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Methods of Measuring Development in Humans


Methods of Measuring Development:


Development describes the growth of humans throughout the lifespan, from conception to death. The scientific study of human development seeks to understand and explain how and why people change throughout life. This includes all aspects of human growth, including physical, emotional, intellectual, social, perceptual and personality development.

Researchers have employed various methods to measure a person’s development. These methods are as follows:

Methods of Measuring Development:

Naturalistic observation:

Naturalistic observation involves methods designed to examine behavior without the experimenter interfering with it any way. Humans are studied in their natural habitat rather than in the laboratory.


  1. People tend to behave naturally
  2. Information that is gathered is rich and full.
  3. Can be used where other methods are not possible.


  1. Experimenter has no control over the situation.
  2. Participants can be aware of being watched and this can affect behavior.
  3. Problems of reliability due to bias or imprecise categorization of behavior.
  4. Replication is not usually possible. As the findings may be different in different environments.

Case studies:

A case study is an in-depth look at an individual. It provides insight into an individual’s fears hopes, fantasies, traumatic experiences, upbringing, family relationship, health or anything that helps a psychologist understand that person’s development.


  1. Case studies provides rich, in depth data
  2. Unusual instances, which could be overlooked in averaged data from experiments, are preserved.
  3. Rare case offer opportunities to study situations that could not ethically or practically be artificially contrived.


  1. Each case study is a unique investigation of a single situation or individual so the findings may not generalize to others.
  2. The evidence obtained from an individual that relates to the past may be hard to verify.


A questionnaire uses written questions and is structured (i.e., the order is fixed). The questions can be closed (a limited number of possible answers) or open (allowing) freedom to give a longer, detailed answer.


  1. Easy to send or email so time and cost-efficient.
  2. Respondent may be more truthful on paper/online than face-to-face an interview, especially if the questions are personal.


  1. Response biases such as always answering no or always ticking the left-hand box can reduce validity.
  2. Questionnaire return rates may be low and the sample may be biased.
  3. Limited because no flexibility for new questions to be added to allow collection of useful out unexpected data.


In comparison to questionnaire, interviews often occur face-to-face although they can take place over the telephone. They can be structured (with fixed questions) or unstructured, where the interviewer decides which questions to ask and follow a direction taken by the interviewer. They can also use a mixture of fixed and spontaneous question (semi-structured interview). Like questionnaire, they can use closed or open questions.


  1. Structured interview data is relatively early to analyses when quantitative as adding up numerical data is simply than interpreting descriptions.
  2. Semi- or on-structured interviews let the researcher collect information that might be missing in structured techniques.


  1. Structured interviews are limited by fixed questions.
  2. Investigator bias may reduce validity as expectation can alter the way questions are asked or the way the responses are interpreted.

Experimental Method:

In experimental method, the relationship between two or more factors is investigated by deliberately producing a change in on one factor (independent variable) and observing the effect that change has upon other factors (Dependent Variables). It is important to avoid extraneous variable which is a factor that could affect the DV and hide the effect of the IV. The experimental method is used in laboratory (artificial, controlled environment) and field (participant’s normal environment) experiments.

Advantages of Laboratory Experiments:

  1. Good control of extraneous variables. (cause and effect relationships)
  2. Causal relationships can be determined.
  3. Standard procedures allow replication improving reliability.

Limitations of Laboratory Experiments:-

  1. Artificial situations may make participants behavior unrepresentative.
  2. Researcher bias affects the result e.g. by behaving differently to some participants.
  3. Participants may respond to “demand characteristics” and alter their behavior.

Advantages of Field Experiments:

  1. Participants in their normal environment are likely to behave in a representative way.
  2. Participants are likely to be unaware that they are in a study so “demand characteristics” will be less problematic. Demand characteristics are clues that allow participant to guess the nature of study.

Limitations of Field Experiments:

  1. Controlling extraneous variable is more difficult than in a lab.
  2. Fewer controls so harder to replicate than lab experiments.

Correlational Method:

In the correlational method, the goal is to describe the strength of the relation between two or more events or characteristics.

Positive Correlation:-

A relation between two variables when an increase in one accompanies an increase in the other.

Negative Correlation:

A relation between two variables which decrease together.


  1. A correlation can demonstrate the presence or absence of a relationship. It there is a relationship, experimental research is worthwhile as a causal (cause and effect) relationship might be found. If there is no relationship, causal relationship is also unlikely.
  2. A correlational study can be conducted on variables which can be measured but not manipulated.


  1. A single correlational analysis cannot indicate whether a relationship is causal.
  2. Correlational analysis can only be used with variables that can be measured on a scale (i.e., ordinal, internal or ratio data levels).

Cross-sectional Research:

This research involves looking at different group of people of different ages at the same point in time. For example, a researcher might observe a group of young adults and compare this data with information gathered about a group of elderly participants. The data for young’s adults and elderly are gathered at the same moment in time.


  1. Inexpensive
  2. Takes relatively little time to complete.
  3. Avoids high dropout of participants from study.


  1. There is still a problem of individual difference because the researcher is comparing different people.
  2. Different age groups are not necessarily very much alike.
  3. Differences across age groups may be due to cohort differences rather than age.

Longitudinal Research:

It involves studying the same group of individuals over an extended period of time. Data is first collected at the outset of the study and may then be gathered repeatedly throughout the length of the study. In some cases, this research can last several decades.


  1. Allows for the study of developmental changes in great detail.
  2. Eliminate cohort differences.


  1. Expensive and time consuming.
  2. Potential for high dropout of participants. Even the researcher might move on, leaving someone else to continue the work.
  3. Differences over time may be due differences in assessment tools rather than age.
  4. So much will change over the period of time that it will be hard to know what is causing the effect you are looking at.

Cross-Sequential Research:

This research combines the cross-sectional and longitudinal research. The basic idea is to study simultaneously individuals of different ages (as in the cross-section) but to follow the individuals and retest them after some period of time has elapsed in the longitudinal approach.


  1. The longitudinal aspect of the design, in which the researcher compares the same cohort with itself over time, allows the researcher to rule out the possibility that the age-related differences is a cohort effect.
  2. The cross-sectional aspect of the design permits the researcher to rule out the possibility that the age related difference is due to recent environment events.


  1. Much costly and time consuming than cross-sectional research.
  2. Despite being the strongest design, may still leave questions about whether a development change is generalizable beyond the cohorts studied.

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