated measurements are taken on each subject.

• Accuracy, Precision, and Error

  • Accuracy is how close a measurement is to the correct value for that measurement.
  • The precision of a measurement system is refers to how close the agreement is between repeated measurements (which are repeated under the same conditions).
  • All measurements are subject to error, which contributes to the uncertainty of the result.
  • All measurements would therefore be overestimated by 0.5 g.
  • Unless you account for this in your measurement, your measurement will contain some error.

• Collecting and Measuring Data

  • There are four main levels of measurement: nominal, ordinal, interval, and ratio.
  • Nominal measurements have no meaningful rank order among values.
  • Nominal data differentiates between items or subjects based only on qualitative classifications they belong to.
  • Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit).
  • Measurement processes that generate statistical data are also subject to error.

• Misleading Research Subjects

  • If a researcher deceives or conceals the purpose or procedure of a study, they are misleading their research subjects.
  • Asch put a subject in a room with other participants who appeared to be normal subjects but who were actually part of the experiment.
  • Some sociology studies involve intentionally deceiving subjects about the nature of the research.
  • A more common case is a study in which researchers are concerned that if the subjects are aware of what is being measured, such as their reaction to a series of violent images, the results will be altered or tempered by that knowledge.
  • This approach respects the autonomy of individuals because subjects consent to the deception.

• Specific Comparisons (Correlated Observations)

  • Table 2 shows the data from five subjects.
  • Table 3 shows L1 for the first five subjects.
  • L1 for Subject 1 was computed by
  • There were 32 subjects in the experiment.
  • Since there were 32 subjects, the degrees of freedom is 32 - 1 = 31.

• Averages of Qualitative and Ranked Data

  • In statistics, levels of measurement, or scales of measure, are types of data that arise in the theory of scale types developed by the psychologist Stanley Smith Stevens.
  • The nominal scale differentiates between items or subjects based only on their names and/or categories and other qualitative classifications they belong to.
  • The mode, i.e. the most common item, is allowed as the measure of central tendency for the nominal type.
  • The median, i.e. middle-ranked, item is allowed as the measure of central tendency; however, the mean (or average) as the measure of central tendency is not allowed.
  • Categorize levels of measurement and identify the appropriate measures of central tendency.